Patent Publication Number: US-11399198-B1

Title: Learned B-frame compression

Description:
TECHNICAL FIELD 
     The present disclosure generally relates to video coding and, more specifically, to learned compression of bidirectional predicted frames (B-frames). 
     BACKGROUND 
     Many devices and systems allow video data to be processed and output for consumption. Digital video data includes large amounts of data to meet increasing demands in video quality, performance, and features. For example, consumers of video data typically desire high quality videos, with high fidelity, resolutions, frame rates, and the like. The large amounts of video data often needed to meet these demands places a significant burden on communication networks and devices that process and store the video data. Video coding techniques may be used to compress video data. One example goal of video coding is to compress video data into a form that uses a lower bit rate, while avoiding or minimizing degradations in video quality. With ever-evolving video services becoming available and the increasing demands in large amounts of video data, coding techniques with better performance and efficiency are needed. 
     SUMMARY 
     Disclosed are systems, methods, computer-readable media and techniques for learned video compression of frames using frame interpolation predictions. According to at least one example, a method is provided for processing video data. The method can include receiving, from an encoder portion of a neural network system, a residual associated with a frame of a current processing time step; determining first motion information for a first reference frame associated with a first processing time step and second motion information for a second reference frame associated with a second processing time step, wherein the current processing time step is after the first processing time step and before the second processing time step; determining, using a decoder portion of the neural network system, third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; generating, using the decoder portion of the neural network system, a predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame; and generating, using the predicted frame and the residual, a reconstructed bidirectional predicted frame (B-frame) for the current processing time step, the reconstructed B-frame representing the frame. 
     According to at least one example, a non-transitory computer-readable medium is provided for processing video data. The non-transitory computer-readable medium can include instructions stored thereon which, when executed by one or more processors, cause the one or more processors to receive, from an encoder portion of a neural network system, a residual associated with a frame of a current processing time step; determine first motion information for a first reference frame associated with a first processing time step and second motion information for a second reference frame associated with a second processing time step, wherein the current processing time step is after the first processing time step and before the second processing time step; determine, using a decoder portion of the neural network system, third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; generate, using the decoder portion of the neural network system, a predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame; and generate, using the predicted frame and the residual, a reconstructed bidirectional predicted frame (B-frame) for the current processing time step, the reconstructed B-frame representing the frame. 
     According to at least one example, an apparatus is provided for processing video data. The apparatus can include memory and one or more processors configured to receive, from an encoder portion of a neural network system, a residual associated with a frame of a current processing time step; determine first motion information for a first reference frame associated with a first processing time step and second motion information for a second reference frame associated with a second processing time step, wherein the current processing time step is after the first processing time step and before the second processing time step; determine, using a decoder portion of the neural network system, third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; generate, using the decoder portion of the neural network system, a predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame; and generate, using the predicted frame and the residual, a reconstructed bidirectional predicted frame (B-frame) for the current processing time step, the reconstructed B-frame representing the frame. 
     According to another example, an apparatus for processing video data can include means for receiving, from an encoder portion of a neural network system, a residual associated with a frame of a current processing time step; determining first motion information for a first reference frame associated with a first processing time step and second motion information for a second reference frame associated with a second processing time step, wherein the current processing time step is after the first processing time step and before the second processing time step; determining, using a decoder portion of the neural network system, third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; generating, using the decoder portion of the neural network system, a predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame; and generating, using the predicted frame and the residual, a reconstructed bidirectional predicted frame (B-frame) for the current processing time step, the reconstructed B-frame representing the frame. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include training, for one or more training iterations, the neural network system using a loss function, the loss function determining a loss between one or more reconstructed B-frames and one or more frames represented by the one or more reconstructed B-frames. 
     In some examples, the first motion information can include a first two-dimensional (2D) optical flow map calculated from the current processing time step to the first reference frame. In some examples, the second motion information can include a second 2D optical flow map calculated from the current processing time step to the second reference frame. In some examples, the third motion information can include a three-dimensional (3D) optical flow map generated based on the first 2D optical flow map and the second 2D optical flow map. 
     In some cases, the first 2D optical flow map and the second 2D optical flow map can include spatial dimensions indicating vertical and horizontal displacements, and the 3D optical flow map can include spatial dimensions indicating estimated vertical and horizontal displacements and a frame dimension mapping each of the estimated vertical and horizontal displacements in the spatial dimensions to a corresponding one of the first reference frame and the second reference frame. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include determining, using the decoder portion of the neural network system, forward motion information from the first reference frame to the second reference frame, and backward motion information from the second reference frame to the first reference frame; determining, using the decoder portion of the neural network system, the first 2D optical flow map based on the forward motion information and a value of an interval of the first processing time step and the second processing time step, the value of the interval corresponding to the current processing time step; and determining, using the decoder portion of the neural network system, the second 2D optical flow map based on the backward motion information and the value of the interval. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include warping, using the 3D optical flow map, the first reference frame and the second reference frame to the current processing time step. In some examples, the predicted frame is generated based on the 3D optical flow map, the warped first reference frame and the warped second reference frame. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include adjusting one or more motion values in at least one of the first 2D optical flow map and the second 2D optical flow map based on a detected non-linearity in the one or more motion values; and after adjusting the one or more values in at least one of the first 2D optical flow map and the second 2D optical flow map, merging the first 2D optical flow map and the second 2D optical flow map into the 3D optical flow map. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include receiving, from the encoder portion of the neural network system, an encoded bitstream including the residual and contextual information extracted from image data associated with at least one of the frame, the first reference frame and the second reference frame; decoding, using the decoder portion of the neural network system, the encoded bitstream; and adjusting the one or more values in at least one of the first 2D optical flow map and the second 2D optical flow map. In some examples, the one or more values can be adjusted further based on the contextual information. In some examples, the contextual information can include an occlusion in the image data, one or more edges in the image data, and/or one or more features in the image data. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include warping, using the first 2D optical flow map, the first reference frame to the current processing time step; and warping, using the second 2D optical flow map, the second reference frame to the current processing time step. In some examples, the 3D optical flow map is generated based on the first reference frame, the second reference frame, the warped first reference frame, the warped second reference frame, the first 2D optical flow map, and/or the second optical flow map. 
     In some cases, the first reference frame and the second reference frame can include an intra-coded frame (I-frame) and/or an additional B-frame. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include receiving, from the encoder portion of the neural network system, an encoded bitstream including the residual; and decoding, using the decoder portion of the neural network system, the encoded bitstream. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include generating, using the decoder portion of the neural network system, the predicted frame and the reconstructed B-frame without the frame and without motion information from the encoder portion of the neural network system. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include determining, using the encoder portion of the neural network system, the first motion information for the first reference frame and the second motion information for the second reference frame; determining, using the encoder portion of the neural network system, the third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; and generating, using the encoder portion of the neural network system, the predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include generating, using the encoder portion of the neural network system, the residual based on the predicted frame and the frame; generating, using the encoder portion of the neural network system, an encoded bitstream including the residual; and transmitting, using the encoder portion of the neural network system, the encoded bitstream to the decoder portion of the neural network system. 
     In some aspects, the method, non-transitory computer-readable medium, and apparatuses described above can include determining, using the encoder portion of the neural network system, contextual information extracted from image data associated with at least one of the frame, the first reference frame and the second reference frame. In some examples, the encoded bitstream further comprises the contextual information. 
     In some aspects, the apparatus can include a mobile device, a wearable device, an extended reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a personal computer, a laptop computer, a server computer, a television, a video game console, or other device. In some aspects, the apparatus comprises a mobile device (e.g., a mobile telephone or so-called “smart phone”). In some aspects, the apparatus further includes at least one camera for capturing one or more images or video frames. For example, the apparatus can include a camera (e.g., an RGB camera) or multiple cameras for capturing one or more images and/or one or more videos including video frames. In some aspects, the apparatus includes a display for displaying one or more images, videos, notifications, or other displayable data. In some aspects, the apparatus includes a transmitter configured to transmit the reconstructed video frame over a transmission medium to at least one device. In some aspects, the processor includes a neural processing unit (NPU), a central processing unit (CPU), a graphics processing unit (GPU), or other processing device or component. 
     This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this patent, any or all drawings, and each claim. 
     The foregoing, together with other features and embodiments, will become more apparent upon referring to the following specification, claims, and accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Illustrative embodiments of the present application are described in detail below with reference to the following figures: 
         FIG. 1  illustrates an example image processing system which can implement the various techniques described herein, in accordance with some examples of the disclosure; 
         FIG. 2A  illustrates an example of a fully connected neural network, in accordance with some examples of the disclosure; 
         FIG. 2B  illustrates an example of a locally connected neural network, in accordance with some examples of the disclosure; 
         FIG. 2C  illustrates an example of a convolutional neural network, in accordance with some examples of the disclosure; 
         FIG. 2D  illustrates a detailed example of a deep convolutional network (DCN) designed to recognize features from an image, in accordance with some examples of the disclosure; 
         FIG. 3  is a block diagram illustrating another example DCN, in accordance with some examples of the disclosure; 
         FIG. 4A  is a diagram illustrating an example system configured to perform learned video coding using blind interpolation, in accordance with some examples of the disclosure; 
         FIG. 4B  is a diagram illustrating an example system configured to perform learned video coding with contextual information, in accordance with some examples of the disclosure; 
         FIG. 5  is a diagram illustrating an example system flow of an example interpolation network, in accordance with some examples of the disclosure; 
         FIG. 6  is a diagram illustrating examples of motion estimation techniques, in accordance with some examples of the disclosure; 
         FIG. 7A  is a diagram illustrating an example sequential scheme for bidirectional frame prediction, in accordance with some examples of the disclosure; 
         FIG. 7B  is a diagram illustrating an example hierarchical scheme for bidirectional frame prediction, in accordance with some examples of the disclosure; 
         FIG. 8  is a flowchart illustrating an example process for learned coding of frames using blind interpolation, in accordance with some examples of the disclosure; and 
         FIG. 9  illustrates an example computing device architecture of an example computing device which can implement the various techniques described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Certain aspects and embodiments of this disclosure are provided below. Some of these aspects and embodiments may be applied independently and some of them may be applied in combination as would be apparent to those of skill in the art. In the following description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of embodiments of the application. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be restrictive. 
     The ensuing description provides example embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the example embodiments will provide those skilled in the art with an enabling description for implementing an example embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the application as set forth in the appended claims. 
     Digital video data can include large amounts of data, particularly as the demand for high quality video data continues to grow. For example, consumers of video data typically desire video of increasingly high quality, with high fidelity, resolution, frame rates, and the like. However, the large amount of video data often needed to meet such demands can place a significant burden on communication networks as well devices that process and store the video data. 
     Various techniques can be used to code video data. Video coding can be performed according to a particular video coding standard and/or scheme. Example video coding standards include high-efficiency video coding (HEVC), advanced video coding (AVC), moving picture experts group (MPEG) coding, and versatile video coding (VVC). Video coding can use prediction methods such as inter-prediction or intra-prediction, which take advantage of redundancies present in video images or sequences. One goal of video coding techniques is to compress video data into a form that uses a lower bit rate, while avoiding or minimizing degradations in the video quality. As the demand for video services grows and new video services become available, coding techniques with better efficiency and performance are needed. 
     Disclosed are systems, methods, computer-readable media and techniques for learned video compression of frames using frame interpolation predictions, such as optical flow predictions. The techniques described herein can enable efficient, high quality video coding with lower resource demands, such as lower bandwidth demands. In some examples, the technologies described herein can efficiently and intelligently predict bidirectional predicted frames (B-frames) using motion information, such as optical flow, interpolated from reference frames. A B-frame can include a type of frame in compressed video data that is generated by referencing data from previous and future frames (e.g., frames preceding and following the B-frame) to determine changes between the previous and future frames. The B-frame can contain the data that changed from the previous frame and/or that differs from the future frame. 
     In some examples, the techniques described herein can reduce the amount of video data from a video encoder that a video decoder needs and/or uses to reconstruct video frames. For example, a video encoder can provide a residual frame to a video decoder. The video decoder can use reference frames to estimate an optical flow for a desired frame, and use the estimated optical flow and the residual frame to reconstruct the desired frame. The video decoder can reconstruct the desired frame without optical flow information from the video encoder, thus reducing the amount of data that the video encoder transmits to the video decoder and providing resource savings such as bandwidth. To estimate the optical flow, the video decoder can perform blind frame interpolation and/or context-aided frame interpolation, as further described herein. 
     The video encoder and the video decoder can use machine learning to process the video data. For example, the video encoder can use machine learning to generate a residual frame for the video decoder, and the video decoder can use machine learning to interpolate the optical flow, generate a predicted frame, and reconstruct the desired frame using the predicted frame and the residual frame. Machine learning (ML) is a subset of artificial intelligence (AI). ML systems include algorithms and statistical models that computer systems can use to perform various tasks by relying on patterns and inference. One example of a ML system is a neural network (also referred to as an artificial neural network), which can include an interconnected group of artificial neurons (e.g., neuron models). Neural networks may be used for various applications and/or devices, such as image analysis and/or computer vision applications, Internet Protocol (IP) cameras, Internet of Things (IoT) devices, autonomous vehicles, service robots, among others. 
     Individual nodes in the neural network may emulate biological neurons by taking input data and performing operations on the data. The results of the operations performed on the input data are selectively passed to other neurons. Weight values are associated with each vector and node in the network, and these values constrain how input data is related to output data. For example, the input data of each node may be multiplied by a corresponding weight value, and the products may be summed. The sum of the products may be adjusted by an optional bias, and an activation function may be applied to the result, yielding the node&#39;s output signal or “output activation” (sometimes referred to as an activation map or feature map). The weight values may initially be determined by an iterative flow of training data through the network (e.g., weight values are established during a training phase in which the network learns how to identify particular classes by their typical input data characteristics). 
     Different types of neural networks exist, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), multilayer perceptron (MLP) neural networks, among others. Convolutional neural networks may include collections of artificial neurons that each have a receptive field (e.g., a spatially localized region of an input space) and that collectively tile an input space. Convolutional neural networks have numerous applications. For example, CNNs can be broadly used in the area of pattern recognition and classification. RNNs work on the principle of saving the output of a layer and feeding this output back to the input to help in predicting an outcome of the layer. In MLP neural networks, data may be fed into an input layer, and one or more hidden layers provide levels of abstraction to the data. Predictions may then be made on an output layer based on the abstracted data. MLPs may be particularly suitable for classification prediction problems where inputs are assigned a class or label. 
     In layered neural network architectures (referred to as deep neural networks when multiple hidden layers are present), the output of a first layer of artificial neurons becomes an input to a second layer of artificial neurons, the output of a second layer of artificial neurons becomes an input to a third layer of artificial neurons, and so on. Convolutional neural networks may be trained to recognize a hierarchy of features. Computation in convolutional neural network architectures may be distributed over a population of processing nodes, which may be configured in one or more computational chains. These multi-layered architectures may be trained one layer at a time and may be fine-tuned using back propagation. 
       FIG. 1  illustrates an example implementation of an image processing system  100 , in accordance with some examples. The image processing system can include a central processing unit (CPU)  102  or a multi-core CPU, configured to perform one or more of the functions described herein. Parameters or variables (e.g., neural signals and synaptic weights), system parameters associated with a computational device (e.g., neural network with weights), delays, frequency bin information, task information, image data, among other information may be stored in a memory block associated with a neural processing unit (NPU)  108 , in a memory block associated with a CPU  102 , in a memory block associated with a graphics processing unit (GPU)  104 , in a memory block associated with a digital signal processor (DSP)  106 , in a memory block  118 , and/or may be distributed across multiple blocks. Instructions executed at the CPU  102  may be loaded from a program memory associated with the CPU  102  and/or from a memory block  118 . 
     The image processing system  100  can also include additional processing blocks for performing specific functions, such as a GPU  104 ; a DSP  106 ; a connectivity block  110 , which may include fifth generation (5G) connectivity, fourth generation long term evolution (4G LTE) connectivity, Wi-Fi connectivity, USB connectivity, Bluetooth connectivity, and the like; and/or a multimedia processor  112  that may, for example, detect image features. In some examples, the NPU  108  can be implemented in the CPU  102 , DSP  106 , and/or GPU  104 . In some cases, the image processing system  100  may also include one or more sensor  114 , one or more image signal processors (ISPs)  116 , and/or storage  120 . 
     In some examples, the image processing system  100  can implement an ARM instruction set architecture for one or more processors. In an aspect of the present disclosure, the instructions loaded into the CPU  102  may include code to search for a stored multiplication result in a lookup table (LUT) corresponding to a multiplication product of an input value and a filter weight. The instructions loaded into the CPU  102  may also include code to disable a multiplier during a multiplication operation of the multiplication product when a lookup table hit of the multiplication product is detected. In addition, the instructions loaded into the CPU  102  may include code to store a computed multiplication product of the input value and the filter weight when a lookup table miss of the multiplication product is detected. 
     The image processing system  100  can be part of a computing device or multiple computing devices. In some examples, the image processing system  100  can be part of an electronic device (or devices) such as a camera system (e.g., a digital camera, an IP camera, a video camera, a security camera, etc.), a telephone system (e.g., a smartphone, a cellular telephone, a conferencing system, etc.), a desktop computer, an XR device (e.g., a head-mounted display, etc.), a smart wearable device (e.g., a smart watch, smart glasses, etc.), a laptop or notebook computer, a tablet computer, a set-top box, a television, a display device, a system-on-chip (SoC), a digital media player, a gaming console, a video streaming device, a server, a drone, a computer in a car, an Internet-of-Things (IoT) device, or any other suitable electronic device(s). 
     In some implementations, the CPU  102 , the GPU  104 , the DSP  106 , the NPU  108 , the connectivity block  110 , the multimedia processor  112 , the one or more sensors  114 , the ISPs  116 , the memory block  118  and/or the storage  120  can be part of the same computing device. For example, in some cases, the CPU  102 , the GPU  104 , the DSP  106 , the NPU  108 , the connectivity block  110 , the multimedia processor  112 , the one or more sensors  114 , the ISPs  116 , the memory block  118  and/or the storage  120  can be integrated into a smartphone, laptop, tablet computer, smart wearable device, video gaming system, server, and/or any other computing device. In other implementations, the CPU  102 , the GPU  104 , the DSP  106 , the NPU  108 , the connectivity block  110 , the multimedia processor  112 , the one or more sensors  114 , the ISPs  116 , the memory block  118  and/or the storage  120  can be part of two or more separate computing devices. 
     The image processing system  100  and/or components thereof may be configured to perform video compression and/or decompression (also referred to as video encoding and/or decoding, collectively referred to as video coding) using techniques according to aspects of the present disclosure discussed herein. By using deep learning architectures and the techniques described herein to perform video compression and/or decompression, aspects of the present disclosure can increase the efficiency of video compression and/or decompression on a device and/or reduce associated resource requirements and/or usage. For example, a device using the video coding techniques described herein can compress video data more efficiently, can reduce the amount of data transmitted in compressed video data to a destination device, and the destination device can receive and decompress the compressed video data efficiently. In some examples, the deep learning architectures and techniques described herein can reduce the amount of data exchanged between coding devices or components, such as encoders and decoders, to code video content. The reduced amount of data transmitted for video coding can reduce latencies, increase performance, and reduce the cost or burden on computing resources such as, for example, bandwidth, memory, storage, power, compute, hardware, etc. 
     As noted above, a neural network is an example of a machine learning system, and can include an input layer, one or more hidden layers, and an output layer. Data is provided from input nodes of the input layer, processing is performed by hidden nodes of the one or more hidden layers, and an output is produced through output nodes of the output layer. Deep learning networks typically include multiple hidden layers. Each layer of the neural network can include feature maps or activation maps that can include artificial neurons (or nodes). A feature map can include a filter, a kernel, or the like. The nodes can include one or more weights used to indicate an importance of the nodes of one or more of the layers. In some cases, a deep learning network can have a series of many hidden layers, with early layers being used to determine simple and low level characteristics of an input, and later layers building up a hierarchy of more complex and abstract characteristics. 
     A deep learning architecture may learn a hierarchy of features. If presented with visual data, for example, the first layer may learn to recognize features, such as edges, in the input stream. In another example, if presented with auditory data, the first layer may learn to recognize spectral power in specific frequencies. The second layer, taking the output of the first layer as input, may learn to recognize features, such as shapes for visual data or combinations of sounds for auditory data. For instance, higher layers may learn to represent complex shapes in visual data or words in auditory data. Still higher layers may learn to recognize common visual objects and/or spoken phrases. 
     Neural networks may be designed with a variety of connectivity patterns. In feed-forward networks, information is passed from lower to higher layers, with each neuron in a given layer communicating to neurons in higher layers. A hierarchical representation may be built up in successive layers of a feed-forward network, as described above. Neural networks may also have recurrent or feedback (also called top-down) connections. In a recurrent connection, the output from a neuron in a given layer may be communicated to another neuron in the same layer. A recurrent architecture may be helpful in recognizing patterns that span more than one of the input data chunks that are delivered to the neural network in a sequence. A connection from a neuron in a given layer to a neuron in a lower layer is called a feedback (or top-down) connection. A network with many feedback connections may be helpful when the recognition of a high-level concept may aid in discriminating the particular low-level features of an input. 
     The connections between layers of a neural network may be fully connected or locally connected.  FIG. 2A  illustrates an example of a fully connected neural network  202 . In a fully connected neural network  202 , a neuron in a first layer may communicate its output to every neuron in a second layer, so that each neuron in the second layer will receive input from every neuron in the first layer.  FIG. 2B  illustrates an example of a locally connected neural network  204 . In a locally connected neural network  204 , a neuron in a first layer may be connected to a limited number of neurons in the second layer. More generally, a locally connected layer of the locally connected neural network  204  may be configured so that each neuron in a layer will have the same or a similar connectivity pattern, but with connections strengths that may have different values (e.g.,  210 ,  212 ,  214 , and  216 ). The locally connected connectivity pattern may give rise to spatially distinct receptive fields in a higher layer, because the higher layer neurons in a given region may receive inputs that are tuned through training to the properties of a restricted portion of the total input to the network. 
     One example of a locally connected neural network is a convolutional neural network.  FIG. 2C  illustrates an example of a convolutional neural network  206 . The convolutional neural network  206  may be configured such that the connection strengths associated with the inputs for each neuron in the second layer are shared (e.g.,  208 ). Convolutional neural networks may be well suited to problems in which the spatial location of inputs is meaningful. Convolutional neural network  206  may be used to perform one or more aspects of video compression and/or decompression, according to aspects of the present disclosure. 
     One type of convolutional neural network is a deep convolutional network (DCN).  FIG. 2D  illustrates an example of a DCN  200  designed to recognize features from an image  226  input from an image capturing device  230 , such as a camera or image sensor. In some examples, the DCN  200  of the current example may be trained to identify visual features in the image  226 , such as one or more objects or signs in the image  226 , for example. 
     In some examples, the DCN  200  may be trained with supervised learning. During training, the DCN  200  may be presented with an image, such as the image  226 , and a forward pass may then be computed to produce an output  222 . The DCN  200  may include a feature extraction section and a classification section. Upon receiving the image  226 , a convolutional layer  232  may apply convolutional kernels (not shown) to the image  226  to generate a first set of feature maps  218 . As an example, the convolutional kernel for the convolutional layer  232  may be a 5×5 kernel that generates 28×28 feature maps. In the present example, because four different feature maps are generated in the first set of feature maps  218 , four different convolutional kernels were applied to the image  226  at the convolutional layer  232 . The convolutional kernels may also be referred to as filters or convolutional filters. 
     The first set of feature maps  218  may be subsampled by a max pooling layer (not shown) to generate a second set of feature maps  220 . The max pooling layer reduces the size of the first set of feature maps  218 . That is, a size of the second set of feature maps  220 , such as 14×14, is less than the size of the first set of feature maps  218 , such as 28×28. The reduced size provides similar information to a subsequent layer while reducing memory consumption. The second set of feature maps  220  may be further convolved via one or more subsequent convolutional layers (not shown) to generate one or more subsequent sets of feature maps (not shown). 
     In the example of  FIG. 2D , the second set of feature maps  220  is convolved to generate a first feature vector  224 . Furthermore, the first feature vector  224  is further convolved to generate a second feature vector  228 . Each feature of the second feature vector  228  may include a number that corresponds to a possible feature of the image  226 , such as “sign”, “60”, and “100”. A softmax function (not shown) may convert the numbers in the second feature vector  228  to a probability. As such, an output  222  of the DCN  200  is a probability of the image  226  including one or more features. 
     In the present example, the probabilities in the output  222  for “sign” and “60” are higher than the probabilities of the others of the output  222 , such as “30”, “40”, “50”, “70”, “80”, “90”, and “100”. Before training, the output  222  produced by the DCN  200  is likely to be incorrect. Thus, an error may be calculated between the output  222  and a target output. The target output is the ground truth of the image  226  (e.g., “sign” and “60”). The weights of the DCN  200  may then be adjusted so the output  222  of the DCN  200  is more closely aligned with the target output. 
     To adjust the weights, a learning algorithm may compute a gradient vector for the weights. The gradient may indicate an amount that an error would increase or decrease if the weight were adjusted. At the top layer, the gradient may correspond directly to the value of a weight connecting an activated neuron in the penultimate layer and a neuron in the output layer. In lower layers, the gradient may depend on the value of the weights and on the computed error gradients of the higher layers. The weights may then be adjusted to reduce the error. This manner of adjusting the weights may be referred to as “back propagation” as it involves a “backward pass” through the neural network. 
     In practice, the error gradient of weights may be calculated over a small number of examples, so that the calculated gradient approximates the true error gradient. This approximation method may be referred to as stochastic gradient descent. Stochastic gradient descent may be repeated until the achievable error rate of the entire system has stopped decreasing or until the error rate has reached a target level. After learning, the DCN may be presented with new images and a forward pass through the network may yield an output  222  that may be considered an inference or a prediction of the DCN. 
     Deep belief networks (DBNs) are probabilistic models comprising multiple layers of hidden nodes. DBNs may be used to extract a hierarchical representation of training data sets. A DBN may be obtained by stacking up layers of Restricted Boltzmann Machines (RBMs). An RBM is a type of artificial neural network that can learn a probability distribution over a set of inputs. Because RBMs can learn a probability distribution in the absence of information about the class to which each input should be categorized, RBMs are often used in unsupervised learning. Using a hybrid unsupervised and supervised paradigm, the bottom RBMs of a DBN may be trained in an unsupervised manner and may serve as feature extractors, and the top RBM may be trained in a supervised manner (on a joint distribution of inputs from the previous layer and target classes) and may serve as a classifier. 
     Deep convolutional networks (DCNs) are networks of convolutional networks, configured with additional pooling and normalization layers. DCNs can achieve high performance on many tasks. DCNs can be trained using supervised learning in which both the input and output targets are known for many exemplars and are used to modify the weights of the network by use of gradient descent methods. 
     DCNs may be feed-forward networks. In addition, as described above, the connections from a neuron in a first layer of a DCN to a group of neurons in the next higher layer are shared across the neurons in the first layer. The feed-forward and shared connections of DCNs may be exploited for fast processing. The computational burden of a DCN may be much less than, for example, that of a similarly sized neural network that comprises recurrent or feedback connections. 
     The processing of each layer of a convolutional network may be considered a spatially invariant template or basis projection. If the input is first decomposed into multiple channels, such as the red, green, and blue channels of a color image, then the convolutional network trained on that input may be considered three-dimensional, with two spatial dimensions along the axes of the image and a third dimension capturing color information. The outputs of the convolutional connections may be considered to form a feature map in the subsequent layer, with each element of the feature map (e.g.,  220 ) receiving input from a range of neurons in the previous layer (e.g., feature maps  218 ) and from each of the multiple channels. The values in the feature map may be further processed with a non-linearity, such as a rectification, max(0,x). Values from adjacent neurons may be further pooled, which corresponds to down sampling, and may provide additional local invariance and dimensionality reduction. 
       FIG. 3  is a block diagram illustrating an example of a deep convolutional network  350 . The deep convolutional network  350  may include multiple different types of layers based on connectivity and weight sharing. As shown in  FIG. 3 , the deep convolutional network  350  includes the convolution blocks  354 A,  354 B. Each of the convolution blocks  354 A,  354 B may be configured with a convolution layer (CONV)  356 , a normalization layer (LNorm)  358 , and a max pooling layer (MAX POOL)  360 . 
     The convolution layers  356  may include one or more convolutional filters, which may be applied to the input data  352  to generate a feature map. Although only two convolution blocks  354 A,  354 B are shown, the present disclosure is not so limiting, and instead, any number of convolution blocks (e.g., blocks  354 A,  354 B) may be included in the deep convolutional network  350  according to design preferences. The normalization layer  358  may normalize the output of the convolution filters. For example, the normalization layer  358  may provide whitening or lateral inhibition. The max pooling layer  360  may provide down sampling aggregation over space for local invariance and dimensionality reduction. 
     The parallel filter banks, for example, of a deep convolutional network may be loaded on a CPU  102  or GPU  104  of an image processing system  100  to achieve high performance and low power consumption. In some examples, the parallel filter banks may be loaded on the DSP  106  or an ISP  116  of an image processing system  100 . The deep convolutional network  350  may access other processing blocks that may be present on the image processing system  100 . 
     The deep convolutional network  350  may include one or more fully connected layers, such as layer  362 A (labeled “FC1”) and layer  362 B (labeled “FC2”). The deep convolutional network  350  may include a logistic regression (LR) layer  364 . Between each layer  356 ,  358 ,  360 ,  362 ,  364  of the deep convolutional network  350  are weights (not shown) that are to be updated. The output of each of the layers (e.g.,  356 ,  358 ,  360 ,  362 ,  364 ) may serve as an input of a succeeding one of the layers (e.g.,  356 ,  358 ,  360 ,  362 ,  364 ) in the deep convolutional network  350  to learn hierarchical feature representations from input data  352  (e.g., images, audio, video, sensor data and/or other input data) supplied at the first of the convolution blocks  354 A. The output of the deep convolutional network  350  is a classification score  366  for the input data  352 . The classification score  366  may be a set of probabilities, where each probability is the probability of the input data including a feature from a set of features. 
     As noted above, digital video data can include large amounts of data, which can place a significant burden on communication networks and devices that process and store the video data. For instance, recording uncompressed video content generally results in large file sizes that greatly increase as the resolution of the recorded video content increases. In one illustrative example, uncompressed 16-bit per channel video recorded in 1080p/24 (e.g., a resolution of 1920 pixels in width and 1080 pixels in height, with 24 frames per second captured) may occupy 12.4 megabytes per frame, or 297.6 megabytes per second. Uncompressed 16-bit per channel video recorded in 4K resolution at 24 frames per second may occupy 49.8 megabytes per frame, or 1195.2 megabytes per second. 
     Network bandwidth is another constraint for which large video files can become problematic. For example, video content is oftentimes delivered over wireless networks (e.g., via LTE, LTE-Advanced, New Radio (NR), WiFi™, Bluetooth™, or other wireless networks), and can make up a large portion of consumer internet traffic. Thus, it is desirable to reduce the amount of bandwidth used to deliver video content in these networks. 
     Because uncompressed video content can result in large files that may involve sizable memory for physical storage and considerable bandwidth for transmission, video coding techniques can be utilized to compress and decompress such video content, as further described herein. To reduce the size of video content—and thus the amount of storage involved to store video content and the amount of bandwidth involved in delivering video content—various video coding techniques can be performed according to a particular video coding standard and/or scheme, such as HEVC, AVC, MPEG, VVC, among others. Video coding can use prediction methods such as inter-prediction or intra-prediction, which take advantage of redundancies present in video images or sequences. One goal of video coding techniques is to compress video data into a form that uses a lower bit rate, while avoiding or minimizing degradations in the video quality. As the demand for video services grows and new video services become available, coding techniques with better coding efficiency, performance, and rate control are needed. 
     An encoding device can encode video data according to a video coding standard to generate an encoded video bitstream. In some examples, an encoded video bitstream (or “video bitstream” or “bitstream”) can include a series of one or more coded video sequences. The encoding device can generate coded representations of pictures by partitioning each picture into multiple slices. A slice is independent of other slices so that information in the slice is coded without dependency on data from other slices within the same picture. A slice includes one or more slice segments including an independent slice segment and, if present, one or more dependent slice segments that depend on previous slice segments. In HEVC, the slices are partitioned into coding tree blocks (CTBs) of luma samples and chroma samples. A CTB of luma samples and one or more CTBs of chroma samples, along with syntax for the samples, are referred to as a coding tree unit (CTU). A CTU may also be referred to as a “tree block” or a “largest coding unit” (LCU). A CTU is the basic processing unit for HEVC encoding. A CTU can be split into multiple coding units (CUs) of varying sizes. A CU contains luma and chroma sample arrays that are referred to as coding blocks (CBs). 
     The luma and chroma CBs can be further split into prediction blocks (PBs). A PB is a block of samples of the luma component or a chroma component that uses the same motion parameters for inter-prediction or intra-block copy (IBC) prediction (when available or enabled for use). The luma PB and one or more chroma PBs, together with associated syntax, form a prediction unit (PU). For inter-prediction, a set of motion parameters (e.g., one or more motion vectors, reference indices, or the like) is signaled in the bitstream for each PU and is used for inter-prediction of the luma PB and the one or more chroma PBs. The motion parameters can also be referred to as motion information. A CB can also be partitioned into one or more transform blocks (TBs). A TB represents a square block of samples of a color component on which a residual transform (e.g., the same two-dimensional transform in some cases) is applied for coding a prediction residual signal. A transform unit (TU) represents the TBs of luma and chroma samples, and corresponding syntax elements. Transform coding is described in more detail below. 
     According to the HEVC standard, transformations may be performed using TUs. The TUs may be sized based on the size of PUs within a given CU. The TUs may be the same size or smaller than the PUs. In some examples, residual samples corresponding to a CU may be subdivided into smaller units using a quadtree structure known as residual quad tree (RQT). Leaf nodes of the RQT may correspond to TUs. Pixel difference values associated with the TUs may be transformed to produce transform coefficients. The transform coefficients may then be quantized by the encoding device. 
     Once the pictures of the video data are partitioned into CUs, the encoding device predicts each PU using a prediction mode. The prediction unit or prediction block is then subtracted from the original video data to get residuals (described below). For each CU, a prediction mode may be signaled inside the bitstream using syntax data. A prediction mode may include intra-prediction (or intra-picture prediction) or inter-prediction (or inter-picture prediction). Intra-prediction utilizes the correlation between spatially neighboring samples within a picture. For example, using intra-prediction, each PU is predicted from neighboring image data in the same picture using, for example, DC prediction to find an average value for the PU, planar prediction to fit a planar surface to the PU, direction prediction to extrapolate from neighboring data, or any other suitable types of prediction. Inter-prediction uses the temporal correlation between pictures in order to derive a motion-compensated prediction for a block of image samples. For example, using inter-prediction, each PU is predicted using motion compensation prediction from image data in one or more reference pictures (before or after the current picture in output order). The decision whether to code a picture area using inter-picture or intra-picture prediction may be made, for example, at the CU level. 
     In some examples, the one or more slices of a picture are assigned a slice type. Slice types can include an I slice, a P slice, and a B slice. An I slice (intra-frames, independently decodable) is a slice of a picture that is only coded by intra-prediction, and therefore is independently decodable since the I slice requires only the data within the frame to predict any prediction unit or prediction block of the slice. A P slice (uni-directional predicted frames) is a slice of a picture that may be coded with intra-prediction and with uni-directional inter-prediction. Each prediction unit or prediction block within a P slice is either coded with intra-prediction or inter-prediction. When inter-prediction applies, the prediction unit or prediction block is predicted by one reference picture, and therefore reference samples are from one reference region of one frame. A B slice (bi-directional predictive frames) is a slice of a picture that may be coded with intra-prediction and inter-prediction (e.g., either bi-prediction or uni-prediction). A prediction unit or prediction block of a B slice may be bi-directionally predicted from two reference pictures. Each picture can contribute a reference region and sample sets of the two reference regions can be weighted (e.g., with equal weights or with different weights) to produce the prediction signal of the bi-directional predicted block. As explained above, slices of one picture are independently coded. In some cases, a picture can be coded as just one slice. 
     After performing prediction using intra- and/or inter-prediction, the encoding device can perform transformation and quantization. For example, following prediction, the encoding device may calculate residual values corresponding to the PU. Residual values may include pixel difference values between the current block of pixels being coded (the PU) and the prediction block used to predict the current block (e.g., the predicted version of the current block). For example, after generating a prediction block (e.g., issuing inter-prediction or intra-prediction), the encoding device can generate a residual block by subtracting the prediction block produced by a prediction unit from the current block. The residual block includes a set of pixel difference values that quantify differences between pixel values of the current block and pixel values of the prediction block. In some examples, the residual block may be represented in a two-dimensional block format (e.g., a two-dimensional matrix or array of pixel values). In such examples, the residual block is a two-dimensional representation of the pixel values. 
     Any residual data that may be remaining after prediction is performed is transformed using a block transform, which may be based on discrete cosine transform, discrete sine transform, an integer transform, a wavelet transform, other suitable transform function, or any combination thereof. In some cases, one or more block transforms (e.g., sizes 32×32, 16×16, 8×8, 4×4, or other suitable size) may be applied to residual data in each CU. In some embodiments, a TU may be used for the transform and quantization processes implemented by the encoding device. A given CU having one or more PUs may also include one or more TUs. As described in further detail below, the residual values may be transformed into transform coefficients using the block transforms, and then may be quantized and scanned using TUs to produce serialized transform coefficients for entropy coding. 
     The encoding device may perform quantization of the transform coefficients. Quantization provides further compression by quantizing the transform coefficients to reduce the amount of data used to represent the coefficients. For example, quantization may reduce the bit depth associated with some or all of the coefficients. In one example, a coefficient with an n-bit value may be rounded down to an m-bit value during quantization, with n being greater than m. 
     Once quantization is performed, the coded video bitstream includes quantized transform coefficients, prediction information (e.g., prediction modes, motion vectors, block vectors, or the like), partitioning information, and any other suitable data, such as other syntax data. The different elements of the coded video bitstream may then be entropy encoded by the encoding device. In some examples, the encoding device may utilize a predefined scan order to scan the quantized transform coefficients to produce a serialized vector that can be entropy encoded. In some examples, encoding device may perform an adaptive scan. After scanning the quantized transform coefficients to form a vector (e.g., a one-dimensional vector), the encoding device may entropy encode the vector. For example, the encoding device may use context adaptive variable length coding, context adaptive binary arithmetic coding, syntax-based context-adaptive binary arithmetic coding, probability interval partitioning entropy coding, or another suitable entropy encoding technique. 
     The encoding device can store the encoded video bitstream and/or can send the encoded video bitstream data over a communications link to a receiving device, which can include a decoding device. The decoding device may decode the encoded video bitstream data by entropy decoding (e.g., using an entropy decoder) and extracting the elements of one or more coded video sequences making up the encoded video data. The decoding device may then rescale and perform an inverse transform on the encoded video bitstream data. Residual data is then passed to a prediction stage of the decoding device. The decoding device then predicts a block of pixels (e.g., a PU) using intra-prediction, inter-prediction, IBC, and/or other type of prediction. In some examples, the prediction is added to the output of the inverse transform (the residual data). The decoding device may output the decoded video to a video destination device, which may include a display or other output device for displaying the decoded video data to a consumer of the content. 
     Video coding systems and techniques defined by the various video coding standards (e.g., the HEVC video coding techniques described above) may be able to retain much of the information in raw video content and may be defined a priori based on signal processing and information theory concepts. However, while various compression algorithms may be applicable generally (e.g., to any type of video content), these video coding techniques may lack certain characteristics that are beneficial in a network-based environment or other type of environment. Methods (also referred to as processes), systems, apparatuses, and computer-readable media (collectively referred to as “systems and techniques”) are described herein that perform learned video compression of frames using frame interpolation predictions, such as optical flow predictions. The systems and techniques described herein can enable efficient, high quality video coding with lower resource demands, such as lower bandwidth demands. In some examples, the systems and techniques described herein can efficiently and intelligently predict bidirectional predicted frames (B-frames) using motion information, such as optical flow, interpolated from reference frames. 
     In some examples, the systems and techniques described herein can reduce the amount of video data from a video encoder that a video decoder needs and/or uses to reconstruct video frames. For example, a video encoder can provide a residual frame to a video decoder. The video decoder can use reference frames to estimate an optical flow for a desired frame, and use the estimated optical flow and the residual frame to reconstruct the desired frame. The video decoder can reconstruct the desired frame without optical flow information from the video encoder, thus reducing the amount of data that the video encoder transmits to the video decoder and providing resource savings such as bandwidth. To estimate the optical flow, the video decoder can perform blind frame interpolation and/or context-aided frame interpolation, as further described herein. 
     In some examples, the systems and techniques described herein can utilize multiple neural network systems to compress and decompress video frames or images. For instance, one or more neural network systems can act as an image encoder (e.g., an image autoencoder) to compress a frame using optical flow information interpolated from reference frames. One or more neural network systems can be used for encoding and/or decoding frames using optical flow information interpolated from reference frames. The optical flow information can be used to determine the data of a current frame by describing how the pixels from reference frames move between the reference frames. The optical flow information can be used along with residual information to reconstruct the current frame. 
     While optical flow interpolation is used in various examples described herein, it should be noted that optical flow interpolation is merely used as an illustrative frame interpolation example. As one of ordinary skill in the art will recognize from the disclosure, the systems and techniques described herein can implement any other frame interpolation technique. 
       FIG. 4A  is a diagram illustrating an example system  400  configured to perform learned video coding using blind interpolation. In this example, the system  400  can include an encoder  426  including an interpolation network  420 A, a decoder  428  including an interpolation network  420 B, and an autoencoder  430 . In some examples, the interpolation network  420 A can be a local copy at the encoder  426  of the interpolation network  420 B at the decoder  428 . In some examples, the autoencoder  430  can be part of the decoder  428 . In some examples, the autoencoder  430  can be part of the encoder  426 . In other examples, the autoencoder  430  can be separate from the encoder  426  and the decoder  428 . In some cases, the encoder  426 , the decoder  428 , and/or the autoencoder  430  can be implemented by a same computing device. In other cases, the encoder  426 , the decoder  428 , and/or the autoencoder  430  can be implemented by two or more separate computing devices. 
     In some examples, the encoder  426  can be part of or implemented by an encoding device, and the decoder  428  (and/or the autoencoder  430 ) can be part of or implemented by a destination device, such as a decoding device for example. The interpolation network  420 A and the interpolation network  420 B can each include one or more neural network systems, as further described below with respect to  FIG. 5 . For example, the interpolation network  420 A and the interpolation network  420 B can each include CNN networks configured to implement blind interpolation operations as described with respect to  FIG. 5 . The autoencoder  430  can include an encoder network  432  and a decoder network  434 . In some examples, the autoencoder  430  can implement a CNN architecture, and the encoder network  432  and decoder network  434  can include neural networks. 
     At the encoder  426 , the interpolation network  420 A can use reference frames  402  (frame x 0 ) and  404  (frame x 1 ) to generate frame prediction  410  ({tilde over (x)}) for a frame  406  (frame x t ). In some cases, the frame prediction  410  can include a frame predicted using the reference frame  402  and the reference frame  404 . For example, the frame prediction  410  can provide a prediction of frame  406 . The frame  406  (x t ) can be a frame temporally/sequentially after reference frame  402  and before reference frame  404 . In some examples, the frame  406  (x t ) can be a current frame at time t, the reference frame  402  can be a past frame (e.g., temporally/sequentially before the current frame at time t) at time t−n and the reference frame  404  can be a future frame (e.g., temporally/sequentially after the current frame) at time t+m, where n and m can represent the same or different values. 
     In some cases, the frame  406  can be a B-frame, and each of the reference frames  402  and  404  can be an I-frame or a B-frame. For example, reference frame  402  can be an I-frame or a B-frame and reference frame  404  can be an I-frame or a B-frame. In some cases, both the reference frames  402  and  404  can be a same type of frame, such as a B-frame or an I-frame. In other cases, reference frame  402  and reference frame  404  can be different types of frames. For example, reference frame  402  can be a B-frame and reference frame  404  can be an I-frame. In some cases, the reference frames  402  and  404  can be selected according to a sequential scheme  700  or a hierarchical scheme  715 , as shown in  FIGS. 7A and 7B  and further described below. 
     To generate the frame prediction  410 , the interpolation network  420 A can perform frame interpolation to generate the frame prediction  410  given the reference frame  402  and the reference frame  404 . The interpolation network  420 A can perform any frame interpolation technique such as, for example and without limitation, frame interpolation based on optical flows, moving gradients methods, phase-based methods, kernel-based methods, and/or any other frame interpolation technique. In some examples, to generate the frame prediction  410 , the interpolation network  420 A can calculate motion estimation information (e.g., optical flow information), such as flow maps (also referred to as motion vector maps), for reference frame  402  and reference frame  404 , and interpolate the motion estimation information (e.g., the flow maps) to determine a motion estimation (e.g., an optical flow) for a time t of the frame  406  associated with the frame prediction  410 . The interpolation network  420 A can use the interpolated motion estimation information (e.g., the interpolated optical flow) and the reference frames  402  and  404  to generate a warped frame representing the frame prediction  410 . 
     In some examples, the interpolation network  420 A can determine motion estimation information (e.g., optical flow information) on a pixel-by-pixel basis. For instance, the interpolation network  420 A can map each pixel in the reference frame  402  (or a subset of pixels in the reference frame  402 ) to the location of the corresponding pixel in the reference frame  404  (or a corresponding subset of pixels in the reference frame  404 ), and vice versa (e.g., in both directions). The motion estimation information (e.g., optical flow information) for each pixel can include a vector indicating a movement of the pixel between the reference frames  402  and  404 . In some examples, optical flow maps can be generated based on the optical flow vectors between the reference frames  402  and  404 . The optical flow maps can include an optical flow vector for each pixel in a frame, where each vector indicates a movement of a pixel between the frames. 
     In one illustrative example, the optical flow vector for a pixel can be a displacement vector (e.g., indicating horizontal and vertical displacements, such as x- and y-displacements) showing the movement of a pixel from a first frame to a second frame. In some cases, the interpolation network  420 A can calculate a 2D flow map (e.g., indicating horizontal and vertical displacements) for the reference frame  402  and a 2D flow map for the reference frame  404 , and merge the 2D flow maps to generate a 3D flow map for the frame prediction  410 . The 3D flow map can include a horizontal and a vertical dimension indicating horizontal and vertical displacements, and a frame dimension including a frame index identifying a frame to which the horizontal and vertical displacements apply. For example, if the frame dimension identifies reference frame  402 , the horizontal and vertical dimensions can indicate the horizontal and vertical displacements for the pixels in the reference frame  402  (e.g., how much the pixels in reference frame  402  should be moved based on the optical flow information). 
     The interpolation network  420 A can use the interpolated optical flow (e.g., the 3D flow map) to warp the reference frame  402  and the reference frame  404  to the specific time step (e.g., time t) of the frame  406  (e.g., frame x t ) associated with the frame prediction  410 , and fuse the warped frames to generate the frame prediction  410  ({tilde over (x)}). 
     The frame prediction  410  can be subtracted  424  from the frame  406  to generate a residual  412  ( r ) which represents a difference between the frame prediction  410  and the frame  406 . An encoder network  432  of the autoencoder  430  can use the residual  412  to generate a latent  414  ( z ) used to represent the residual  412 . In some examples, the encoder network  432  can map the residual  412  to latent code. In some cases, the encoder network  432  can convert the latent  414  into a bit stream via entropy coding. In some examples, the encoder network  432  can also quantize the latent  414 . The quantized latent  414  can include a quantized representation of the compressed frame  406  (e.g., a quantized representation of the residual  412 ). In some cases, the latent  414  can include neural network data (e.g., a node&#39;s activation map or feature map) that represents quantized codes. 
     The decoder network  434  of the autoencoder  430  can decode or reconstruct the latent  414  to generate a decoded residual  416 . The decoded residual  416  can be used with the interpolation result (e.g., the frame prediction  410 ) from the interpolation network  420 B to generate a reconstructed frame  418  (frame {tilde over (x)}) representing frame  406  at time t. For example, the interpolation network  420 B at the decoder  428  can add  422  the frame prediction  410  to the decoded residual  416  to generate the reconstructed frame  418 . The interpolation network  420 B can use any frame interpolation technique to generate the frame prediction  410 . In some cases, the interpolation network  420 B can use the reference frame  402 , the reference frame  404  and the interpolated motion estimation information (e.g., interpolated optical flow) to generate the frame prediction  410 , as previously described with respect to the interpolation network  420 A at the encoder  426 . 
     The interpolation network  420 B at the decoder  428  can generate the frame prediction  410  and the reconstructed frame  418  without motion estimation information (e.g., optical flow information) from the encoder  426  or a copy of the frame  406 . Thus, the encoder  426  does not have to transmit motion estimation information or a copy of the frame  406  to the decoder  428  (e.g., to the interpolation network  420 B) for the decoder  428  to generate the reconstructed frame  418 . The encoder  426  may provide the residual for the decoder  428  without sending motion estimation information or the frame  406 , and thus reduce the amount of data transmitted to the decoder  428  and the amount of bandwidth used by the system  400 . 
     In some cases, the interpolation network  420 A and/or the interpolation network  420 B can be trained and optimized using one or more loss functions. In some examples, the interpolation network  420 A and the interpolation network  420 B can be trained end-to-end. In some cases, a loss function Loss can equal D+βR, where D is a distortion between a frame (e.g., frame  406 ) and its reconstructed frame (e.g., D(frame x, frame {circumflex over (x)})), β is a hyper parameter that controls a bitrate (bits per pixel), and R is the number of bits used to convert the residual (e.g., residual  416 ) to a compressed bitstream (e.g., latent  414 ). In some examples, the distortion D can be calculated based on a peak signal-to-noise ratio (PSNR), a structural similarity index measure (SSIM), a multiscale SSIM (MS-SSIM), and/or the like. 
     In some cases, an interpolation network can also use contextual information obtained for the frame  406  to calculate the interpolated motion estimation information (e.g., optical flow) used to generate the frame prediction (e.g., frame prediction  410 ). The interpolation network can receive the contextual information from a context network at the encoder. 
       FIG. 4B  is a diagram illustrating an example system  440  configured to perform learned video coding using interpolation aided by contextual information. In this example, the encoder  426  can include a context network  442  in addition to the interpolation network  420 A. The system  440  can also include the decoder  428  and an autoencoder  470 . The autoencoder  470  can be a same autoencoder or separate autoencoder as the autoencoder  430  previously described. In some examples, the context network  442  can include one or more neural networks, such as one or more CNNs. In some cases, the context network  442  can include a U-Net architecture. 
     As shown, the context network  442  can use the reference frame  402 , the reference frame  404 , and the frame  406  to extract contextual information  444  ( c ) for the frame  406 . The contextual information  444  can provide information about the frame  406  as compared to the reference frame  402  and the reference frame  404 . For example, the contextual information  444  can identify one or more occluded areas in the frame  406 , movements around edges in the frames  406 , one or more features in frame  406 , and/or any high level information about frame  406 . 
     The interpolation network  420 A can use the contextual information  444 , along with the reference frame  402  and the reference frame  404 , to calculate a frame prediction  450  for frame  406 . The frame prediction  450  can be subtracted  424  from the frame  406  to generate a residual  452  ( r ) which represents a difference between the frame prediction  450  and the frame  406 . In some examples, the interpolation network  420 A or any other component can subtract  424  the frame prediction  450  from the frame  406  to determine the residual  452 . In some examples, the interpolation network  420 A can use the contextual information  444  to refine motion estimation information (e.g., optical flow information) calculated for the frame  406  by interpolating motion estimation information from the reference frame  402  and the reference frame  404 . For example, the interpolation network  420 A can use the contextual information  444  to refine a non-linearity of motion information calculated from the reference frame  402  and the reference frame  404 . 
     In some cases, the contextual information  444  can allow the interpolation network  420 A to reduce the amount of data (e.g., the bits) in the residual  452  and thus lower the bitrate (bits per pixel) transmitted to the decoder  428  and/or the interpolation network  420 B at the decoder  428 . For example, the context network  442  and the interpolation network  420 A can be trained and optimized to reduce the amount of distortion (D) between the frame  406  and the reconstructed frame. Since the amount of residual and contextual information bits transmitted (e.g., the rate) by the interpolation network  420 A to the decoder  428  can depend on the amount of distortion (D), by allowing the interpolation network  420 A to reduce the residual bits, the contextual information  444  can allow the interpolation network  420 A to reduce the rate (and/or the residual bits) transmitted to the decoder  428 . 
     In some cases, the interpolation network  420 A, the interpolation network  420 B, and/or the context network  442  can be trained and optimized using one or more loss functions. In some examples, a loss function Loss can equal D+βR, where D is a distortion between a frame (e.g., frame  406 ) and its reconstructed frame (e.g., D(frame x, frame {circumflex over (x)})), β is a hyper parameter that controls a bitrate, and R is the number of bits used to convert the residual (e.g., residual  452 ) and/or the contextual information  444  to a compressed bitstream (e.g., bitstream  454 ). In some examples, the distortion D can be calculated based on PSNR, SSIM, MS-SSIM, and/or the like. 
     The encoder network  472  of the autoencoder  470  can encode the contextual information  444  and the residual  452  into bitstream  454 . As previously noted, the autoencoder  470  can be a same autoencoder or separate autoencoder as the autoencoder  430  shown in  FIG. 4A . In some examples, the bitstream  454  can include a compressed bitstream with compressed contextual information (e.g., compressed contextual information  444 ) and a compressed residual (e.g., compressed residual  452 ). The decoder network  474  of the autoencoder  470  can decode the bitstream  454  and obtain the decoded residual  456  and the decoded contextual information  458  from the bitstream  454  for generating the reconstructed frame  460  (frame {circumflex over (x)}). 
     The autoencoder  470  can send the decoded contextual information  458  to the interpolation network  420 B at the decoder  428 , for use by the interpolation network  420 B to generate a reconstructed frame  460  (frame {circumflex over (x)}) representing frame  406  at time t. The frame prediction  450  and the decoded residual  456  can be added  422  to generate the reconstructed frame  460  (frame {circumflex over (x)}). In some examples, the interpolation network  420 B can use the reference frame  402 , the reference frame  404  and the interpolated motion estimation information (e.g., interpolated optical flow) to generate the frame prediction  450 , as previously described with respect to the interpolation network  420 A at the encoder  426 . 
     The interpolation network  420 B at the decoder  428  can generate the frame prediction  450  and the reconstructed frame  460  without motion estimation information (e.g., optical flow information) from the encoder  426  or a copy of the frame  406 . Thus, the encoder  426  does not have to transmit motion estimation information or a copy of the frame  406  to the decoder  428  (e.g., to the interpolation network  420 B) for the decoder  428  to generate the reconstructed frame  460 . The encoder  426  may provide the residual and contextual information for the decoder  428  without sending motion estimation information or the frame  406 , and thus reduce the amount of data transmitted to the decoder  428  and the amount of bandwidth used by the system  440 . 
     While  FIGS. 4A and 4B  are described with respect to using optical flow interpolation, it should be noted that optical flow interpolation is merely used herein as an illustrative example for explanation purposes. One of ordinary skill in the art will recognize that the system  400  in  FIG. 4A  and the system  440  in  FIG. 4B  can implement any other frame interpolation technique. 
       FIG. 5  is a diagram illustrating an example system flow  500  of an example interpolation network (e.g., interpolation network  420 A,  402 B). In this example, the interpolation network includes an optical flow estimation layer  502 , an optical flow prediction layer  504 , a 2D warping layer  506 , a refinement layer  508  and a 3D warping layer  510 . 
     In some examples, the optical flow estimation layer  502  can include a one or more neural networks. For example, the optical flow estimation layer  502  can be implemented using one or more optical flow estimation networks. The optical flow estimation layer  502  can use the reference frame  402  and the reference frame  404  to estimate bidirectional motion information  520  (e.g., forward optical flow and backward optical flow) for reference frame  402  and reference frame  404 . The bidirectional motion information  520  can include an optical flow or optical flow map from reference frame  402  (x 0 ) to reference frame  404  (x 1 ) denoted as f 0→1 , and an optical flow or optical flow map from reference frame  404  (x 1 ) to reference frame  402  (x 0 ) denoted as f 1→0 . 
     For instance, the bidirectional motion information  520  can include an optical flow map f 0→1  that maps the location of each pixel in the reference frame  402  (or a subset of pixels in the reference frame  402 ) to the location of the corresponding pixel in the reference frame  404  (or a corresponding subset of pixels in the reference frame  404 ), and an optical flow map f 1→0  that maps the location of each pixel in the reference frame  404  (or a subset of pixels in the reference frame  404 ) to the location of the corresponding pixel in the reference frame  402  (or a corresponding subset of pixels in the reference frame  402 ). In some cases, the motion information  520  for each pixel can include a vector indicating a movement of the pixel between the reference frames  402  and  404 . In some examples, optical flow maps f 0→1  and f 1→0  can be generated based on optical flow vectors between the reference frames  402  and  404 . The optical flow maps can include an optical flow vector for each pixel in a frame, where each vector indicates a movement of a pixel between the frames. 
     The optical flow estimation layer  502  can provide the bidirectional motion information  520  (e.g., f 0→1  and f 1→0 ) to the optical flow prediction layer  504 . The optical flow prediction layer  504  can obtain time information  522  indicating a time tin the interval 0 to 1 (e.g., t∈(0,1), where 0 represents a time step of reference frame  402 , 1 represents a time step of reference frame  404 , and time t corresponds to a time step of the fame to be coded (e.g., frame  406 ). The optical flow prediction layer  504  can use the bidirectional motion information  520  and the time information  522  to predict 2D optical flow maps  524 . In some examples, the optical flow estimation layer  502  can interpolate the bidirectional motion information  520  (e.g., f 0→1  and f 1→0 ) for reference frame  402  and reference frame  404 , to determine the optical flow at time t. In some cases, the optical flow at time t can be the linear interpolation between f 0→1  and f 1→0 . 
     In some examples, the 2D optical flow maps  524  can include an optical flow map (e.g., {tilde over (f)} t→0   2D ) from time t to the reference frame  402  (e.g., to time step  0  of reference frame  402 ) and an optical flow map (e.g., {tilde over (f)} t→1   2D ) from time t to the reference frame  404  (e.g., to time step  1  of reference frame  404 ). For example, the 2D optical flow maps  524  can include a backward 2D flow map {tilde over (f)} t→0   2D  with motion information from time t to a time series (e.g., time series  0  in the example shown in  FIG. 5 ) of reference frame  402  and a forward 2D flow map {tilde over (f)} t→1   2D  with motion information from time t to a time series (e.g., time series  1  in the example shown in  FIG. 5 ) of reference frame  404 . 
     In some examples, the optical flow prediction layer  504  can predict the 2D optical flows according to Equations 1 and 2 as follows:
 
 {tilde over (f)}   t→0   2D =1(1− t ) tf   0→1   +t   2   f   1→0   Equation (1)
 
 {tilde over (f)}   t→1   2D =1(1− t ) 2   f   0→1   −t (1− t ) f   1→0   Equation (2)
 
     In Equation 1 and Equation 2 above, t represents the time step of the frame being coded (e.g., frame  406 ) between a time step of reference frame  402  and a time step of reference frame  404  (e.g., a time step in the interval 0 to 1), f 0→1  represents an optical flow from reference frame  402  to reference frame  404 , f 1→0  represents an optical flow from reference frame  404  to reference frame  402 , {tilde over (f)} t→0   2D  represents a 2D optical flow map identifying backward motion from t to the time step associated with the reference frame  402  (e.g., time step  0  in the example shown in  FIG. 5 ), and {tilde over (f)} t→1   2D  represents a 2D optical flow map identifying forward motion from t to the time step associated with the reference frame  404  (e.g., time step  1  in the example shown in  FIG. 5 ). 
     The 2D warping layer  506  can obtain the 2D optical flow maps  524 , the reference frame  402 , and the reference frame  404  and generate warped frames  526 . The warped frames  526  can include a warped frame (W x0 ) corresponding to the reference frame  402  and generated by applying the 2D optical flow map {tilde over (f)} t→0   2D  to reference frame  402 , and a warped frame (W x1 ) corresponding to the reference frame  404  and generated by applying the 2D optical flow map {tilde over (f)} t→1   2D  to reference frame  404 . The 2D warping layer  506  can implement one or more warp functions to warp the reference frame  402  to the time step t and warp the reference frame  404  to the time step t. 
     In some examples, the 2D warping layer  506  can use the 2D optical flow map {tilde over (f)} t→0   2D  calculated for the reference frame  402  to move pixels in the reference frame  402  to respective locations corresponding to and/or relative to time t. Similarly, the 2D warping layer  506  can use the 2D optical flow map {tilde over (f)} t→1   2D  calculated for the reference frame  404  to move pixels in the reference frame  404  to respective locations corresponding to and/or relative to time t. In some cases, the warped frames  526  can include a 2D frame for reference frame  402  that accounts for vertical and/or horizontal displacements of pixels (and/or pixel locations) at time t, and a 2D frame for reference frame  404  that accounts for vertical and/or horizontal displacements of pixels (and/or pixel locations) at time t. 
     The refinement layer  508  can obtain the 2D optical flow maps  524 , the warped frames  526 , and the reference frames  402  and  404 , and perform refinement operations. In some cases, the refinement layer  508  can include one or more neural networks. For example, the refinement layer  508  can include one or more interpolation CNNs. In some examples, the refinement operations can include refining and/or correcting values in the 2D optical flow maps  524 . For example, in some cases, some of the movement between time step  0  corresponding to reference frame  402  and time step  1  corresponding to reference frame  404  may not be linear. The refinement layer  508  can detect and correct such non-linear movement. The refinement layer  508  can generate refined 2D optical flows that account for such non-linearity. In other examples, the refinement layer  508  can refine or correct artifacts in any of the warped frames  526  (and/or any of the reference frames  402  and  404 ), refine or correct portions of any the warped frames  526  (and/or any of the reference frames  402  and  404 ) such as edges, make refinements or corrections to account for an occlusion(s) in any of the warped frames  526  (and/or any of the reference frames  402  and  404 ), and/or perform any other refinement of image data and/or optical flow information associated with the warped frames  526  (and/or the reference frames  402  and  404 ). 
     In some examples, the refinement operations can also include merging the 2D optical flow maps  524  into a 3D optical flow map  528  for the frame to be decoded (e.g., frame  406 ). For example, as previously noted, the 2D optical flow maps  524  can include a 2D optical flow map (e.g., {tilde over (f)} t→0   2D ) for the reference frame  402  and a 2D optical flow map (e.g., {tilde over (f)} t→1   2D ) for the reference frame  404 . The refinement layer  508  can merge the 2D optical flow maps  524  (e.g., the 2D optical flow map {tilde over (f)} t→0   2D  and the 2D optical flow map {tilde over (f)} t→1   2D ) to generate a 3D optical flow map  528 . The 3D optical flow map  528  can map specific frames (e.g., reference frame  402 , reference frame  404 ) to corresponding optical flow information. For example, the 3D optical flow map  528  can include two spatial dimensions (e.g., X and Y axes or vertical and horizontal dimensions), and a third dimension (e.g., a frame dimension) including frame information. The frame information in the third dimension can identify the frame (e.g., reference frame  402  or reference frame  404 ) to which the motion information (e.g., vertical and/or horizontal displacements) in the two spatial dimensions applies. 
     To illustrate, if the third dimension (e.g., the frame dimension) identifies reference frame  402 , the corresponding values in the spatial dimensions (e.g., in the X and Y axes or vertical and horizontal dimensions) can indicate the horizontal and/or vertical displacements for the pixels in the reference frame  402  (e.g., how much the pixels in reference frame  402  should be moved based on the optical flow information in the spatial dimensions). Similarly, if the third dimension identifies reference frame  404 , the corresponding values in the spatial dimensions can indicate the horizontal and/or vertical displacements for the pixels in the reference frame  404  (e.g., how much the pixels in reference frame  404  should be moved based on the optical flow information in the spatial dimensions). 
     In some cases, the refinement layer  508  can use the 2D optical flow maps  524  to merge the 2D optical flow maps  524  into the 3D optical flow map  528 . In some examples, the refinement layer  508  can use the refined 2D optical flows generated by refining the 2D optical flow maps  524  to merge the 2D optical flow maps  524  into the 3D optical flow map  528 . In some cases, the refinement layer  508  can use the reference frames  402  and  404 , the warped frames  526 , and/or the 2D optical flow maps  524  (or the refined 2D optical flows) as inputs to generate the 3D optical flow map  528 . 
     In some cases, the refinement layer  508  can optionally use contextual information  530  as an additional input for the refinement operations. For example, the refinement layer  508  can receive contextual information generated by context network  442  shown in  FIG. 4B , and use the contextual information to assist in refining or correcting the values in the 2D optical flow maps  524  and/or in merging the 2D optical flow maps  524  into a 3D optical flow map  528 . In some cases, the refinement layer  508  can use the contextual information to detect and refine or correct non-linearities in the 2D optical flow maps  524 , refine or correct artifacts in any of the warped frames  526  (and/or any of the reference frames  402  and  404 ), etc. In some examples, the contextual information  530  can include the contextual information  444  or the decoded contextual information  458  previously described with respect to  FIG. 4B . 
     The 3D warping layer  510  can use the 3D optical flow map  528  and the reference frames  402  and  404  (and/or the warped frames  526 ) to generate a frame prediction  532  ({tilde over (x)}) for the frame corresponding to time step t (e.g., frame  406  or frame x t ). For example, the 3D warping layer  510  can use the 3D optical flow map  528  to warp the reference frames  402  and  404  (or the warped frames  526 ), and fuse the warped frames (e.g., the warped reference frames  402 - 404  or warped frames  526 ) to generate the frame prediction  532 . In some cases, the 3D warping layer  510  can implement one or more warping functions. 
     In some cases, the optical flow estimation layer  502 , the optical flow prediction layer  504 , the 2D warping layer  506 , the refinement layer  508  and/or the 3D warping layer  510  can be trained and/or optimized using one or more loss functions. In some cases, the optical flow estimation layer  502 , the optical flow prediction layer  504 , the 2D warping layer  506 , the refinement layer  508  and the 3D warping layer  510  can be trained end-to-end using one or more loss functions. 
     In some examples, a loss function Loss can equal D+βR, where D is a distortion between a frame (e.g., frame  406 ) and its reconstructed frame (e.g., D(frame x, frame {tilde over (x)})), β is a hyper parameter that controls a bitrate, and R is the number of bits used to convert the residual (e.g., residual  412  or residual  452 ) and/or contextual information (e.g., contextual information  530 ) to a compressed bitstream. In some examples, the distortion D can be calculated based on PSNR, SSIM, MS-SSIM, and/or the like. 
     While  FIG. 5  is described with reference to using optical flow interpolation, it should be noted that optical flow interpolation is merely used herein as an illustrative example for explanation purposes. One of ordinary skill in the art will recognize that any other frame interpolation technique can be used in the system flow  500  shown in  FIG. 5  and escribed above. 
       FIG. 6  is a diagram illustrating different types of motion estimations that can be performed to determine motion information between reference frames (e.g., from reference frame  402  to reference frame  404  or vice versa) and warp a reference frame. In  FIG. 6 , the term x denotes a reference frame from which motion can be estimated, the term f denotes a motion estimation, and the term y denotes a warped frame that can be computed as follows: y=f(x). 
     One type of motion estimation is a block-based motion estimation technique  602 . The block-based motion estimation can be performed on a block-by-block basis. For instance, for each block in the frame y, the motion estimation f defines the location of the corresponding block in the frame x. In one illustrative example, the motion estimation f can include a motion vector that indicates the displacement (e.g., the horizontal and vertical displacement) of a block in the frame y relative to the corresponding block in the frame x. A block from the frame x can be determined to correspond to a block in the frame y by determining a similarity (e.g., a similarity in pixel values) between the blocks. 
     Another type of motion estimation that can performed is an optical flow motion estimation technique  604 . The optical flow motion estimation can be performed on a pixel-by-pixel basis. For instance, for each pixel in the frame y, the motion estimation f defines the location of the corresponding pixel in the frame x. The motion estimation f for each pixel can include a vector indicates a movement of the pixel between the frames. In some cases, optical flow maps (also referred to as motion vector maps) can be generated based on the computation of the optical flow vectors between frames. The optical flow maps can include an optical flow vector for each pixel in a frame, where each vector indicates a movement of a pixel between the frames. In one illustrative example, the optical flow vector for a pixel can be a displacement vector (e.g., indicating horizontal and vertical displacements, such as x- and y-displacements) showing the movement of a pixel from a first frame to a second frame. 
     In some cases, the optical flow map can include vectors for less than all pixels in a frame. For instance, a dense optical flow can be computed between frames to generate optical flow vectors for each pixel in a frame, which can be included in a dense optical flow map. In some examples, each optical flow map can include a 2D vector field, with each vector being a displacement vector showing the movement of points from a first frame to a second frame. 
     As noted above, an optical flow vector or optical flow map can be computed between frames of a sequence of frames. Two frames can include two directly adjacent frames that are consecutively captured frames or two frames that are a certain distance apart (e.g., within two frames of one another, within three frames of one another, or any other suitable distance) in a sequence of frames. In one illustrative example, a pixel I(x, y, t) in the frame x can move by a distance (Δx, Δy) in the frame y. 
     Another type of motion estimation that can performed is a dynamic convolution (also referred to as adaptive convolution) motion estimation technique  606 . The dynamic convolution motion estimation can be performed on a pixel-by-pixel basis. For instance, for each pixel in the frame y, the motion estimation f defines a linear combination of a neighborhood of pixels in the frame x. As shown in  FIG. 6 , for a pixel at a pixel location in the frame y, a kernel or matrix is defined around a neighborhood of pixels surrounding a pixel at a same pixel location in the frame x. The example dynamic convolution motion estimation in  FIG. 6  is performed for a pixel at a location (2, 7) (corresponding to (row number, column number)) in the frame y, where the top-left pixel location in the frame y is at location (0, 0). The kernel is defined around a pixel at location (2, 7) in the frame x. For example, as shown, a kernel of size 5×5 is applied to a neighborhood of pixels surrounding the pixel at location (2, 7) in the frame x. 
     Each entry in the kernel or matrix can include a number (e.g., a set of 5×5 numbers in the example of  FIG. 6 ). The set of numbers in the kernel can describe how to combine the pixels from the neighborhood of pixels that surround the pixel at the pixel location in the frame x corresponding to the same pixel location of the pixel that is being mapped and/or reconstructed in frame y. 
       FIG. 7A  is a diagram illustrating an example sequential scheme  700  for bidirectional frame prediction. The sequential scheme  700  can be used to select reference frames for bidirectional coding of B-frames. In this example, I-frame  702  and I-frame  704  can be used as reference frames (e.g., reference frame  402  and reference frame  404 ) for interpolating motion information and predicting a B-frame (e.g., frame  406 ) as previously described. 
     B-frame  710  can then be used with I-frame  704  to interpolate motion information and predict B-frame  712 . Similarly, B-frame  712  can then be used with I-frame  704  to interpolate motion information and predict B-frame  714 . In this way, I-frame  702 , B-frame  710 , and B-frame  712  are sequentially used along with I-frame  704  to interpolate motion information and predict a frame. 
       FIG. 7B  is a diagram illustrating an example hierarchical scheme  715  for bidirectional frame prediction. The hierarchical scheme  715  can be used to select B-frames for use as reference frames in bidirectional coding of B-frames. In this example, I-frame  720  and I-frame  722  can be used as reference frames (e.g., reference frame  402  and reference frame  404 ) for interpolating motion information and predicting B-frame  730 . 
     B-frame  730  can then be used with I-frame  720  to interpolate motion information and predict B-frame  732  between B-frame  730  and I-frame  720 . Similarly, B-frame  730  can be used with I-frame  722  to interpolate motion information and predict B-frame  738  between B-frame  730  and I-frame  722 . 
     B-frame  732  can be used with I-frame  720  to interpolate motion information and predict B-frame  734  between B-frame  732  and I-frame  720 . B-frame  732  can also be used with B-frame  730  to interpolate motion information and predict B-frame  736  between B-frame  732  and B-frame  730 . 
     B-frame  738  can be used with B-frame  730  to interpolate motion information and predict B-frame  740  between B-frame  730  and B-frame  738 . B-frame  738  can also be used with I-frame  722  to interpolate motion information and predict B-frame  742  between B-frame  738  and I-frame  722 . 
     As illustrated in this example, a hierarchical order and/or sequence can be used to select the I-frames and/or B-frames as reference frames for bidirectional coding B-frames. 
     The sequential scheme  700  shown in  FIG. 7A  and the hierarchical scheme  715  shown in  FIG. 7B  can be used to select reference frames  402  and  404  to use for interpolating motion information for frame  406 , predicting frame  406 , reconstructing frame  406 , and/or any other coding operations as described herein. It should be noted that the sequential scheme  700  shown in  FIG. 7A  and the hierarchical scheme  715  shown in  FIG. 7B  are merely illustrative examples and other schemes can also be used to implement the techniques described herein. 
       FIG. 8  is a flowchart illustrating an example process  800  for learned coding of frames, in accordance with some examples. At block  802 , the process  800  can include receiving, from an encoder portion (e.g., encoder  426  and/or interpolation network  420 A) of a neural network system (e.g., system  400  or system  440 ), a residual (e.g., residual  412  or residual  452 ) associated with a frame (e.g., frame  406 ) of a current processing time step (e.g., time t). 
     At block  804 , the process  800  can include determining first motion information (e.g., motion information from the motion information  520 , an optical flow map from 2D optical flows maps  524 , etc.) for a first reference frame (e.g., reference frame  402 ) associated with a first processing time step (e.g., time step  0 ) and second motion information (e.g., motion information from the motion information  520 , an optical flow map from 2D optical flow maps  524 , etc.) for a second reference frame (e.g., reference frame  404 ) associated with a second processing time step (e.g., time step  1 ). The current processing time step can be after the first processing time step and before the second processing time step. 
     In some cases, motion information (and/or optical flow maps) can be generated using Equations 1 and 2, as previously explained. In some examples, the first reference frame and/or the second reference frame can be an intra-coded frame (I-frame) and/or an additional B-frame. In some cases, the first reference frame and the second reference frame can be a same type of frame (e.g., an I-frame or B-frame). In other cases, the first reference frame and the second reference frame can be a different type of frame (e.g., an I-frame and B-frame). 
     At block  806 , the process  800  can include determining, using a decoder portion (e.g., decoder  428  and/or interpolation network  420 B) of the neural network system, third motion information (e.g., 3D optical flow map  528 ) for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame. As noted above, in some examples, the first motion information can include the motion information  520 , an optical flow map from 2D optical flows maps  524 , and/or other information for the reference frame  402 , and the second motion information can include the motion information  520 , an optical flow map from 2D optical flow maps  524 , and/or other information for the reference frame  404 . 
     At block  808 , the process  800  can include generating, using the decoder portion of the neural network system, a predicted frame (e.g., frame prediction  410 , frame prediction  450 , frame prediction  532 ) based on the third motion information of the frame, the first reference frame, and the second reference frame. At block  810 , the process  800  can include generating, using the predicted frame and the residual, a reconstructed bidirectional predicted frame (B-frame) (e.g., reconstructed frame  418 , reconstructed frame  460 ) for the current processing time step. The reconstructed B-frame can represent the frame. 
     In some examples, the first motion information can include a first 2D optical flow map (e.g., an optical flow map from 2D optical flow maps  524 ) calculated from the current processing time step to the first reference frame, and the second motion information can include a second 2D optical flow map (e.g., a different optical flow map from 2D optical flow maps  524 ) calculated from the current processing time step to the second reference frame. In some cases, the third motion information can include a 3D optical flow map (e.g., 3D optical flow map  528 ) generated based on the first 2D optical flow map and the second 2D optical flow map. 
     In some cases, the first 2D optical flow map and the second 2D optical flow map can include spatial dimensions (e.g., X and Y dimensions or horizontal and vertical dimensions) indicating vertical and horizontal displacements. In some cases, the 3D optical flow map can include spatial dimensions e.g., X and Y dimensions or horizontal and vertical dimensions) indicating estimated vertical and horizontal displacements and a frame dimension mapping each of the estimated vertical and horizontal displacements in the spatial dimensions to a corresponding one of the first reference frame and the second reference frame. 
     In some examples, the process  800  can include determining, using the decoder portion of the neural network system, forward motion information (e.g., forward motion information from the motion information  520 ) from the first reference frame to the second reference frame and backward motion information (e.g., backward motion information from the motion information  520 ) from the second reference frame to the first reference frame; determining, using the decoder portion of the neural network system, the first 2D optical flow map based on the forward motion information and a value of an interval (e.g., time information  522 ) of the first processing time step and the second processing time step; and determining, using the decoder portion of the neural network system, the second 2D optical flow map based on the backward motion information and the value of the interval. In some examples, the value of the interval can correspond to the current processing time step 
     In some cases, the process  800  can include warping (e.g., via 3D warping layer  510 ), using the 3D optical flow map, the first reference frame and the second reference frame to the current processing time step. In some examples, the predicted frame can be generated based on the 3D optical flow map, the warped first reference frame and the warped second reference frame. 
     In some cases, the process  800  can include adjusting (e.g., refining via refinement layer  508 ) one or more motion values in the first 2D optical flow map and/or the second 2D optical flow map based on a detected non-linearity in the one or more motion values, and after adjusting the one or more values in the first 2D optical flow map and/or the second 2D optical flow map, merging (e.g., via the refinement layer  508 ) the first 2D optical flow map and the second 2D optical flow map into the 3D optical flow map. 
     In some cases, the process  800  can include receiving, from the encoder portion of the neural network system, an encoded bitstream (e.g., bitstream  454 ) including the residual and contextual information (e.g., contextual information  444 , contextual information  530 ) extracted from image data associated with the frame, the first reference frame and/or the second reference frame (e.g., extracted from the frame, the first reference frame, and/or the second reference frame); decoding, using the decoder portion of the neural network system, the encoded bitstream; and adjusting the one or more values in the first 2D optical flow map and/or the second 2D optical flow map, the one or more values being adjusted further based on the contextual information. In some examples, the contextual information can include an occlusion in the image data, one or more edges in the image data, one or more features in the image data, and/or any other information about the frames. 
     In some examples, the process  800  can include warping (e.g., via 2D warping layer  506 ), using the first 2D optical flow map, the first reference frame to the current processing time step, and warping, using the second 2D optical flow map, the second reference frame to the current processing time step. In some cases, the 3D optical flow map is generated based on the first reference frame, the second reference frame, the warped first reference frame, the warped second reference frame, the first 2D optical flow map, and/or the second optical flow map. 
     In some examples, the process  800  can include receiving, from the encoder portion of the neural network system, an encoded bitstream including the residual, and decoding, using the decoder portion of the neural network system, the encoded bitstream. 
     In some examples, the process  800  can include generating, using the decoder portion of the neural network system, the predicted frame and the reconstructed B-frame without the frame and without motion information from the encoder portion of the neural network system. 
     In some examples, the process  800  can include determining, using the encoder portion of the neural network system, the first motion information for the first reference frame and the second motion information for the second reference frame; determining, using the encoder portion of the neural network system, the third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; and generating, using the encoder portion of the neural network system, the predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame. 
     In some examples, the process  800  can include generating, using the encoder portion of the neural network system, the residual based on the predicted frame and the frame; generating, using the encoder portion of the neural network system, an encoded bitstream comprising the residual; and transmitting, using the encoder portion of the neural network system, the encoded bitstream to the decoder portion of the neural network system. 
     In some examples, the process  800  can include determining, using the encoder portion of the neural network system, contextual information extracted from image data associated with the frame, the first reference frame and/or the second reference frame. In some cases, the encoded bitstream can further include the contextual information. 
     In some examples, the process  800  can include training, for one or more training iterations, the neural network system using a loss function (e.g., Loss). In some cases, the loss function can determine a loss between one or more reconstructed B-frames and one or more frames represented by the one or more reconstructed B-frames. In some examples, the first loss function can equal D+βR, as previously described. 
     In some examples, the processes described herein (e.g., process  800  and/or other process described herein) may be performed by a computing device or apparatus, such as a computing device having the computing device architecture  900  shown in  FIG. 9 . The computing device can include any suitable device, such as an autonomous vehicle computer, a robotic device, a mobile device (e.g., a mobile phone), a desktop computing device, a tablet computing device, a wearable device, a server device, a video game device, an extended reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a camera device, a set-top box device, and/or any other computing device with the resource capabilities to perform the processes described herein, including process  800 . In some examples, the computing device can include a mobile device, a wearable device, an XR device, a personal computer, a laptop computer, a video server, a television, a camera, a set-top box, a video game console, or other device. In some examples, the process  800  can be performed by a computing device with the computing device architecture  900  implementing the system  100 , system  400  and/or the system  440  (using any of the architectures shown in  FIG. 2A - FIG. 5  or any combination thereof). 
     In some cases, the computing device or apparatus may include various components, such as one or more input devices, one or more output devices, one or more processors, one or more microprocessors, one or more microcomputers, one or more transmitters, receivers or combined transmitter-receivers (e.g., referred to as transceivers), one or more cameras, one or more sensors, and/or other component(s) that are configured to carry out the steps of processes described herein. In some examples, the computing device may include a display, a network interface configured to communicate and/or receive the data, any combination thereof, and/or other component(s). The network interface may be configured to communicate and/or receive Internet Protocol (IP) based data or other type of data. 
     The components of the computing device can be implemented in circuitry. For example, the components can include and/or can be implemented using electronic circuits or other electronic hardware, which can include one or more programmable electronic circuits (e.g., microprocessors, graphics processing units (GPUs), digital signal processors (DSPs), central processing units (CPUs), neural processing units (NPUs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and/or other suitable electronic circuits), and/or can include and/or be implemented using computer software, firmware, or any combination thereof, to perform the various operations described herein. 
     Process  800  is illustrated as a logical flow diagram, the operation of which represents a sequence of operations that can be implemented in hardware, computer instructions, or a combination thereof. In the context of computer instructions, the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations can be combined in any order and/or in parallel to implement the processes. 
     Additionally, the processes described herein (including process  800 ) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. As noted above, the code may be stored on a computer-readable or machine-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. The computer-readable or machine-readable storage medium may be non-transitory. 
       FIG. 9  illustrates an example computing device architecture  900  of an example computing device which can implement the various techniques described herein. In some examples, the computing device can include a mobile device, a wearable device, an XR device, a personal computer, a laptop computer, a video server, a video game console, a robotic device, a set-top box, a television, a camera, a server, or other device. For example, the computing device architecture  900  can implement the system  400  and/or the system  440  (including any one of the architectures shown in  FIG. 2A - FIG. 5  or any combination thereof). The components of computing device architecture  900  are shown in electrical communication with each other using connection  905 , such as a bus. The example computing device architecture  900  includes a processing unit (CPU or processor)  910  and computing device connection  905  that couples various computing device components including computing device memory  915 , such as read only memory (ROM)  920  and random access memory (RAM)  925 , to processor  910 . 
     Computing device architecture  900  can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of processor  910 . Computing device architecture  900  can copy data from memory  915  and/or the storage device  930  to cache  912  for quick access by processor  910 . In this way, the cache can provide a performance boost that avoids processor  910  delays while waiting for data. These and other modules can control or be configured to control processor  910  to perform various actions. Other computing device memory  915  may be available for use as well. Memory  915  can include multiple different types of memory with different performance characteristics. Processor  910  can include any general purpose processor and a hardware or software service, such as service  1   932 , service  2   934 , and service  3   936  stored in storage device  930 , configured to control processor  910  as well as a special-purpose processor where software instructions are incorporated into the processor design. Processor  910  may be a self-contained system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. 
     To enable user interaction with the computing device architecture  900 , input device  945  can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. Output device  935  can also be one or more of a number of output mechanisms known to those of skill in the art, such as a display, projector, television, speaker device, etc. In some instances, multimodal computing devices can enable a user to provide multiple types of input to communicate with computing device architecture  900 . Communication interface  940  can generally govern and manage the user input and computing device output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed. 
     Storage device  930  is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs)  925 , read only memory (ROM)  920 , and hybrids thereof. Storage device  930  can include services  932 ,  934 ,  936  for controlling processor  910 . Other hardware or software modules are contemplated. Storage device  930  can be connected to the computing device connection  905 . In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor  910 , connection  905 , output device  935 , and so forth, to carry out the function. 
     The term “device” is not limited to one or a specific number of physical objects (such as one smartphone, one controller, one processing system, and so on). As used herein, a device can include any electronic device with one or more parts that may implement at least some portions of this disclosure. While the description and examples use the term “device” to describe various aspects of this disclosure, the term “device” is not limited to a specific configuration, type, or number of objects. Additionally, the term “system” is not limited to multiple components or specific embodiments. For example, a system may be implemented on one or more printed circuit boards or other substrates, and may have movable or static components. While the description and examples use the term “system” to describe various aspects of this disclosure, the term “system” is not limited to a specific configuration, type, or number of objects. 
     Specific details are provided in the description to provide a thorough understanding of the embodiments and examples provided herein. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software. Additional components may be used other than those shown in the figures and/or described herein. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. 
     Individual embodiments may be described above as a process or method which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function. 
     Processes and methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can include, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, source code, etc. 
     The term “computer-readable medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A computer-readable medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as flash memory, memory or memory devices, magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, compact disk (CD) or digital versatile disk (DVD), any suitable combination thereof, among others. A computer-readable medium may have stored thereon code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, or the like. 
     In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se. 
     Devices implementing processes and methods according to these disclosures can include hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and can take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a computer-readable or machine-readable medium. A processor(s) may perform the necessary tasks. Typical examples of form factors include laptops, smart phones, mobile phones, tablet devices or other small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example. 
     The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functions described in the disclosure. 
     In the foregoing description, aspects of the application are described with reference to specific embodiments thereof, but those skilled in the art will recognize that the application is not limited thereto. Thus, while illustrative embodiments of the application have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art. Various features and aspects of the above-described application may be used individually or jointly. Further, embodiments can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. For the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described. 
     One of ordinary skill will appreciate that the less than (“&lt;”) and greater than (“&gt;”) symbols or terminology used herein can be replaced with less than or equal to (“≤”) and greater than or equal to (“≥”) symbols, respectively, without departing from the scope of this description. 
     Where components are described as being “configured to” perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof. 
     The phrase “coupled to” refers to any component that is physically connected to another component either directly or indirectly, and/or any component that is in communication with another component (e.g., connected to the other component over a wired or wireless connection, and/or other suitable communication interface) either directly or indirectly. 
     Claim language or other language reciting “at least one of” a set and/or “one or more” of a set indicates that one member of the set or multiple members of the set (in any combination) satisfy the claim. For example, claim language reciting “at least one of A and B” or “at least one of A or B” means A, B, or A and B. In another example, claim language reciting “at least one of A, B, and C” or “at least one of A, B, or C” means A, B, C, or A and B, or A and C, or B and C, or A and B and C. The language “at least one of” a set and/or “one or more” of a set does not limit the set to the items listed in the set. For example, claim language reciting “at least one of A and B” or “at least one of A or B” can mean A, B, or A and B, and can additionally include items not listed in the set of A and B. 
     The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, firmware, or combinations thereof. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. 
     The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, performs one or more of the methods described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise memory or data storage media, such as random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer, such as propagated signals or waves. 
     The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein. 
     Illustrative examples of the disclosure include: 
     Aspect 1: A method comprising receiving, from an encoder portion of a neural network system, a residual associated with a frame of a current processing time step; determining first motion information for a first reference frame associated with a first processing time step and second motion information for a second reference frame associated with a second processing time step, wherein the current processing time step is after the first processing time step and before the second processing time step; determining, using a decoder portion of the neural network system, third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; generating, using the decoder portion of the neural network system, a predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame; and generating, using the predicted frame and the residual, a reconstructed bidirectional predicted frame (B-frame) for the current processing time step, the reconstructed B-frame representing the frame. 
     Aspect 2: The method of Aspect 1, wherein the first motion information comprises a first two-dimensional (2D) optical flow map calculated from the current processing time step to the first reference frame, wherein the second motion information comprises a second 2D optical flow map calculated from the current processing time step to the second reference frame, and wherein the third motion information comprises a three-dimensional (3D) optical flow map generated based on the first 2D optical flow map and the second 2D optical flow map. 
     Aspect 3: The method of Aspect 2, wherein the first 2D optical flow map and the second 2D optical flow map comprise spatial dimensions indicating vertical and horizontal displacements, and wherein the 3D optical flow map comprises spatial dimensions indicating estimated vertical and horizontal displacements and a frame dimension mapping each of the estimated vertical and horizontal displacements in the spatial dimensions to a corresponding one of the first reference frame and the second reference frame. 
     Aspect 4: The method of any of Aspects 2 to 3, further comprising: determining, using the decoder portion of the neural network system, forward motion information from the first reference frame to the second reference frame, and backward motion information from the second reference frame to the first reference frame; determining, using the decoder portion of the neural network system, the first 2D optical flow map based on the forward motion information and a value of an interval of the first processing time step and the second processing time step, the value of the interval corresponding to the current processing time step; and determining, using the decoder portion of the neural network system, the second 2D optical flow map based on the backward motion information and the value of the interval. 
     Aspect 5: The method of any of Aspects 2 to 4, further comprising: warping, using the 3D optical flow map, the first reference frame and the second reference frame to the current processing time step, wherein the predicted frame is generated based on the 3D optical flow map, the warped first reference frame and the warped second reference frame. 
     Aspect 6: The method of any of Aspects 2 to 5, further comprising: adjusting one or more motion values in at least one of the first 2D optical flow map and the second 2D optical flow map based on a detected non-linearity in the one or more motion values; and after adjusting the one or more values in at least one of the first 2D optical flow map and the second 2D optical flow map, merging the first 2D optical flow map and the second 2D optical flow map into the 3D optical flow map. 
     Aspect 7: The method of any of Aspects 2 to 6, further comprising: receiving, from the encoder portion of the neural network system, an encoded bitstream comprising the residual and contextual information extracted from image data associated with at least one of the frame, the first reference frame and the second reference frame; decoding, using the decoder portion of the neural network system, the encoded bitstream; and adjusting the one or more values in at least one of the first 2D optical flow map and the second 2D optical flow map, the one or more values being adjusted further based on the contextual information, the contextual information comprising at least one of an occlusion in the image data, one or more edges in the image data, and one or more features in the image data. 
     Aspect 8: The method of any of Aspects 2 to 7, further comprising: warping, using the first 2D optical flow map, the first reference frame to the current processing time step; and warping, using the second 2D optical flow map, the second reference frame to the current processing time step, wherein the 3D optical flow map is generated based on at least one of the first reference frame, the second reference frame, the warped first reference frame, the warped second reference frame, the first 2D optical flow map, and the second optical flow map. 
     Aspect 9: The method of any of Aspects 1 to 8, wherein the first reference frame and the second reference frame comprise at least one of an intra-coded frame (I-frame) and an additional B-frame. 
     Aspect 10: The method of any of Aspects 1 to 9, further comprising: receiving, from the encoder portion of the neural network system, an encoded bitstream comprising the residual; and decoding, using the decoder portion of the neural network system, the encoded bitstream. 
     Aspect 11: The method of any of Aspects 1 to 10, further comprising: generating, using the decoder portion of the neural network system, the predicted frame and the reconstructed B-frame without the frame and without motion information from the encoder portion of the neural network system. 
     Aspect 12: The method of any of Aspects 1 to 11, further comprising: determining, using the encoder portion of the neural network system, the first motion information for the first reference frame and the second motion information for the second reference frame; determining, using the encoder portion of the neural network system, the third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; and generating, using the encoder portion of the neural network system, the predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame. 
     Aspect 13: The method of any of Aspects 1 to 12, further comprising: generating, using the encoder portion of the neural network system, the residual based on the predicted frame and the frame; generating, using the encoder portion of the neural network system, an encoded bitstream comprising the residual; and transmitting, using the encoder portion of the neural network system, the encoded bitstream to the decoder portion of the neural network system. 
     Aspect 14: The method of any of Aspects 1 to 13, further comprising: determining, using the encoder portion of the neural network system, contextual information extracted from image data associated with at least one of the frame, the first reference frame and the second reference frame, wherein the encoded bitstream further comprises the contextual information. 
     Aspect 15: The method of any of Aspects 1 to 14, further comprising: training, for one or more training iterations, the neural network system using a loss function, the loss function determining a loss between one or more reconstructed B-frames and one or more frames represented by the one or more reconstructed B-frames. 
     Aspect 16: An apparatus comprising memory and one or more processors coupled to the memory and configured to: receive, from an encoder portion of a neural network system, a residual associated with a frame of a current processing time step; determine first motion information for a first reference frame associated with a first processing time step and second motion information for a second reference frame associated with a second processing time step, wherein the current processing time step is after the first processing time step and before the second processing time step; determine, using a decoder portion of the neural network system, third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; generate, using the decoder portion of the neural network system, a predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame; and generate, using the predicted frame and the residual, a reconstructed bidirectional predicted frame (B-frame) for the current processing time step, the reconstructed B-frame representing the frame. 
     Aspect 17: The apparatus of Aspect 16, wherein the first motion information comprises a first two-dimensional (2D) optical flow map calculated from the current processing time step to the first reference frame, wherein the second motion information comprises a second 2D optical flow map calculated from the current processing time step to the second reference frame, and wherein the third motion information comprises a three-dimensional (3D) optical flow map generated based on the first 2D optical flow map and the second 2D optical flow map. 
     Aspect 18: The apparatus of Aspect 17, wherein the first 2D optical flow map and the second 2D optical flow map comprise spatial dimensions indicating vertical and horizontal displacements, and wherein the 3D optical flow map comprises spatial dimensions indicating estimated vertical and horizontal displacements and a frame dimension mapping each of the estimated vertical and horizontal displacements in the spatial dimensions to a corresponding one of the first reference frame and the second reference frame. 
     Aspect 19: The apparatus of any of Aspects 17 to 18, wherein the one or more processors are configured to: determine, using the decoder portion of the neural network system, forward motion information from the first reference frame to the second reference frame, and backward motion information from the second reference frame to the first reference frame; determine, using the decoder portion of the neural network system, the first 2D optical flow map based on the forward motion information and a value of an interval of the first processing time step and the second processing time step, the value of the interval corresponding to the current processing time step; and determine, using the decoder portion of the neural network system, the second 2D optical flow map based on the backward motion information and the value of the interval. 
     Aspect 20: The apparatus of any of Aspects 17 to 19, wherein the one or more processors are configured to: warp, using the 3D optical flow map, the first reference frame and the second reference frame to the current processing time step, wherein the predicted frame is generated based on the 3D optical flow map, the warped first reference frame and the warped second reference frame. 
     Aspect 21: The apparatus of any of Aspects 17 to 20, wherein the one or more processors are configured to: adjust one or more motion values in at least one of the first 2D optical flow map and the second 2D optical flow map based on a detected non-linearity in the one or more motion values; and after adjusting the one or more values in at least one of the first 2D optical flow map and the second 2D optical flow map, merge the first 2D optical flow map and the second 2D optical flow map into the 3D optical flow map. 
     Aspect 22: The apparatus of any of Aspects 17 to 21, wherein the one or more processors are configured to: receive, from the encoder portion of the neural network system, an encoded bitstream comprising the residual and contextual information extracted from image data associated with at least one of the frame, the first reference frame and the second reference frame; decode, using the decoder portion of the neural network system, the encoded bitstream; and adjust the one or more values in at least one of the first 2D optical flow map and the second 2D optical flow map, the one or more values being adjusted further based on the contextual information, the contextual information comprising at least one of an occlusion in the image data, one or more edges in the image data, and one or more features in the image data. 
     Aspect 23: The apparatus of any of Aspects 17 to 22, wherein the one or more processors are configured to: warp, using the first 2D optical flow map, the first reference frame to the current processing time step; and warp, using the second 2D optical flow map, the second reference frame to the current processing time step, wherein the 3D optical flow map is generated based on at least one of the first reference frame, the second reference frame, the warped first reference frame, the warped second reference frame, the first 2D optical flow map, and the second optical flow map. 
     Aspect 24: The apparatus of any of Aspects 16 to 23, wherein the first reference frame and the second reference frame comprise at least one of an intra-coded frame (I-frame) and an additional B-frame. 
     Aspect 25: The apparatus of any of Aspects 16 to 24, wherein the one or more processors are configured to: receive, from the encoder portion of the neural network system, an encoded bitstream comprising the residual; and decode, using the decoder portion of the neural network system, the encoded bitstream. 
     Aspect 26: The apparatus of any of Aspects 16 to 25, wherein the one or more processors are configured to: generate, using the decoder portion of the neural network system, the predicted frame and the reconstructed B-frame without the frame and without motion information from the encoder portion of the neural network system. 
     Aspect 27: The apparatus of any of Aspects 16 to 26, wherein the one or more processors are configured to: determine, using the encoder portion of the neural network system, the first motion information for the first reference frame and the second motion information for the second reference frame; determine, using the encoder portion of the neural network system, the third motion information for the frame based on the first motion information of the first reference frame and the second motion information of the second reference frame; and generate, using the encoder portion of the neural network system, the predicted frame based on the third motion information of the frame, the first reference frame and the second reference frame. 
     Aspect 28: The apparatus of any of Aspects 16 to 27, wherein the one or more processors are configured to: generate, using the encoder portion of the neural network system, the residual based on the predicted frame and the frame; generate, using the encoder portion of the neural network system, an encoded bitstream comprising the residual; and transmit, using the encoder portion of the neural network system, the encoded bitstream to the decoder portion of the neural network system. 
     Aspect 29: The apparatus of any of Aspects 16 to 28, wherein the one or more processors are configured to: determine, using the encoder portion of the neural network system, contextual information extracted from image data associated with at least one of the frame, the first reference frame and the second reference frame, wherein the encoded bitstream further comprises the contextual information. 
     Aspect 30: The apparatus of any of Aspects 16 to 29, wherein the one or more processors are configured to: train, for one or more training iterations, the neural network system using a loss function, the loss function determining a loss between one or more reconstructed B-frames and one or more frames represented by the one or more reconstructed B-frames. 
     Aspect 31: The apparatus of any of Aspects 16 to 30, wherein the apparatus comprises a mobile device. 
     Aspect 32: The apparatus of any of Aspects 16 to 31, wherein the apparatus comprises an extended reality device. 
     Aspect 33: The apparatus of any of Aspects 16 to 32, wherein the apparatus comprises a camera configured to capture one or more video frames. 
     Aspect 34: The apparatus of any of Aspects 16 to 33, wherein the apparatus comprises a display configured to display one or more video frames. 
     Aspect 35: A non-transitory computer-readable medium having stored thereon instructions which, when executed by one or more processors, cause the one or more processors to perform operations according to any of Aspects 1 to 34. 
     Aspect 36: An apparatus comprising means for performing operations according to any of Aspects 1 to 34.