Patent Publication Number: US-2022222505-A1

Title: Multi-task neural network by micro-structured parameter sharing for multi-quality loop filter

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based on and claims priority to U.S. Provisional Patent Application No. 63/135,994, filed on Jan. 11, 2021, the disclosure of which is incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     Traditional video coding standards, such as the H.264/Advanced Video Coding (H.264/AVC), High-Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) share a similar (recursive) block-based hybrid prediction/transform framework where individual coding tools like the intra/inter prediction, integer transforms, and context-adaptive entropy coding, are intensively handcrafted to optimize the overall efficiency. The spatiotemporal pixel neighborhoods are leveraged for predictive signal construction, to obtain corresponding residuals for subsequent transform, quantization, and entropy coding. On the other hand, the nature of Neural Networks (NN) is to extract different levels of spatiotemporal stimuli by analyzing spatiotemporal information from the receptive field of neighboring pixels. The capability of exploring high nonlinearity and nonlocal spatiotemporal correlations provide promising opportunity for largely improved compression quality. 
     In lossy video compression, the compressed video inevitably suffers from compression artifacts, which severely degrade the Quality of Experience (QoE). The amount of distortion tolerated in practice depends on the applications, and in general, the higher the compression ratio, the larger the distortion. There are many ways to control the compression quality. For example, the Quantization Parameter (QP) determines the quantization step size. The larger the QP value, the larger the quantization step size, and the larger the distortion. To accommodate different requests of users, the video coding method needs the ability to compress videos with different compression qualities. For example, VVC allows the QP value to vary between 0 and 63. 
     SUMMARY 
     According to embodiments, a method of multi-quality loop filter video compression using a masked multi-task neural network, based on micro-structured parameter sharing is performed by at least one processor and includes generating a first set of masked weight parameters, based on an input and a plurality of quantization parameter values with a first set of masks and first plurality of weight parameters, for a first set of layers in a neural network, selecting a second set of layers of the neural network for the plurality of quantization parameter values with a second plurality of weight parameters, based on the plurality of quantization parameter values, and computing and outputting a inference output, based on the first set of masked weight parameters and the second plurality of weight parameters. 
     According to embodiments, an apparatus of multi-quality loop filter video compression using a masked multi-task neural network based on micro-structured parameter sharing includes at least one memory configured to store program code, and at least one processor configured to read the program code and operate as instructed by the program code. The program code includes generating code configured to cause the at least one processor to generate a first set of masked weight parameters, based on an input and a plurality of quantization parameter values with a first set of masks and first plurality of weight parameters, for a first set of layers of a neural network, first selecting code configured to cause the at least one processor to select a second set of layers of the neural network for the plurality of quantization parameter values with a second plurality of weight parameters, based on the plurality of quantization parameter values, computing code configured to cause the at least one processor to compute a inference output, based on the first set of masked weight parameters and the second plurality of weight parameters; and outputting code configured to cause at least one processor to output the computed inference output. 
     According to embodiments, a non-transitory computer-readable medium storing instructions that, when executed by at least one processor for processing a video with a multi-quality loop filter using a multi-task neural network, cause the at least one processor to generate a first set of masked weight parameters, based on an input and a plurality of quantization parameter values with a first set of masks and first plurality of weight parameters, for a first set of layers of a neural network, select a second set of layers of the neural network for the plurality of quantization parameter values with a second plurality of weight parameters, based on the plurality of quantization parameter values, compute a inference output, based on the first set of masked weight parameters and the second plurality of weight parameters, and output the computed inference output. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram of an environment in which methods, apparatuses and systems described herein may be implemented, according to embodiments. 
         FIG. 2  is a block diagram of example components of one or more devices of  FIG. 1 . 
         FIG. 3  is a block diagram of an inference of a Multi-Task Neural Network (MTNN) for a Multi-Quality Loop Filter (MQLF) method of video compression using micro-structured masks, during a test stage, according to embodiments. 
         FIG. 4  is a block diagram of a training apparatus for multi-task neural network video compression using micro-structured masks, during a training stage, according to embodiments. 
         FIG. 5  is a detailed block diagram of the Weight Filling module from the training apparatus in  FIG. 4 , during a training stage, according to embodiments. 
         FIG. 6  is a detailed block diagram of the Micro-Structured Pruning module from the training apparatus in  FIG. 4 , during a training stage, according to embodiments. 
         FIG. 7  is a flowchart of a method for video compression with a MQLF using a MTNN, with shared multi-task layers, task-specific layers and micro-structured masks, according to embodiments. 
         FIG. 8  is a block diagram of an apparatus for video compression with a MQLF using a MTNN, with shared multi-task layers, task-specific layers and micro-structured masks, according to embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Deep Neural Network (DNN) based methods have been developed to successfully enhance the visual quality of compressed video, such as: video denoising, super-resolution, deblurring, deblocking, etc. However, it has been a challenging issue for NN-based quality enhancement methods to accommodate multiple QPs. Traditionally, each QP value is treated as an individual task and one NN model instance is trained and deployed for each QP value. It is important to decrease the storage and computation costs caused by multiple QP values. 
     Given a set of p QP values qp 1 , . . . qp p , one solution is to treat all p values as one set without distinguishing them, and therefore train one NN model instance for all p values by combining training data of different QP values. An improved solution is to use the QP value qp i  as additional input of the NN model and train one NN model instance with the combined training data of different QP values. By using the QP values as inputs, one hopes that the NN model instance automatically learns to organize its parameters to implicitly model distribution of sub-groups of data samples, one corresponding to each QP value. 
     Embodiments may relate to a Multi-Quality Loop Filter (MQLF) mechanism by using a Multi-Task Neural Network (MTNN) based on micro-structured parameter sharing. One MTNN model instance is used to accommodate multiple QP values, where a binary Micro-Structured Mask (MSM) is associated with each QP value to explicitly guide the inference computation of the MTNN for that QP value. 
     Accordingly, embodiments of the present disclosure may describes a method and an apparatus using the Multi-Quality Loop Filter (MQLF) mechanism for processing a decoded video to reduce one or more types of artifacts such as noises, blur, and blocky effects. The Multi-Task Neural Network (MTNN) based on micro-structured parameter sharing is proposed, where one MTNN model instance is used to accommodate multiple Quantization Parameter (QP) values, with one Micro-Structured Mask (MSM) assigned to each QP value to explicitly guide the inference computation of the MTNN for that QP value. 
       FIG. 1  is a diagram of an environment  100  in which methods, apparatuses and systems described herein may be implemented, according to embodiments. 
     As shown in  FIG. 1 , the environment  100  may include a user device  110 , a platform  120 , and a network  130 . Devices of the environment  100  may interconnect via wired connections, wireless connections, or a combination of wired and wireless connections. 
     The user device  110  includes one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with platform  120 . For example, the user device  110  may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smart phone, a radiotelephone, etc.), a wearable device (e.g., a pair of smart glasses or a smart watch), or a similar device. In some implementations, the user device  110  may receive information from and/or transmit information to the platform  120 . 
     The platform  120  includes one or more devices as described elsewhere herein. In some implementations, the platform  120  may include a cloud server or a group of cloud servers. In some implementations, the platform  120  may be designed to be modular such that software components may be swapped in or out. As such, the platform  120  may be easily and/or quickly reconfigured for different uses. 
     In some implementations, as shown, the platform  120  may be hosted in a cloud computing environment  122 . Notably, while implementations described herein describe the platform  120  as being hosted in the cloud computing environment  122 , in some implementations, the platform  120  may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based. 
     The cloud computing environment  122  includes an environment that hosts the platform  120 . The cloud computing environment  122  may provide computation, software, data access, storage, etc. services that do not require end-user (e.g., the user device  110 ) knowledge of a physical location and configuration of system(s) and/or device(s) that hosts the platform  120 . As shown, the cloud computing environment  122  may include a group of computing resources  124  (referred to collectively as “computing resources  124 ” and individually as “computing resource  124 ”). 
     The computing resource  124  includes one or more personal computers, workstation computers, server devices, or other types of computation and/or communication devices. In some implementations, the computing resource  124  may host the platform  120 . The cloud resources may include compute instances executing in the computing resource  124 , storage devices provided in the computing resource  124 , data transfer devices provided by the computing resource  124 , etc. In some implementations, the computing resource  124  may communicate with other computing resources  124  via wired connections, wireless connections, or a combination of wired and wireless connections. 
     As further shown in  FIG. 1 , the computing resource  124  includes a group of cloud resources, such as one or more applications (“APPs”)  124 - 1 , one or more virtual machines (“VMs”)  124 - 2 , virtualized storage (“VSs”)  124 - 3 , one or more hypervisors (“HYPs”)  124 - 4 , or the like. 
     The application  124 - 1  includes one or more software applications that may be provided to or accessed by the user device  110  and/or the platform  120 . The application  124 - 1  may eliminate a need to install and execute the software applications on the user device  110 . For example, the application  124 - 1  may include software associated with the platform  120  and/or any other software capable of being provided via the cloud computing environment  122 . In some implementations, one application  124 - 1  may send/receive information to/from one or more other applications  124 - 1 , via the virtual machine  124 - 2 . 
     The virtual machine  124 - 2  includes a software implementation of a machine (e.g., a computer) that executes programs like a physical machine. The virtual machine  124 - 2  may be either a system virtual machine or a process virtual machine, depending upon use and degree of correspondence to any real machine by the virtual machine  124 - 2 . A system virtual machine may provide a complete system platform that supports execution of a complete operating system (“OS”). A process virtual machine may execute a single program, and may support a single process. In some implementations, the virtual machine  124 - 2  may execute on behalf of a user (e.g., the user device  110 ), and may manage infrastructure of the cloud computing environment  122 , such as data management, synchronization, or long-duration data transfers. 
     The virtualized storage  124 - 3  includes one or more storage systems and/or one or more devices that use virtualization techniques within the storage systems or devices of the computing resource  124 . In some implementations, within the context of a storage system, types of virtualizations may include block virtualization and file virtualization. Block virtualization may refer to abstraction (or separation) of logical storage from physical storage so that the storage system may be accessed without regard to physical storage or heterogeneous structure. The separation may permit administrators of the storage system flexibility in how the administrators manage storage for end users. File virtualization may eliminate dependencies between data accessed at a file level and a location where files are physically stored. This may enable optimization of storage use, server consolidation, and/or performance of non-disruptive file migrations. 
     The hypervisor  124 - 4  may provide hardware virtualization techniques that allow multiple operating systems (e.g., “guest operating systems”) to execute concurrently on a host computer, such as the computing resource  124 . The hypervisor  124 - 4  may present a virtual operating platform to the guest operating systems, and may manage the execution of the guest operating systems. Multiple instances of a variety of operating systems may share virtualized hardware resources. 
     The network  130  includes one or more wired and/or wireless networks. For example, the network  130  may include a cellular network (e.g., a fifth generation (5G) network, a long-term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., the Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, or the like, and/or a combination of these or other types of networks. 
     The number and arrangement of devices and networks shown in  FIG. 1  are provided as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown in  FIG. 1 . Furthermore, two or more devices shown in  FIG. 1  may be implemented within a single device, or a single device shown in  FIG. 1  may be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of the environment  100  may perform one or more functions described as being performed by another set of devices of the environment  100 . 
       FIG. 2  is a block diagram of example components of one or more devices of  FIG. 1 . 
     A device  200  may correspond to the user device  110  and/or the platform  120 . As shown in  FIG. 2 , the device  200  may include a bus  210 , a processor  220 , a memory  230 , a storage component  240 , an input component  250 , an output component  260 , and a communication interface  270 . 
     The bus  210  includes a component that permits communication among the components of the device  200 . The processor  220  is implemented in hardware, firmware, or a combination of hardware and software. The processor  220  is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, the processor  220  includes one or more processors capable of being programmed to perform a function. The memory  230  includes a random access memory (RAM), a read only memory (ROM), and/or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and/or an optical memory) that stores information and/or instructions for use by the processor  220 . 
     The storage component  240  stores information and/or software related to the operation and use of the device  200 . For example, the storage component  240  may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, and/or a solid state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of non-transitory computer-readable medium, along with a corresponding drive. 
     The input component  250  includes a component that permits the device  200  to receive information, such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and/or a microphone). Additionally, or alternatively, the input component  250  may include a sensor for sensing information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, and/or an actuator). The output component  260  includes a component that provides output information from the device  200  (e.g., a display, a speaker, and/or one or more light-emitting diodes (LEDs)). 
     The communication interface  270  includes a transceiver-like component (e.g., a transceiver and/or a separate receiver and transmitter) that enables the device  200  to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. The communication interface  270  may permit the device  200  to receive information from another device and/or provide information to another device. For example, the communication interface  270  may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, or the like. 
     The device  200  may perform one or more processes described herein. The device  200  may perform these processes in response to the processor  220  executing software instructions stored by a non-transitory computer-readable medium, such as the memory  230  and/or the storage component  240 . A computer-readable medium is defined herein as a non-transitory memory device. A memory device includes memory space within a single physical storage device or memory space spread across multiple physical storage devices. 
     Software instructions may be read into the memory  230  and/or the storage component  240  from another computer-readable medium or from another device via the communication interface  270 . When executed, software instructions stored in the memory  230  and/or the storage component  240  may cause the processor  220  to perform one or more processes described herein. Additionally, or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software. 
     The number and arrangement of components shown in  FIG. 2  are provided as an example. In practice, the device  200  may include additional components, fewer components, different components, or differently arranged components than those shown in  FIG. 2 . Additionally, or alternatively, a set of components (e.g., one or more components) of the device  200  may perform one or more functions described as being performed by another set of components of the device  200 . 
     A method and an apparatus for multi-quality loop filter video compression using a masked multi-task neural network based on micro-structured parameter sharing will now be described in detail. 
     A typical video compression framework can be described as follows. Assume an input video x comprises of a plurality of image frames x 1 , . . . , x T . In the first motion estimation step, the frames are partitioned into spatial blocks, each block can be partitioned into smaller blocks iteratively, and a set of motion vectors mt between a current frame x t  and a set of previous reconstructed frames {{circumflex over (x)} j } t−1  is computed for each block. Note that the subscript t denotes the current t-th encoding cycle, which may not match the time stamp of the image frames. Also, previous reconstructed frames {{circumflex over (x)} j } t−1  contain frames from multiple previous encoding cycles. Then, in the second motion compensation step, a predicted frame {tilde over (x)} t  is obtained by copying the corresponding pixels of the previous reconstructed frames {{circumflex over (x)} j } t−1  based on the motion vectors m t , and a residual r t  between the original frame x t  and the predicted frame {tilde over (x)} t . In the third step, the residual r t  is quantized (typically after a transformation like DCT where the DCT coefficients of r t  are quantized to achieve better quantization performance). The quantization step results in a quantized frame ŷ t . Both the motion vectors mt and the quantized frame ŷ t  are encoded into bit steams by entropy coding, which are sent to decoders. Then on the decoder side, the quantized frame ŷ t  is dequantized (typically through inverse transformation like IDCT with the dequantized coefficients) to obtain a recovered residual {circumflex over (r)} t . Then, the recovered residual {circumflex over (r)} t  is added back to the predicted frame {tilde over (x)} t  to obtain the reconstructed frame 
     Additional components are further used to improve the visual quality of the reconstructed frame {circumflex over (x)} t . One or multiple of the following enhancement modules may be selected to process the reconstructed frame {circumflex over (x)} t , including: a Deblocking Filter (DF), a Sample-Adaptive Offset (SAO), an Adaptive Loop Filter (ALF), a Cross-Component Adaptive Loop Filter (CCALF), etc. The processing order of the selected modules is not limited to this and can change. 
     Embodiments of this disclosure propose a MQLF for enhancing the visual quality of the reconstructed frame {circumflex over (x)} t . The target is to reduce artifacts such as noises, blur, blocky effects in reconstructed frame {circumflex over (x)} t , resulting in a high-quality frame Specifically, an MTNN is used to compute the high-quality frame {circumflex over (x)} t   h , where one model instance can accommodate multiple QP values. A binary MSM is used for each QP value to guide the inference computation of the MTNN for that QP value. 
     The proposed MQLF can be used in combination with one or multiple of the previously mentioned additional components (i.e., DF, SAO, ALF, CCALF, etc.) to improve the visual quality of the reconstructed frame {circumflex over (x)} t . For example, the reconstructed frame {circumflex over (x)} t  can go through DF first, followed by the MQLF, further followed by SAO and ALF. The order of how these components are used can change arbitrarily. In one example embodiment, the proposed method can also be used alone as a replacement of all the other components to enhance the visual quality of the reconstructed frame {circumflex over (x)} t . 
     The MQLF may be used for both Slice/CU level on each of the frames, in combination with one or multiple of the above mentioned additional components (e.g., DF, SAO, ALF, CCALF, etc.). In other words, the reconstructed frame {circumflex over (x)} t  can be one entire frame, such as the I frame if applied with Slice switch On, indicating the hierarchical level 0 frames. Also, the reconstructed frame {circumflex over (x)} t  can be a block in a frame, e.g., a CTU if applied with the CTU switch On, indicating lower level frames of the hierarchical structure. There are no restrictions on the specific video compression methods (e.g., HEVC, VVC) that compute the reconstructed frame {circumflex over (x)} t . 
       FIG. 3  is a block diagram of an inference of a Multi-Task Neural Network (MTNN) by micro-structured parameter sharing for a Multi-Quality Loop Filter (MQLF) apparatus  300 , during a test stage, according to embodiments. 
     As shown in  FIG. 3 , the test apparatus  300  includes a MTNN Inference module  310 . 
     Given an input {circumflex over (x)} t  of size (h, w, c), where h, w, c are the height, width, and number of channels, respectively, a test stage of the inference workflow of the MQLF can be described as follows. 
     The MQLF uses an MTNN that may be separated into two parts: a set of Shared Multi-Task Layers (SMTL) and a set of Task-Specific Layers (TSL). The model parameters of the SMTL are shared across (i.e., the same for) different QP values. The model parameters of the TSL are for each QP value individually. Assume that there are p QP values qp 1 , . . . , qp p  ranked in an order, preferably in a descending order corresponding to reconstruction qualities of the input frame {circumflex over (x)} t  from low to high). Let {W j   SMTL } denote a set of weight parameters of the SMTL, where each W j   SMTL  is the weight parameters of the j-th layer. Let {M ij   SMTL } denote a set of binary masks corresponding to the QP value qp i , where each binary mask M ij   SMTL  has the same shape as the weight parameter W j   SMTL  of the j-th layer. Each entry of the binary mask M ij   SMTL  is 1 or 0 indicating whether or not the corresponding weight entry in the weight parameter W j   SMTL  participates in the inference computation for the QP value qp i . Let {W j   TSL (qp i )} denote a set of weight parameters of the TSL corresponding to the QP value qp i , where W j   TSL (qp i ) is the weight parameters of the j-th layer. No restriction are put in on the type of layers (e.g., convolution, normalization, fully connected, etc.) and the position of the layers in SMTL and TSL. For example, layers of STML and TSL can be interlaced to form the MTNN. 
     Each of the weight parameters W j   SMTL  or W j   TSL (qp i ) are a general 5-Dimension (5D) tensor with size (c 1 , k 1 , k 2 , k 3 , c 2 ). The input of the corresponding layer is a 4-Dimension (4D) tensor A of size (h 1 ,w 1 ,d 1 ,c 1 ), and the output is a 4D tensor B of size (h 2 ,w 2 ,d 2 ,c 2 ). The sizes c 1 , k 1 , k 2 , k 3 , c 2 , h 1 , w 1 , d 1 , h 2 , w 2 , d 2  are integer numbers greater or equal to 1. When any of the sizes c 1 , k 1 , k 2 , k 3 , c 2 , h 1 , w 1 , d 1 , h 2 , w 2 , d 2  are equal to 1, the corresponding tensor reduces to a lower dimension. Each item in each tensor is a floating number. The parameters h 1 , w 1  and d 1  are the height, weight and depth of the input A. Similarly, the parameters h 2 , w 2  and d 2  are the height, weight and depth of the output B. The parameter c 1  is the number of input channels in the input A. Similarly, the parameter c 2  is the number of output channels in the output B. The parameters k 1 , k 2  and k 3  are the size of the convolution kernel corresponding to the height, weight, and depth axes, respectively. The output B is computed through the convolution operation ⊙ based on input A, weight parameters W j   SMTL  or W j   TSL (qp i ) and masks M ij   SMTL  if available. Note that for the weight parameters W j   TSL (qp i ), a mask M j   TSL (qp i ) may also be associated to it, and all entries of the masks M j   TSL (qp i ) are set to be 1. From this perspective, traditional methods of training an individual model instance for each individual QP value as an individual task can be seen as a special case of the proposed method in this disclosure, where the SMTL has no layers and masks while TSL has all the layers. 
     The output B may be obtained by convolving input A with the masked weights: 
       W ij   SMTL   ′=W   j   SMTL   ·M   ij   MSTL  or  W   j   TSL ′( s   i )= W   j   TSL ( s   i )· M   j   TSL ( s   i )= W   j   TSL ( qp   i )   (1)
 
     where · is element-wise multiplication. 
     Referring to  FIG. 3 , given the above learned weight parameters {W j   SMTL }, {W j   TSL (qp i )}, i=1, . . . , p, and masks {M ij   SMTL }, i=1, . . . , p, with the input {circumflex over (x)} t  and the associated target QP value qp i ({circumflex over (x)} t ), the corresponding mask {M ij   SMTL } is used to generate the masked weight parameters {W ij   SMTL ′} for the SMTL and the corresponding subnetwork of the TSL for the QP value qp i ({circumflex over (x)} t ) is selected with weight parameters W j   TSL (qp i ({circumflex over (x)} t )). Then, using the masked weight parameters {W ij   SMTL ′} for the SMTL inference and using the weight parameters W j   TSL (qp i ({circumflex over (x)} t )) for the TSL inference, the MTNN Inference module  310  computes the inference output {circumflex over (x)} t   h , which is the enhanced high-quality result. 
     The shape of each weight parameter W j   SMTL  can be changed, corresponding to the convolution of a reshaped input with the reshaped W j   SMTL  to obtain the same output. Similarly, the shape of each mask M ij   SMTL  can also be changed. In embodiments, the shape of each weight parameter may take two configurations. First, the 5D weight tensor is reshaped into a 3D tensor of size (c′ 1 , c′ 2 , k), where c′ 1 ×c′ 2 ×k=c 1 ×c 2 ×k 1 ×k 2 ×k 3 . For example, a configuration is c′ 1 =c 1 , c′ 2 =c 2 , k=k 1 ×k 2 ×k 3 . Second, the 5D weight tensor is reshaped into a 2D matrix of size (c′ 1 , c′ 2 ), where c′ 1 ×c′ 2 =c 1 ×c 2 ×k 1 ×k 2 ×k 3 . For example, some configurations include c′ 1 =c 1 , c′ 2 =c 2 ×k 1 ×k 2 ×k 3 , or c′ 2 =c 2 , c′ 1 =c 1 ×k 1 ×k 2 ×k 3 . 
     The desired micro-structure of the masks are designed to align with the underlying GEMM matrix multiplication process of how the convolution operation is implemented so that the inference computation of using the masked weight parameters can be accelerated. In embodiments, block-wise micro-structures are used for the masks (i.e., the masked weight parameters) of each layer in the 3D reshaped weight tensor or the 2D reshaped weight matrix. Specifically, for the case of reshaped 3D weight tensor, the blocks are partitioned into blocks of size (g′ 1 , g′ 2 , g k ). For the case of reshaped 2D weight matrix, the blocks are partitioned into blocks of size (g′ 1 , g′ 2 ). When any of the g′ 1 , g′ 2 , g k  takes size 1, the corresponding block reduces to a lower dimension. All items in a block of a mask have the same binary value, 1 or 0. That is, weight parameters are masked out in block-wise micro-structured fashion. 
     The training process of the NN model according to embodiments will now be described. An overall workflow of the proposed multi-stage training framework is shown in  FIG. 4 . The goal of the training stage is to learn the MTNN model instance with weight parameters {W j   SMTL } and {W j   TSL (qp i )} and the set of micro-structured masks {M ij   SMTL } for i=1, . . . , p, each mask {M ij   SMTL } and weight parameters {W j   TSL (qp i )} targeting at each QP value of the interest QP value qp i . A progressive multi-stage training framework may be used to achieve this goal. 
       FIG. 4  is a block diagram of a training apparatus  400  for multi-task neural network video compression by micro-structured masks, during a training stage, according to embodiments. 
     As shown in  FIG. 4 , the training apparatus  400  includes a Weight Filling module  410  and a Micro-Structured Pruning module  420 . 
     Assume that a current task is to train the masks targeting the QP value qp i , a current model instance having weights {W j   SMTL (i−1)}, and the corresponding masks {M i−1j   SMTL }. Also, the current QP value qp i  have their corresponding TSL with weight parameters {W j   TSL (qp i )} to learn. In other words, the goal is to obtain masks {M ij   SMTL } and the updated weight parameters {W j   SMTL (i)} and the new weight parameters {W j   TSL (qp i )}. 
     First, the weight parameters {W j   SMTL (i−1)} that are masked by masks {M i−1j   SMTL } are fixed. For Example, the weight parameters whose corresponding mask entry M i−1j   SMTL  is 1. Then, a learning process is conducted through the Weight Filling module  410  to fill up the remaining unfixed weights in {W j   SMTL (i−1)} for the SMTL and the weights {W j   TSL (qp i )} for the TSL. This results in a set of updated weight parameters {W j   SMTL ′(i)} and {W j   TSL (qp i )}. 
     Then, the Micro-Structured Pruning module  420  conducts micro-structured pruning, based on the set of updated weight parameters {W j   SMTL ′(i)}, masks {M i−1j   SMTL }, and {W j   TSL (s i )}, to obtain the pruned model instance and masks {W j   SMTL (i)}, {M ij   SMTL }, and {W j   TSL (qp i )}. 
       FIG. 5  is a workflow of the Weight Filling module  410  of  FIG. 4 , during a training stage, according to embodiments. 
     As shown in  FIG. 5 , the Weight Filling module  410  includes the MTNN Inference module  320 , a Weight Fixing and Filling module  510 , a Compute Loss module  520 , a Compute Additional Loss module  530 , and a Back-Propagation &amp; Weight Update module  540 . 
     Given the current weights {W j   SMTL (i−1)} and the corresponding masks {M i−1j   SMTL }, weight parameters in {W j   SMTL (i−1)} that are masked by {M i−1j   SMTL } are fixed in the Weight Fixing and Filling module  510 . Then, the remaining weight parameters in {W j   SMTL (i−1)} are reinitialized. For example, this may be done by setting them to some random initial values or using the corresponding weights of a previously learned full model such as the first full model with weights {W j   SMTL ′(0)}. This gives the weight parameters {W j   SMTL ′(i)} for the SMTL. The weight parameters {W j   TSL (qp i )} of the TSL are also initialized (e.g., by setting them to some random initial values or using the corresponding weights of some previously learned full model such as an individual full model trained for the current QP value qp i ). After that, the training input {circumflex over (x)} t  is passed through the MTNN to compute the output {circumflex over (x)} t   h  in the MTNN Inference module  320  using weight parameter {W j   SMTL ′(i)} for the STML inference and the weight parameter {W j   TSL (qp i )} for the TSL inference. 
     For training purposes, each training input {circumflex over (x)} t  has a corresponding ground-truth x t   h (qp i ) for the QP value qp i . For example, the training input {circumflex over (x)} t  is reconstructed from the compressed version of the ground-truth x t   h (qp i ) with a video compression method using the QP value qp i . The general goal of training is to minimize the distortion between the ground-truth x t   h (qp j ) and the estimated output {circumflex over (x)} t   h . The Compute Loss module  520  computes a distortion loss L(x t   h (qp i ), {circumflex over (x)} t   h ) to measure the distortion, such as the MSE or SSIM, between the ground-truth x t   h (qp i ) and the estimated output {circumflex over (x)} t   h . In some example embodiments, the Compute Additional Loss module  530  may compute other losses to help regularize the training process. For example, a perceptual loss can be used, where by passing either the ground-truth x t   h (qp i ) or estimated output {circumflex over (x)} t   h  as inputs into a feature extraction NN like VGG, a feature map can be computed, and the difference between the feature maps computed by ground-truth x t   h (qp i ) and the estimated output {circumflex over (x)} t   h  are measured, weighted and combined with the distortion loss L(x t   h (qp i ), {circumflex over (x)} t   h ). Also, an adversarial loss may be used, where a discriminator tries to classify whether the ground-truth x t   h (qp i ) or the estimated output {circumflex over (x)} t   h  is an original input or a generated input by the MTNN. The classification loss can be used as the adversarial loss, weighted and combined with the distortion loss L(x t   h (qp i ), {circumflex over (x)} t   h ). The Back-Propagation &amp; Weight Update module  540  computes the gradient of this combined loss (or L(x t   h (qp i ), {circumflex over (x)} t   h ) if no other loss is used) and updates the unfixed weight parameters {W j   SMTL ′(i)} of the SMTL and the weight parameters {W j   TSL (qp i )} of the TSL. Multiple iterations may be taken in this Back-Propagation &amp; Weight Update module  540  (e.g., until reaching a maximum iteration number or until the loss converges). 
       FIG. 6  is a detailed workflow of the Micro-Structured Pruning module  420  of  FIG. 4 , during a training stage, according to embodiments. 
     As shown in  FIG. 6 , the Micro-Structured Pruning module  420  includes the MTNN Inference module  320 , the Compute Loss module  520 , the Compute Additional Loss module  530 , the Back-Propagation &amp; Weight Update module  540 , and a Compute Pruning Mask module  610 . 
     First, given the updated weights {W j   SMTL ′(i)} of the SMTL and the weights {W j   TSL (qp i )} of the TSL from the Weight Filling module  410 , as well as the current masks {M i−1j   SMTL }, the Compute Pruning Mask module  610  computes the pruning masks. In detail, the updated weight parameters {W j   SMTL ′(i)} that are masked by {M i−1j   SMTL }, and for the remaining unfixed weight parameters in {W j   SMTL ′(i)}, are fixed and a pruning loss L p (b) (e.g., the L 1  or L 2  norm of the weights in the block) is computed for each micro-structured block b as mentioned before. The Compute Pruning Mask module  610  ranks the micro-structured blocks based on their pruning loss L p (b) in ascending order and the blocks are pruned (i.e., by setting the corresponding weights in the pruned blocks as 0) top down from the ranked list until a stop criterion is reached. For example, given a validation dataset S val , the MTNN model with weights {W j   SMTL ′(i)} and {W j   TSL (qp i )} generates a distortion loss L val  as: 
         L   val =Σ x     t       h     (qp     i     )∈S     val     L ( x   t   h ( qp   i ), {circumflex over (x)}   t   h )   (2)
 
     As more and more micro-blocks are pruned, this distortion loss L val  gradually increases. The stop criterion can be a tolerable percentage threshold that allows the distortion loss L val  to increase. The stop criterion can also be a simple percentage (e.g., 50%) of the micro-structured blocks to be pruned. A set of binary pruning masks {P ij   SMTL } can be generated, where an entry in a pruning mask P ij   SMTL  is 1 means the corresponding weight parameter in W j   SMTL ′(i) is pruned. Then, in the Back-Propagation &amp; Weight Update module  540 , the additional unfixed weights in the weights W j   SMTL ′(i) that are masked by masks {P ij   SMTL } as being pruned are fixed, and the remaining weights W j   SMTL ′(i) that are not masked by either pruning masks {P ij   SMTL } or {M i−1j   SMTL } are updated, and the weights {W j   TSL (qp i )} are updated, by regular back-propagation to optimize the combined loss (or the distortion loss L(x t   h (qp i ),{circumflex over (x)} t   h ) if no other loss are used) over the training data. Multiple iterations may be taken, e.g., until reaching a maximum number of iterations or until the loss converges. 
     The corresponding masks {M ij   SMTL } can be computed as: 
         M   ij   SMTL   =M   i−1j   SMTL ∪   P   lj   SMTL     (3)
 
     That is, the non-pruned entries in P ij   SMTL  that are not masked in M i−1j   SMTL  are additionally set to 1 as being masked in M ij   SMTL . Also, the above micro-structured weight pruning process outputs the updated weights {W j   SMTL (i)} and {W j   TSL (qp i )}. Note that the above micro-structured pruning process can also be, optionally, applied to weights {W j   TSL (qp i )} to further reduce the model size and inference computation. That is, the Compute Pruning Mask module  610  can also reshape and partition weights of the TSL into micro-structures, compute the pruning loss of those micro-structures, and prune top ranked micro-structures with small pruning loss. It may also optionally choose to do so to balance the MTNN reconstruction quality and storage and computation. 
     Finally, the last updated weights {W j   SMTL (N)} are the final output weights {W j   SMTL } for the SMTL of the learned MTNN model instance for the MQLF. 
       FIG. 7  is a flowchart of a method  700  for video compression with a MQLF using a MTNN, with shared multi-task layers, task-specific layers, and micro-structured masks, according to embodiments. 
     In some implementations, one or more process blocks of  FIG. 7  may be performed by the platform  120 . In some implementations, one or more process blocks of  FIG. 7  may be performed by another device or a group of devices separate from or including the platform  120 , such as the user device  110 . 
     As shown in  FIG. 7 , in operation  710 , the method  700  includes generating a first set of masked weight parameters, based on an input and a plurality of quantization parameter values with a first set of masks and first plurality of weight parameters, for a first set of layers of a neural network. Each mask in the first set of masks may be a binary value indicating if a corresponding one of the first plurality of weight parameters is used to compute the inference output. Additionally, the first set of masked weight parameters may be obtained by performing convolution of the first plurality of weight parameters and the first set of masks. 
     In operation  720 , the method  700  includes selecting a second set of layers of the neural network for the plurality of quantization parameter values with a second plurality of weight parameters, based on the plurality of quantization parameter values. A second set of masks corresponding to the second plurality of weight parameters may also be selected. 
     In operation  730 , the method  700  includes computing an inference output, based on the first set of masked weight parameters and the second plurality of weight parameters 
     In operation  740 , the method  700  includes outputting the computed inference output. 
     Although  FIG. 7  shows example blocks of the method, in some implementations, the method may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in  FIG. 7 . Additionally, or alternatively, two or more of the blocks of the method may be performed in parallel. 
     For example, operations of the method  700  may include splitting a neural network into the first set of layers of a neural network and the second set of layers of the neural network. The model parameters of the first set of layers of the neural network may be shared layers that are shared across different quantization parameter values with a corresponding one of the first plurality of weight parameters for each of the shared layers. The model parameters of the second set of layers of the neural network may be task specific layers that are different for each of the quantization parameter values with a corresponding one of the second plurality of weight parameters for each of the task specific layers. 
     The neural network is trained by updating one or more of the first plurality of weight parameters that are not respectively masked by the first set of masks, to minimize a distortion loss that is determined based on the input, the inference output and a respective quantization parameter value. Further, the neural network is trained by pruning the updated one or more of the first plurality of weight parameters not respectively masked by the first set of masks to obtain binary pruning masks indicating which of the first plurality of weight parameters are pruned, and updating at least one of the first plurality of weight parameters that are not respectively masked by the first set of masks and the obtained binary pruning masks, to minimize the distortion loss. 
       FIG. 8  is a block diagram of an apparatus  800  for video compression with a MQLF using a MTNN, with shared multi-task layers, task-specific layers and micro-structured masks, according to embodiments. 
     As shown in  FIG. 8 , the apparatus  800  includes generating code  810 , first selecting code  820 , and computing code  830 . 
     The generating code  810  configured to cause the at least one processor to generate a first set of masked weight parameters, based on an input and a plurality of quantization parameter values with a first set of masks and first plurality of weight parameters, for a first set of layers of the neural network. Each mask in the first set of masks may be a binary value indicating if a corresponding one of the first plurality of weight parameters is used to compute the inference output. 
     The first selecting code  820  configured to cause the at least one processor to select a second set of layers of the neural network for the plurality of quantization parameter values with a second plurality of weight parameters, based on the plurality of quantization parameter values. 
     The computing code  830  configured to cause the at least one processor to compute an inference output, based on the first set of masked weight parameters and the second plurality of weight parameters. 
     The outputting code  840  configured to cause the at least one processor to output the computed inference output. 
     Although  FIG. 8  shows example blocks of the apparatus, in some implementations, the apparatus may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in  FIG. 8 . Additionally, or alternatively, two or more of the blocks of the apparatus may be combined. 
     For example, the apparatus  800  may include splitting code configured to cause the at least one processor to split a neural network into the first set of layers of the neural network and the second set of layers of the neural network, wherein model parameters of the first set of layers of the neural network are shared layers that are shared across different quantization parameter values with a corresponding one of the first plurality of weight parameters for each of the shared layers, and model parameters of the second set of layers of the neural network are task specific layers that are different for each of the quantization parameter values with a corresponding one of the second plurality of weight parameters for each of the task specific layers. 
     The neural network may be trained by updating one or more of the first plurality of weight parameters that are not respectively masked by the first set of masks, to minimize a distortion loss that is determined based on the input, the inference output and a respective quantization parameter value, pruning the updated one or more of the first plurality of weight parameters not respectively masked by the first set of masks to obtain binary pruning masks indicating which of the first plurality of weight parameters are pruned, and updating at least one of the first plurality of weight parameters that are not respectively masked by the first set of masks and the obtained binary pruning masks, to minimize the distortion loss. 
     Further, apparatus  800  may include second selecting code configured to cause the at least one processor to select a second set of masks corresponding to the second plurality of weight parameters and performing code configured to cause the at least one processor to perform convolution of the first plurality of weight parameters and the first set of masks to obtain the first set of masked weight parameters. 
     Compared with the traditional loop filtering or NN-based loop filtering methods, embodiments describe using one MTNN model instance to accommodate the compression of multiple QP values by using multiple binary masks. The block-wise micro-structured masks also preserve the compression performance of individual QP values and can reduce inference computation. This method largely reduces deployment storage for compression using multiple QP values. It also provides a flexible and general framework that accommodates various types of underlying NNLF methods and model architectures and various shaped micro-structures. 
     The proposed methods may be used separately or combined in any order. Further, each of the methods (or embodiments) may be implemented by processing circuitry (e.g., one or more processors or one or more integrated circuits). In one example, the one or more processors execute a program that is stored in a non-transitory computer-readable medium. 
     The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations. 
     As used herein, the term component is intended to be broadly construed as hardware, firmware, or a combination of hardware and software. 
     It will be apparent that systems and/or methods, described herein, may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and/or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and/or methods were described herein without reference to specific software code—it being understood that software and hardware may be designed to implement the systems and/or methods based on the description herein. 
     Even though combinations of features are recited in the claims and/or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and/or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set. 
     No element, act, or instruction used herein may be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more.” Furthermore, as used herein, the term “set” is intended to include one or more items (e.g., related items, unrelated items, a combination of related and unrelated items, etc.), and may be used interchangeably with “one or more.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.