Patent Publication Number: US-2022215593-A1

Title: Multiple neural network models for filtering during video coding

Description:
This application claims the benefit of U.S. Provisional Application No. 63/133,733, filed Jan. 4, 2021, the entire contents of which are incorporated by reference herein. 
    
    
     TECHNICAL FIELD 
     This disclosure relates to video coding, including video encoding and video decoding. 
     BACKGROUND 
     Digital video capabilities can be incorporated into a wide range of devices, including digital televisions, digital direct broadcast systems, wireless broadcast systems, personal digital assistants (PDAs), laptop or desktop computers, tablet computers, e-book readers, digital cameras, digital recording devices, digital media players, video gaming devices, video game consoles, cellular or satellite radio telephones, so-called “smart phones,” video teleconferencing devices, video streaming devices, and the like. Digital video devices implement video coding techniques, such as those described in the standards defined by MPEG-2, MPEG-4, ITU-T H.263, ITU-T H.264/MPEG-4, Part 10, Advanced Video Coding (AVC), ITU-T H.265/High Efficiency Video Coding (HEVC), and extensions of such standards. The video devices may transmit, receive, encode, decode, and/or store digital video information more efficiently by implementing such video coding techniques. 
     Video coding techniques include spatial (intra-picture) prediction and/or temporal (inter-picture) prediction to reduce or remove redundancy inherent in video sequences. For block-based video coding, a video slice (e.g., a video picture or a portion of a video picture) may be partitioned into video blocks, which may also be referred to as coding tree units (CTUs), coding units (CUs) and/or coding nodes. Video blocks in an intra-coded (I) slice of a picture are encoded using spatial prediction with respect to reference samples in neighboring blocks in the same picture. Video blocks in an inter-coded (P or B) slice of a picture may use spatial prediction with respect to reference samples in neighboring blocks in the same picture or temporal prediction with respect to reference samples in other reference pictures. Pictures may be referred to as frames, and reference pictures may be referred to as reference frames. 
     SUMMARY 
     In general, this disclosure describes techniques for filtering decoded pictures, which may be distorted. The filtering process may be based on neural network techniques. The filtering process may be used in the context of advanced video codecs, such as extensions of ITU-T H.266/Versatile Video Coding (VVC), or subsequent generations of video coding standards, and any other video codecs. In one example, a neural network filtering unit may receive boundary strength values calculated by a deblocking filter and use the boundary strength values to further filter deblocked video data, e.g., using one or more neural network models. 
     In one example, a method of filtering decoded video data includes receiving, by a neural network filtering unit of a video decoding device, data for a decoded picture of video data; receiving, by the neural network filtering unit, data from one or more other units of the video decoding device, the data from the one or more other units being different than the data for the decoded picture, and wherein receiving the data from the one or more other units of the video decoding device comprises receiving boundary strength data from a deblocking unit of the video decoding device; determining, by the neural network filtering unit, one or more neural network models to be used to filter a portion of the decoded picture; and filtering, by the neural network filtering unit, the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device, including the boundary strength data. 
     In another example, a device for filtering decoded video data includes a memory configured to store a decoded picture of video data; and one or more processors implemented in circuitry and configured to execute a neural network filtering unit to: receive data from one or more other units of the device, the data from the one or more other units of the device being different than data for the decoded picture, and wherein to receive the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to receive boundary strength data from a deblocking unit of the device; determine one or more neural network models to be used to filter a portion of the decoded picture; and filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, including the boundary strength data. 
     In another example, a computer-readable storage medium has stored thereon instructions that, when executed, cause a processor of a video decoding device to execute a neural network filtering unit to: receive data for a decoded picture of video data; receive data from one or more other units of the video decoding device, the data from the one or more other units of the video decoding device being different than the data for the decoded picture, and wherein the instructions that cause the processor to receive the data from the one or more other units of the video decoding device comprise instructions that cause the processor to receive boundary strength data from a deblocking unit of the video decoding device; determine one or more neural network models to be used to filter a portion of the decoded picture; and filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device, including the boundary strength data. 
     In another example, device for filtering decoded video data, the device comprising a filtering unit comprising: means for receiving data for a decoded picture of video data; means for receiving data from one or more other units of the video decoding device, the data from the one or more other units being different than the data for the decoded picture, and wherein the means for receiving the data from the one or more other units of the video decoding device comprises means for receiving boundary strength data from a deblocking unit of the video decoding device; means for determining one or more neural network models to be used to filter a portion of the decoded picture; and means for filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device, including the boundary strength data. 
     The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description, drawings, and claims. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating an example video encoding and decoding system that may perform the techniques of this disclosure. 
         FIG. 2  is a conceptual diagram illustrating a hybrid video coding framework. 
         FIG. 3  is a conceptual diagram illustrating a hierarchical prediction structure using a group of pictures (GOP) size of 16. 
         FIG. 4  is a conceptual diagram illustrating a neural network based filter with four layers. 
         FIG. 5  is a conceptual diagram illustrating an example portion of a picture including boundaries, boundary samples, and internal samples. 
         FIG. 6  is a block diagram illustrating an example video encoder that may perform the techniques of this disclosure. 
         FIG. 7  is a block diagram illustrating an example video decoder that may perform the techniques of this disclosure. 
         FIG. 8  is a flowchart illustrating an example method for encoding a current block in accordance with the techniques of this disclosure. 
         FIG. 9  is a flowchart illustrating an example method for decoding a current block in accordance with the techniques of this disclosure. 
         FIG. 10  is a flowchart illustrating an example method of filtering decoded video data according to the techniques of this disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Video coding standards include ITU-T H.261, ISO/IEC MPEG-1 Visual, ITU-T H.262 or ISO/IEC MPEG-2 Visual, ITU-T H.263, ISO/IEC MPEG-4 Visual and ITU-T H.264 (also known as ISO/IEC MPEG-4 AVC), High Efficiency Video Coding (HEVC) or ITU-T H.265, including its range extension, multiview extension (MV-HEVC), and scalable extension (SHVC). Another example video coding standard is Versatile Video Coding (VVC) or ITU-T H.266, which has been developed by the Joint Video Expert TEAM (JVET) of ITU-T Video Coding Experts Group (VCEG) and ISO/IEC Motion Picture Experts Group (MPEG). Version 1 of the VVC specification, referred to as “VVC FDIS” hereinafter, is available from http://phenix.int-evry.fr/jvet/doc_end_user/documents/19_Teleconference/wg11/JVET-S2001-v17.zip. 
       FIG. 1  is a block diagram illustrating an example video encoding and decoding system  100  that may perform the techniques of this disclosure. The techniques of this disclosure are generally directed to coding (encoding and/or decoding) video data. In general, video data includes any data for processing a video. Thus, video data may include raw, uncoded video, encoded video, decoded (e.g., reconstructed) video, and video metadata, such as signaling data. 
     As shown in  FIG. 1 , system  100  includes a source device  102  that provides encoded video data to be decoded and displayed by a destination device  116 , in this example. In particular, source device  102  provides the video data to destination device  116  via a computer-readable medium  110 . Source device  102  and destination device  116  may comprise any of a wide range of devices, including desktop computers, notebook (i.e., laptop) computers, mobile devices, tablet computers, set-top boxes, telephone handsets such as smartphones, televisions, cameras, display devices, digital media players, video gaming consoles, video streaming device, or the like. In some cases, source device  102  and destination device  116  may be equipped for wireless communication, and thus may be referred to as wireless communication devices. 
     In the example of  FIG. 1 , source device  102  includes video source  104 , memory  106 , video encoder  200 , and output interface  108 . Destination device  116  includes input interface  122 , video decoder  300 , memory  120 , and display device  118 . In accordance with this disclosure, video encoder  200  of source device  102  and video decoder  300  of destination device  116  may be configured to apply the techniques for filtering using multiple neural network models. Thus, source device  102  represents an example of a video encoding device, while destination device  116  represents an example of a video decoding device. In other examples, a source device and a destination device may include other components or arrangements. For example, source device  102  may receive video data from an external video source, such as an external camera. Likewise, destination device  116  may interface with an external display device, rather than including an integrated display device. 
     System  100  as shown in  FIG. 1  is merely one example. In general, any digital video encoding and/or decoding device may perform techniques for filtering using multiple neural network models. Source device  102  and destination device  116  are merely examples of such coding devices in which source device  102  generates coded video data for transmission to destination device  116 . This disclosure refers to a “coding” device as a device that performs coding (encoding and/or decoding) of data. Thus, video encoder  200  and video decoder  300  represent examples of coding devices, in particular, a video encoder and a video decoder, respectively. In some examples, source device  102  and destination device  116  may operate in a substantially symmetrical manner such that each of source device  102  and destination device  116  includes video encoding and decoding components. Hence, system  100  may support one-way or two-way video transmission between source device  102  and destination device  116 , e.g., for video streaming, video playback, video broadcasting, or video telephony. 
     In general, video source  104  represents a source of video data (i.e., raw, uncoded video data) and provides a sequential series of pictures (also referred to as “frames”) of the video data to video encoder  200 , which encodes data for the pictures. Video source  104  of source device  102  may include a video capture device, such as a video camera, a video archive containing previously captured raw video, and/or a video feed interface to receive video from a video content provider. As a further alternative, video source  104  may generate computer graphics-based data as the source video, or a combination of live video, archived video, and computer-generated video. In each case, video encoder  200  encodes the captured, pre-captured, or computer-generated video data. Video encoder  200  may rearrange the pictures from the received order (sometimes referred to as “display order”) into a coding order for coding. Video encoder  200  may generate a bitstream including encoded video data. Source device  102  may then output the encoded video data via output interface  108  onto computer-readable medium  110  for reception and/or retrieval by, e.g., input interface  122  of destination device  116 . 
     Memory  106  of source device  102  and memory  120  of destination device  116  represent general purpose memories. In some examples, memories  106 ,  120  may store raw video data, e.g., raw video from video source  104  and raw, decoded video data from video decoder  300 . Additionally or alternatively, memories  106 ,  120  may store software instructions executable by, e.g., video encoder  200  and video decoder  300 , respectively. Although memory  106  and memory  120  are shown separately from video encoder  200  and video decoder  300  in this example, it should be understood that video encoder  200  and video decoder  300  may also include internal memories for functionally similar or equivalent purposes. Furthermore, memories  106 ,  120  may store encoded video data, e.g., output from video encoder  200  and input to video decoder  300 . In some examples, portions of memories  106 ,  120  may be allocated as one or more video buffers, e.g., to store raw, decoded, and/or encoded video data. 
     Computer-readable medium  110  may represent any type of medium or device capable of transporting the encoded video data from source device  102  to destination device  116 . In one example, computer-readable medium  110  represents a communication medium to enable source device  102  to transmit encoded video data directly to destination device  116  in real-time, e.g., via a radio frequency network or computer-based network. Output interface  108  may modulate a transmission signal including the encoded video data, and input interface  122  may demodulate the received transmission signal, according to a communication standard, such as a wireless communication protocol. The communication medium may comprise any wireless or wired communication medium, such as a radio frequency (RF) spectrum or one or more physical transmission lines. The communication medium may form part of a packet-based network, such as a local area network, a wide-area network, or a global network such as the Internet. The communication medium may include routers, switches, base stations, or any other equipment that may be useful to facilitate communication from source device  102  to destination device  116 . 
     In some examples, source device  102  may output encoded data from output interface  108  to storage device  112 . Similarly, destination device  116  may access encoded data from storage device  112  via input interface  122 . Storage device  112  may include any of a variety of distributed or locally accessed data storage media such as a hard drive, Blu-ray discs, DVDs, CD-ROMs, flash memory, volatile or non-volatile memory, or any other suitable digital storage media for storing encoded video data. 
     In some examples, source device  102  may output encoded video data to file server  114  or another intermediate storage device that may store the encoded video data generated by source device  102 . Destination device  116  may access stored video data from file server  114  via streaming or download. 
     File server  114  may be any type of server device capable of storing encoded video data and transmitting that encoded video data to the destination device  116 . File server  114  may represent a web server (e.g., for a website), a server configured to provide a file transfer protocol service (such as File Transfer Protocol (FTP) or File Delivery over Unidirectional Transport (FLUTE) protocol), a content delivery network (CDN) device, a hypertext transfer protocol (HTTP) server, a Multimedia Broadcast Multicast Service (MBMS) or Enhanced MBMS (eMBMS) server, and/or a network attached storage (NAS) device. File server  114  may, additionally or alternatively, implement one or more HTTP streaming protocols, such as Dynamic Adaptive Streaming over HTTP (DASH), HTTP Live Streaming (HLS), Real Time Streaming Protocol (RTSP), HTTP Dynamic Streaming, or the like. 
     Destination device  116  may access encoded video data from file server  114  through any standard data connection, including an Internet connection. This may include a wireless channel (e.g., a Wi-Fi connection), a wired connection (e.g., digital subscriber line (DSL), cable modem, etc.), or a combination of both that is suitable for accessing encoded video data stored on file server  114 . Input interface  122  may be configured to operate according to any one or more of the various protocols discussed above for retrieving or receiving media data from file server  114 , or other such protocols for retrieving media data. 
     Output interface  108  and input interface  122  may represent wireless transmitters/receivers, modems, wired networking components (e.g., Ethernet cards), wireless communication components that operate according to any of a variety of IEEE 802.11 standards, or other physical components. In examples where output interface  108  and input interface  122  comprise wireless components, output interface  108  and input interface  122  may be configured to transfer data, such as encoded video data, according to a cellular communication standard, such as 4G, 4G-LTE (Long-Term Evolution), LTE Advanced, 5G, or the like. In some examples where output interface  108  comprises a wireless transmitter, output interface  108  and input interface  122  may be configured to transfer data, such as encoded video data, according to other wireless standards, such as an IEEE 802.11 specification, an IEEE 802.15 specification (e.g., ZigBee™), a Bluetooth™ standard, or the like. In some examples, source device  102  and/or destination device  116  may include respective system-on-a-chip (SoC) devices. For example, source device  102  may include an SoC device to perform the functionality attributed to video encoder  200  and/or output interface  108 , and destination device  116  may include an SoC device to perform the functionality attributed to video decoder  300  and/or input interface  122 . 
     The techniques of this disclosure may be applied to video coding in support of any of a variety of multimedia applications, such as over-the-air television broadcasts, cable television transmissions, satellite television transmissions, Internet streaming video transmissions, such as dynamic adaptive streaming over HTTP (DASH), digital video that is encoded onto a data storage medium, decoding of digital video stored on a data storage medium, or other applications. 
     Input interface  122  of destination device  116  receives an encoded video bitstream from computer-readable medium  110  (e.g., a communication medium, storage device  112 , file server  114 , or the like). The encoded video bitstream may include signaling information defined by video encoder  200 , which is also used by video decoder  300 , such as syntax elements having values that describe characteristics and/or processing of video blocks or other coded units (e.g., slices, pictures, groups of pictures, sequences, or the like). Display device  118  displays decoded pictures of the decoded video data to a user. Display device  118  may represent any of a variety of display devices such as a liquid crystal display (LCD), a plasma display, an organic light emitting diode (OLED) display, or another type of display device. 
     Although not shown in  FIG. 1 , in some examples, video encoder  200  and video decoder  300  may each be integrated with an audio encoder and/or audio decoder, and may include appropriate MUX-DEMUX units, or other hardware and/or software, to handle multiplexed streams including both audio and video in a common data stream. If applicable, MUX-DEMUX units may conform to the ITU H.223 multiplexer protocol, or other protocols such as the user datagram protocol (UDP). 
     Video encoder  200  and video decoder  300  each may be implemented as any of a variety of suitable encoder and/or decoder circuitry, such as one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), discrete logic, software, hardware, firmware or any combinations thereof. When the techniques are implemented partially in software, a device may store instructions for the software in a suitable, non-transitory computer-readable medium and execute the instructions in hardware using one or more processors to perform the techniques of this disclosure. Each of video encoder  200  and video decoder  300  may be included in one or more encoders or decoders, either of which may be integrated as part of a combined encoder/decoder (CODEC) in a respective device. A device including video encoder  200  and/or video decoder  300  may comprise an integrated circuit, a microprocessor, and/or a wireless communication device, such as a cellular telephone. 
     Video encoder  200  and video decoder  300  may operate according to a video coding standard, such as ITU-T H.265, also referred to as High Efficiency Video Coding (HEVC) or extensions thereto, such as the multi-view and/or scalable video coding extensions. Alternatively, video encoder  200  and video decoder  300  may operate according to other proprietary or industry standards, such as Versatile Video Coding (VVC). A draft of the VVC standard is described in Bross, et al. “Versatile Video Coding (Draft 9),” Joint Video Experts Team (WET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, 18 th  Meeting, 15-24 Apr., JVET-R2001-v8 (hereinafter “VVC Draft 9”). The techniques of this disclosure, however, are not limited to any particular coding standard. 
     In general, video encoder  200  and video decoder  300  may perform block-based coding of pictures. The term “block” generally refers to a structure including data to be processed (e.g., encoded, decoded, or otherwise used in the encoding and/or decoding process). For example, a block may include a two-dimensional matrix of samples of luminance and/or chrominance data. In general, video encoder  200  and video decoder  300  may code video data represented in a YUV (e.g., Y, Cb, Cr) format. That is, rather than coding red, green, and blue (RGB) data for samples of a picture, video encoder  200  and video decoder  300  may code luminance and chrominance components, where the chrominance components may include both red hue and blue hue chrominance components. In some examples, video encoder  200  converts received RGB formatted data to a YUV representation prior to encoding, and video decoder  300  converts the YUV representation to the RGB format. Alternatively, pre- and post-processing units (not shown) may perform these conversions. 
     This disclosure may generally refer to coding (e.g., encoding and decoding) of pictures to include the process of encoding or decoding data of the picture. Similarly, this disclosure may refer to coding of blocks of a picture to include the process of encoding or decoding data for the blocks, e.g., prediction and/or residual coding. An encoded video bitstream generally includes a series of values for syntax elements representative of coding decisions (e.g., coding modes) and partitioning of pictures into blocks. Thus, references to coding a picture or a block should generally be understood as coding values for syntax elements forming the picture or block. 
     HEVC defines various blocks, including coding units (CUs), prediction units (PUs), and transform units (TUs). According to HEVC, a video coder (such as video encoder  200 ) partitions a coding tree unit (CTU) into CUs according to a quadtree structure. That is, the video coder partitions CTUs and CUs into four equal, non-overlapping squares, and each node of the quadtree has either zero or four child nodes. Nodes without child nodes may be referred to as “leaf nodes,” and CUs of such leaf nodes may include one or more PUs and/or one or more TUs. The video coder may further partition PUs and TUs. For example, in HEVC, a residual quadtree (RQT) represents partitioning of TUs. In HEVC, PUs represent inter-prediction data, while TUs represent residual data. CUs that are intra-predicted include intra-prediction information, such as an intra-mode indication. 
     As another example, video encoder  200  and video decoder  300  may be configured to operate according to VVC. According to VVC, a video coder (such as video encoder  200 ) partitions a picture into a plurality of coding tree units (CTUs). Video encoder  200  may partition a CTU according to a tree structure, such as a quadtree-binary tree (QTBT) structure or Multi-Type Tree (MTT) structure. The QTBT structure removes the concepts of multiple partition types, such as the separation between CUs, PUs, and TUs of HEVC. A QTBT structure includes two levels: a first level partitioned according to quadtree partitioning, and a second level partitioned according to binary tree partitioning. A root node of the QTBT structure corresponds to a CTU. Leaf nodes of the binary trees correspond to coding units (CUs). 
     In an MTT partitioning structure, blocks may be partitioned using a quadtree (QT) partition, a binary tree (BT) partition, and one or more types of triple tree (TT) (also called ternary tree (TT)) partitions. A triple or ternary tree partition is a partition where a block is split into three sub-blocks. In some examples, a triple or ternary tree partition divides a block into three sub-blocks without dividing the original block through the center. The partitioning types in MTT (e.g., QT, BT, and TT), may be symmetrical or asymmetrical. 
     In some examples, video encoder  200  and video decoder  300  may use a single QTBT or MTT structure to represent each of the luminance and chrominance components, while in other examples, video encoder  200  and video decoder  300  may use two or more QTBT or MTT structures, such as one QTBT/MTT structure for the luminance component and another QTBT/MTT structure for both chrominance components (or two QTBT/MTT structures for respective chrominance components). 
     Video encoder  200  and video decoder  300  may be configured to use quadtree partitioning per HEVC, QTBT partitioning, MTT partitioning, or other partitioning structures. For purposes of explanation, the description of the techniques of this disclosure is presented with respect to QTBT partitioning. However, it should be understood that the techniques of this disclosure may also be applied to video coders configured to use quadtree partitioning, or other types of partitioning as well. 
     In some examples, a CTU includes a coding tree block (CTB) of luma samples, two corresponding CTBs of chroma samples of a picture that has three sample arrays, or a CTB of samples of a monochrome picture or a picture that is coded using three separate color planes and syntax structures used to code the samples. A CTB may be an N×N block of samples for some value of N such that the division of a component into CTBs is a partitioning. The component may be an array or single sample from one of three arrays (luma and two chroma) for a picture in 4:2:0, 4:2:2, or 4:4:4 color format, or an array or a single sample of the array for a picture in monochrome format. In some examples, a coding block is an M×N block of samples for some values of M and N such that a division of a CTB into coding blocks is a partitioning. 
     The blocks (e.g., CTUs or CUs) may be grouped in various ways in a picture. As one example, a brick may refer to a rectangular region of CTU rows within a particular tile in a picture. A tile may be a rectangular region of CTUs within a particular tile column and a particular tile row in a picture. A tile column refers to a rectangular region of CTUs having a height equal to the height of the picture and a width specified by syntax elements (e.g., such as in a picture parameter set). A tile row refers to a rectangular region of CTUs having a height specified by syntax elements (e.g., such as in a picture parameter set) and a width equal to the width of the picture. 
     In some examples, a tile may be partitioned into multiple bricks, each of which may include one or more CTU rows within the tile. A tile that is not partitioned into multiple bricks may also be referred to as a brick. However, a brick that is a true subset of a tile may not be referred to as a tile. 
     The bricks in a picture may also be arranged in a slice. A slice may be an integer number of bricks of a picture that may be exclusively contained in a single network abstraction layer (NAL) unit. In some examples, a slice includes either a number of complete tiles or only a consecutive sequence of complete bricks of one tile. 
     This disclosure may use “N×N” and “N by N” interchangeably to refer to the sample dimensions of a block (such as a CU or other video block) in terms of vertical and horizontal dimensions, e.g., 16×16 samples or 16 by 16 samples. In general, a 16×16 CU will have 16 samples in a vertical direction (y=16) and 16 samples in a horizontal direction (x=16). Likewise, an N×N CU generally has N samples in a vertical direction and N samples in a horizontal direction, where N represents a nonnegative integer value. The samples in a CU may be arranged in rows and columns. Moreover, CUs need not necessarily have the same number of samples in the horizontal direction as in the vertical direction. For example, CUs may comprise N×M samples, where M is not necessarily equal to N. 
     Video encoder  200  encodes video data for CUs representing prediction and/or residual information, and other information. The prediction information indicates how the CU is to be predicted in order to form a prediction block for the CU. The residual information generally represents sample-by-sample differences between samples of the CU prior to encoding and the prediction block. 
     To predict a CU, video encoder  200  may generally form a prediction block for the CU through inter-prediction or intra-prediction. Inter-prediction generally refers to predicting the CU from data of a previously coded picture, whereas intra-prediction generally refers to predicting the CU from previously coded data of the same picture. To perform inter-prediction, video encoder  200  may generate the prediction block using one or more motion vectors. Video encoder  200  may generally perform a motion search to identify a reference block that closely matches the CU, e.g., in terms of differences between the CU and the reference block. Video encoder  200  may calculate a difference metric using a sum of absolute difference (SAD), sum of squared differences (SSD), mean absolute difference (MAD), mean squared differences (MSD), or other such difference calculations to determine whether a reference block closely matches the current CU. In some examples, video encoder  200  may predict the current CU using uni-directional prediction or bi-directional prediction. 
     Some examples of VVC also provide an affine motion compensation mode, which may be considered an inter-prediction mode. In affine motion compensation mode, video encoder  200  may determine two or more motion vectors that represent non-translational motion, such as zoom in or out, rotation, perspective motion, or other irregular motion types. 
     To perform intra-prediction, video encoder  200  may select an intra-prediction mode to generate the prediction block. Some examples of VVC provide sixty-seven intra-prediction modes, including various directional modes, as well as planar mode and DC mode. In general, video encoder  200  selects an intra-prediction mode that describes neighboring samples to a current block (e.g., a block of a CU) from which to predict samples of the current block. Such samples may generally be above, above and to the left, or to the left of the current block in the same picture as the current block, assuming video encoder  200  codes CTUs and CUs in raster scan order (left to right, top to bottom). 
     Video encoder  200  encodes data representing the prediction mode for a current block. For example, for inter-prediction modes, video encoder  200  may encode data representing which of the various available inter-prediction modes is used, as well as motion information for the corresponding mode. For uni-directional or bi-directional inter-prediction, for example, video encoder  200  may encode motion vectors using advanced motion vector prediction (AMVP) or merge mode. Video encoder  200  may use similar modes to encode motion vectors for affine motion compensation mode. 
     Following prediction, such as intra-prediction or inter-prediction of a block, video encoder  200  may calculate residual data for the block. The residual data, such as a residual block, represents sample by sample differences between the block and a prediction block for the block, formed using the corresponding prediction mode. Video encoder  200  may apply one or more transforms to the residual block, to produce transformed data in a transform domain instead of the sample domain. For example, video encoder  200  may apply a discrete cosine transform (DCT), an integer transform, a wavelet transform, or a conceptually similar transform to residual video data. Additionally, video encoder  200  may apply a secondary transform following the first transform, such as a mode-dependent non-separable secondary transform (MDNSST), a signal dependent transform, a Karhunen-Loeve transform (KLT), or the like. Video encoder  200  produces transform coefficients following application of the one or more transforms. 
     As noted above, following any transforms to produce transform coefficients, video encoder  200  may perform quantization of the transform coefficients. Quantization generally refers to a process in which transform coefficients are quantized to possibly reduce the amount of data used to represent the transform coefficients, providing further compression. By performing the quantization process, video encoder  200  may reduce the bit depth associated with some or all of the transform coefficients. For example, video encoder  200  may round an n-bit value down to an rn-bit value during quantization, where n is greater than rn. In some examples, to perform quantization, video encoder  200  may perform a bitwise right-shift of the value to be quantized. 
     Following quantization, video encoder  200  may scan the transform coefficients, producing a one-dimensional vector from the two-dimensional matrix including the quantized transform coefficients. The scan may be designed to place higher energy (and therefore lower frequency) coefficients at the front of the vector and to place lower energy (and therefore higher frequency) transform coefficients at the back of the vector. In some examples, video encoder  200  may utilize a predefined scan order to scan the quantized transform coefficients to produce a serialized vector, and then entropy encode the quantized transform coefficients of the vector. In other examples, video encoder  200  may perform an adaptive scan. After scanning the quantized transform coefficients to form the one-dimensional vector, video encoder  200  may entropy encode the one-dimensional vector, e.g., according to context-adaptive binary arithmetic coding (CABAC). Video encoder  200  may also entropy encode values for syntax elements describing metadata associated with the encoded video data for use by video decoder  300  in decoding the video data. 
     To perform CABAC, video encoder  200  may assign a context within a context model to a symbol to be transmitted. The context may relate to, for example, whether neighboring values of the symbol are zero-valued or not. The probability determination may be based on a context assigned to the symbol. 
     Video encoder  200  may further generate syntax data, such as block-based syntax data, picture-based syntax data, and sequence-based syntax data, to video decoder  300 , e.g., in a picture header, a block header, a slice header, or other syntax data, such as a sequence parameter set (SPS), picture parameter set (PPS), or video parameter set (VPS). Video decoder  300  may likewise decode such syntax data to determine how to decode corresponding video data. 
     In this manner, video encoder  200  may generate a bitstream including encoded video data, e.g., syntax elements describing partitioning of a picture into blocks (e.g., CUs) and prediction and/or residual information for the blocks. Ultimately, video decoder  300  may receive the bitstream and decode the encoded video data. 
     In general, video decoder  300  performs a reciprocal process to that performed by video encoder  200  to decode the encoded video data of the bitstream. For example, video decoder  300  may decode values for syntax elements of the bitstream using CABAC in a manner substantially similar to, albeit reciprocal to, the CABAC encoding process of video encoder  200 . The syntax elements may define partitioning information for partitioning a picture into CTUs, and partitioning of each CTU according to a corresponding partition structure, such as a QTBT structure, to define CUs of the CTU. The syntax elements may further define prediction and residual information for blocks (e.g., CUs) of video data. 
     The residual information may be represented by, for example, quantized transform coefficients. Video decoder  300  may inverse quantize and inverse transform the quantized transform coefficients of a block to reproduce a residual block for the block. Video decoder  300  uses a signaled prediction mode (intra- or inter-prediction) and related prediction information (e.g., motion information for inter-prediction) to form a prediction block for the block. Video decoder  300  may then combine the prediction block and the residual block (on a sample-by-sample basis) to reproduce the original block. Video decoder  300  may perform additional processing, such as performing a deblocking process to reduce visual artifacts along boundaries of the block. 
     This disclosure may generally refer to “signaling” certain information, such as syntax elements. The term “signaling” may generally refer to the communication of values for syntax elements and/or other data used to decode encoded video data. That is, video encoder  200  may signal values for syntax elements in the bitstream. In general, signaling refers to generating a value in the bitstream. As noted above, source device  102  may transport the bitstream to destination device  116  substantially in real time, or not in real time, such as might occur when storing syntax elements to storage device  112  for later retrieval by destination device  116 . 
       FIG. 2  is a conceptual diagram illustrating a hybrid video coding framework. Video coding standards since H.261 have been based on the so-called hybrid video coding principle, which is illustrated in  FIG. 2 . The term hybrid refers to the combination of two means to reduce redundancy in the video signal, i.e., prediction and transform coding with quantization of the prediction residual. Whereas prediction and transforms reduce redundancy in the video signal by decorrelation, quantization decreases the data of the transform coefficient representation by reducing their precision, ideally by removing only irrelevant details. This hybrid video coding design principle is also used in the two recent standards, ITU-T H.265/HEVC and ITU-T H.266/VVC. 
     As shown in  FIG. 2 , a modern hybrid video coder  130  generally performs block partitioning, motion-compensated or inter-picture prediction, intra-picture prediction, transformation, quantization, entropy coding, and post/in-loop filtering. In the example of  FIG. 2 , video coder  130  includes summation unit  134 , transform unit  136 , quantization unit  138 , entropy coding unit  140 , inverse quantization unit  142 , inverse transform unit  144 , summation unit  146 , loop filter unit  148 , decoded picture buffer (DPB)  150 , intra prediction unit  152 , inter-prediction unit  154 , and motion estimation unit  156 . 
     In general, video coder  130  may, when encoding video data, receive input video data  132 . Block partitioning is used to divide a received picture (image) of the video data into smaller blocks for operation of the prediction and transform processes. Early video coding standards used a fixed block size, typically 16×16 samples. Recent standards, such as HEVC and VVC, employ tree-based partitioning structures to provide flexible partitioning. 
     Motion estimation unit  156  and inter-prediction unit  154  may predict input video data  132 , e.g., from previously decoded data of DPB  150 . Motion-compensated or inter-picture prediction takes advantage of the redundancy that exists between (hence “inter”) pictures of a video sequence. According to block-based motion compensation, which is used in all the modern video codecs, the prediction is obtained from one or more previously decoded pictures, i.e., the reference picture(s). The corresponding areas to generate the inter-prediction are indicated by motion information, including motion vectors and reference picture indices. 
     Summation unit  134  may calculate residual data as differences between input video data  132  and predicted data from intra prediction unit  152  or inter-prediction unit  154 . Summation unit  134  provides residual blocks to transform unit  136 , which applies one or more transforms to the residual block to generate transform blocks. Quantization unit  138  quantizes the transform blocks to form quantized transform coefficients. Entropy coding unit  140  entropy encodes the quantized transform coefficients, as well as other syntax elements, such as motion information or intra-prediction information, to generate output bitstream  158 . 
     Meanwhile, inverse quantization unit  142  inverse quantizes the quantized transform coefficients, and inverse transform unit  144  inverse transforms the transform coefficients, to reproduce residual blocks. Summation unit  146  combines the residual blocks with prediction blocks (on a sample-by-sample basis) to produce decoded blocks of video data. Loop filter unit  148  applies one or more filters (e.g., at least one of a neural network-based filter, a neural network-based loop filter, a neural network-based post loop filter, an adaptive in-loop filter, or a pre-defined adaptive in-loop filter) to the decoded block to produce filtered decoded blocks. 
     In accordance with the techniques of this disclosure, a neural network filtering unit of loop filter unit  148  may receive data for a decoded picture of video data from summation unit  146  and from one or more other units of hybrid video coder  130 , e.g., transform unit  136 , quantization unit  138 , intra prediction unit  152 , inter-prediction unit  154 , motion estimation unit  156 , and/or one or more other filtering units within loop filter unit  148 . For example, the neural network filtering unit may receive data from a deblocking filtering unit (also referred to as a “deblocking unit) of loop filter unit  148 . The neural network filtering unit may receive, for example, boundary strength values representing whether a particular boundary is to be filtered for deblocking, and if so, a degree to which the boundary will be filtered. For example, the boundary strength values may correspond to a number of samples on either side of the boundary to be modified and/or a degree to which the samples are to be modified. 
     In other examples, in addition to or in the alternative to the boundary strength values, the neural network filtering unit may receive any or all of coding unit (CU) partitioning data, prediction unit (PU) partitioning data, transform unit (TU) partitioning data, deblocking filtering data, quantization parameter (QP) data, intra-prediction data, inter-prediction data, data representing distance between the decoded picture and one or more reference pictures, or motion information for one or more decoded blocks of the decoded picture. The deblocking filtering data may further include one or more of whether long or short filters were used for deblocking or whether strong or weak filters were used for deblocking. The data representing the distance between the decoded picture and the reference pictures may be represented as picture order count (POC) differences between POC values of the pictures. 
     The neural network filtering unit may determine one or more neural network models to be used to filter at least a portion of the decoded picture. The neural network filtering unit may further filter the at least portion of the decoded picture using the determined one or more neural network models and the data from the other units, including the boundary strength data. For example, the neural network filtering unit may provide the additional data as one or more additional input planes to a convolutional neural network (CNN). 
     A block of video data, such as a CTU or CU, may in fact include multiple color components, e.g., a luminance or “luma” component, a blue hue chrominance or “chroma” component, and a red hue chrominance (chroma) component. The luma component may have a larger spatial resolution than the chroma components, and one of the chroma components may have a larger spatial resolution than the other chroma component. Alternatively, the luma component may have a larger spatial resolution than the chroma components, and the two chroma components may have equal spatial resolutions with each other. For example, in 4:2:2 format, the luma component may be twice as large as the chroma components horizontally and equal to the chroma components vertically. As another example, in 4:2:0 format, the luma component may be twice as large as the chroma components horizontally and vertically. The various operations discussed above may generally be applied to each of the luma and chroma components individually (although certain coding information, such as motion information or intra-prediction direction, may be determined for the luma component and inherited by the corresponding chroma components). 
       FIG. 3  is a conceptual diagram illustrating a hierarchical prediction structure  166  using a group of pictures (GOP) size of 16. In recent video codecs, hierarchical prediction structures inside a group of pictures (GOP) is applied to improve coding efficiency. 
     Referring again to  FIG. 2 , intra-picture prediction exploits spatial redundancy that exists within a picture (hence “intra”) by deriving the prediction for a block from already coded/decoded, spatially neighboring (reference) samples. The directional angular prediction, DC prediction and plane or planar prediction are used in the most recent video codec, including AVC, HEVC, and VVC. 
     Hybrid video coding standards apply a block transform to the prediction residual (regardless of whether it comes from inter- or intra-picture prediction). In early standards, including H.261, H.262, and H.263, a discrete cosine transform (DCT) is employed. In HEVC and VVC, more transform kernel besides DCT are applied, in order to account for different statistics in the specific video signal. 
     Quantization aims to reduce the precision of an input value or a set of input values in order to decrease the amount of data needed to represent the values. In hybrid video coding, quantization is typically applied to individual transformed residual samples, i.e., to transform coefficients, resulting in integer coefficient levels. In recent video coding standards, the step size is derived from a so-called quantization parameter (QP) that controls the fidelity and bit rate. A larger step size lowers the bit rate but also deteriorates the quality, which e.g., results in video pictures exhibiting blocking artifacts and blurred details. 
     Context-adaptive binary arithmetic coding (CABAC) is a form of entropy coding used in recent video codecs, e.g., AVC, HEVC, and VVC, due to its high efficiency. 
     Post/in-loop filtering is a filtering process (or combination of such processes) that is applied to the reconstructed picture to reduce the coding artifacts. The input of the filtering process is generally the reconstructed picture, which is the combination of the reconstructed residual signal (which includes quantization error) and the prediction. As shown in  FIG. 2 , the reconstructed pictures after in-loop filtering are stored and used as a reference for inter-picture prediction of subsequent pictures. The coding artifacts are mostly determined by the QP, therefore QP information is generally used in design of the filtering process. In HEVC, the in-loop filters include deblocking filtering and sample adaptive offset (SAO) filtering. In the VVC standard, an adaptive loop filter (ALF) was introduced as a third filter. The filtering process of ALF is as shown below: 
         R ′( i,j )= R ( i,j )+((Σ k≠0 Σ l≠0   f ( k,l )× K ( R ( i+k,j+l )− R ( i,j ), c ( k,l ))+64)&gt;&gt;7)  (1)
 
     where R (i, j) is the set of samples before the filtering process, R′(i, j) is a sample value after the filtering process. f (k, l) denotes filter coefficients, K(x, y) is a clipping function and c(k, l) denotes the clipping parameters. The variables k and l vary between 
     
       
         
           
             
               - 
               
                 L 
                 2 
               
             
             ⁢ 
                 
             and 
             ⁢ 
                 
             
               L 
               2 
             
           
         
       
     
     where L denotes the filter length. The clipping function K(x, y)=min(y, max(−y, x)), which corresponds to the function Clip3 (−y, y, x). The clipping operation introduces non-linearity to make ALF more efficient by reducing the impact of neighbor sample values that are too different with the current sample value. In VVC, the filtering parameters can be signalled in the bit stream, it can be selected from the pre-defined filter sets. The ALF filtering process can also be summarized using the following equation: 
         R ′( i,j )= R ( i,j )+ALF_residual_ouput( R )  (2)
 
       FIG. 4  is a conceptual diagram illustrating a neural network based filter  170  with four layers. Various studies have shown that embedding neural networks (NN) into, e.g., the hybrid video coding framework of  FIG. 2 , can improve compression efficiency. Neural networks have been used in the module of intra prediction and inter-prediction to improve prediction efficiency. NN-based in loop filtering is also a hot research topic in recent years. Sometime the filtering process is applied as post-loop filtering. in this case, the filtering process is only applied to the output picture and the un-filtered picture is used as reference picture. 
     NN-based filter  170  can be applied in addition to the existing filters, such as deblocking filters, sample adaptive offset (SAO), and/or adaptive loop filtering (ALF). NN-based filters can also be applied exclusively, where NN-based filters are designed to replace all of the existing filters. Additionally or alternatively, NN-based filters, such as NN-based filter  170 , may be designed to supplement, enhance, or replace any or all of the other filters. 
     As shown in  FIG. 4 , the NN-based filtering process may take the reconstructed samples as inputs, and the intermediate outputs are residual samples, which are added back to the input to refine the input samples. The NN filter may use all color components (e.g., Y, U, and V, or Y, Cb, and Cr, i.e., luminance, blue-hue chrominance, and red-hue chrominance) as input to exploit cross-component correlations. Different color components may share the same filters (including network structure and model parameters) or each component may have its own specific filters. 
     The filtering process can also be generalized as follows: R′(i, j)=R(i, j)+NN_filter_residual_ouput(R). The model structure and model parameters of NN-based filter(s) can pre-defined and be stored at encoder and decoder. The filters can also be signalled in the bit stream. 
     This disclosure recognizes that in some cases, when a video codec (such as video encoder  200  or video decoder  300  of  FIG. 1 ) applies neural network (NN) based filtering as an additional module, the video codec may generate different kinds of information that can be used by the NN based filters to further improve filtering performance. 
     In general, according to the techniques of this disclosure, video encoder  200  and video decoder  300  may include respective filtering units configured to perform NN-based filtering. The filtering units may use information generated by other units (e.g., block partition information, motion information, deblocking filter information, or other such information) when performing the NN-based filtering process. 
     The filtering units may use any information generated by other units or modules that is available when applying a NN filter. Examples of such units or modules that may generate information that may be used by the filtering unit include intra-prediction units, inter-prediction units, transform processing units, quantization units, loop filtering units (e.g., deblocking filter units, sample adaptive offset (SAO) units, adaptive loop filter (ALF) units, or the like), pre-processing units (e.g., motion-compensated temporal filtering units), and/or another NN-based module or unit that co-exists with the NN filtering unit or module. 
     Video encoder  200  and/or video decoder  300  may include multiple NN-based filtering units, one of which performs NN based filtering before another (current) NN-based filtering unit, e.g., as shown in  FIG. 4 . In such a case, the current NN-based filtering unit may use information generated by one or more previous NN-based filtering units when performing NN filtering. 
     The NN-based filtering unit(s) may use any or all of the following information, in various examples, when performing NN-based filtering: CU, PU, and/or TU partition information, deblocking filtering information (e.g., boundary strength values for the de-blocking filtering process, long or short filters, strong or weak filters, or the like), quantization parameters (QPs) used for the current picture/block and/or reference picture(s), intra- and/or inter-prediction mode information, distance between the current picture and reference pictures used to predict the current picture (e.g., picture order count (POC) value differences), and/or motion information of coded blocks. Boundary strength values may also be referred to as boundary filtering strength. In general, boundary strength values or boundary filtering strength values may represent whether a particular block boundary is to be deblocked, and if so, a degree of deblocking to be applied. For example, a relatively strong deblocking filter may modify more values of samples to either side of the block boundary and to a greater degree than a relatively weak deblocking filter. 
     In some examples, when the NN-based filtering unit uses information from other units or modules, the NN-based filtering unit may provide some similar functionality provided by the other units or modules. In such examples, the NN-based filtering unit may modify elements of the other units or modules to improve cooperation between the NN-based filtering unit and the other units or modules. For example, the NN-based filtering unit may interface with a deblocking filter. In such an example, the deblocking filter unit may generate boundary strength information but not perform actual filtering. The NN-based filtering unit may receive the boundary strength information from the deblocking filter unit and provide the received boundary strength information as an input to the NN based filter. 
     The NN-based filtering unit may use information received from other units or modules in various ways. For example, the NN-based filtering unit may use the information as additional input planes of a convolutional neural network (CNN). As another example, the NN-based filtering unit may use the information to modify or adjust the output of the NN-based filter. For example, after applying an NN-based filter to a picture to form a filtered picture, video encoder  200  or video decoder  300  may further adjust the filtered picture based on other information, such as QP. 
     Information from other units or modules may be converted to be more suitable for the NN-based filtering unit. For example, the NN-based filtering unit may convert values between integer and floating point values, scale values to a range that is more suitable for the NN filter (e.g., boundary strength values of a deblocking filter may be scaled to be the same range as input pixels), or scale values to any other range (where the range may be predefined or signaled in the bitstream). 
       FIG. 5  is a conceptual diagram illustrating an example portion of picture  180  including boundaries, boundary samples, and internal samples. In particular, portion of picture  180  includes vertical boundaries  182 A- 182 D (vertical boundaries  182 ) and horizontal boundaries  184 A- 184 C (horizontal boundaries  184 ). As shown in the example of  FIG. 5 , two adjacent boundary samples (labeled ‘N’ and shaded grey in  FIG. 5 ) define respective boundaries  182 ,  184  (represented by solid black lines in  FIG. 5 ) between the two boundary samples. Internal (i.e., non-boundary) samples are labeled ‘M’ in  FIG. 5  and are unshaded. The boundaries may be CU, PU, and/or TU boundaries. A NN-based filtering unit may use the information of internal/boundary samples and boundary locations depicted in  FIG. 5  when performing NN-based filtering. 
     For example, the NN-based filtering unit may use CU, PU, and TU partition information as one or more additional input planes to a CNN-based filter. First, video encoder  200  and/or video decoder  300  may convert partition information (for CUs, PUs, and/or TUs) into a plane by setting boundary samples to different values (e.g., a predefined value N) from internal samples (e.g., M) as shown in  FIG. 5 . In one example, video encoder  200  and video decoder  300  may set values of N=1 and M=0. 
     Video encoder  200  and video decoder  300  may generate multiple partition planes, e.g., one each for CU partitions, PU partitions, and/or TU partitions. In some examples, planes may be combined, e.g., one plane for CU partitions and another plane for PU and TU partitions. In the case of “dual tree” partitioning being enabled, in which luma and chroma components may be partitioned differently (and/or the two chroma components may be partitioned differently than each other), different color components may have different partition planes. The NN-based filter may use any or all of these various planes as input planes to a CNN based filter. Various examples of handling multiple partition planes include: using the partition planes as separate input planes to the CNN based filter; combining multiple partition planes into one plane (e.g., for each pixel sample at position (i, j), Plane combined  (i,j)=MAX(Plane a  (i,j), Plane b  (i,j), . . . )); or a combination of separate and/or combined partition planes (e.g., combine CU and TU partition planes of each color component into one plane and use the multiple combined planes of each color as input to the CNN based filter). That is, in one example, to combine planes to form a combined input plane, for each position (i, j) of the input planes, video encoder  200  and video decoder  300  may set a value for position (i, j) of the combined input plane equal to a maximum of the values at position (i, j) of the plurality of input planes. 
     After creating the plane(s), before using the values of the planes as input to the CNN based filter, the values may be converted as needed in various examples. For example, values may be converted between integer and floating point, values may be scaled to have the same range as the input pixel values, and/or values may be scaled to any other range, which may be pre-defined or signaled in the bitstream. 
     As discussed above, in some examples, boundary strength calculation logic of the deblocking filter may be used to derive boundary strength parameters. The NN-based filtering unit may use the boundary strength parameters as additional input plane(s) to CNN based filters (e.g., VVC boundary strength calculation of DB filter). The actual filtering process of the deblocking filter may be disabled when the CNN filter is applied. 
     Initially, the deblocking filtering unit may derive boundary strength values for edges that are qualified for de-blocking filtering. The Conversion may be applied as needed by examples of the techniques of this disclosure (e.g., conversion between integer and floating-point value type, scaling the values to have the same range as the input pixels or any other range that considered suitable for a CNN filter to use, etc.). 
     The NN-based filtering unit, or another unit of the video codec, may convert the boundary strength values into plane(s) that can be used together with other input planes as the input to the CNN based filter. One example of such conversion is similar to that described above with respect to  FIG. 5 , where the boundary samples may be set to the boundary strength values and the non-boundary samples may be set to 0. In the case of VVC, the range of boundary samples is [0, 2]. 
     Since boundary strength of different color components may be calculated separately, the horizontal and vertical boundaries may also be calculated separately. For a picture or a coded region, multiple boundary strength planes can be generated. Similar to the discussion above, in one example, the NN-based filtering unit may choose to use a single input plane or multiple input planes. When multiple planes are used, different ways can be applied to organize the planes. Several examples include: using the planes as separate input planes to the CNN based filter; combining multiple boundary strength planes into one plane; or a combination of these examples. 
     As an example of combining two planes, for each sample position (i, j), Plane combined  (i,j)=MAX(Plane A  (i,j), Plane B  (i,j)). As another example, given 2 planes, A and B, Plane combined  (i,j)=Plane A  (i,j)+Plane B  (i,j). As another example, given 2 planes, A and B, let R B  be the range of values in plane B; for each sample position (i,j), Plane combined  (i,j)=R B *Plane A  (i,j)+Plane B  (i,j). In this example, the effect can be considered like using Plane A  as a major factor and Plane B  as refinement. To combine more than 2 planes, the techniques described above can be applied multiple times. And the techniques described above can be used at different stages. For example, for planes A, B, and C, use one technique above to get a combined plane AB and use the other technique above to combine AB with C to get ABC. 
     To combine the techniques discussed above, in one example, the boundary strength planes for vertical and horizontal planes may be combined using any of the various techniques discussed above, and then the boundary strength planes of different color components may be provided to the CNN based filter as separate input planes. As discussed above, the values of the planes may be converted as needed, e.g., conversion between integer and floating point and/or scaled to a particular range, which may be predetermined or signaled in the bitstream. 
     In some examples, information of long/short filter may be used as an additional or alternative input plane(s) to CNN based filters. Similar to the case of using boundary strength, the information of using long or short de-blocking filter can be generated for the CNN based filter(s) to use, multiple planes can be used as separate planes or be combined before using as CNN filter input. 
     In some examples, the information of strong/weak filter may be used as additional or alternative input plane(s) to CNN based filters. Similar to the case of using boundary strength, the information of using strong or weak de-blocking filter can be generated for the CNN based filter(s) to use, multiple planes can be used as separate planes or be combined before using as CNN filter input. 
     The various techniques discussed above may be combined in a variety of ways. For example, the following planes may be generated and used in the CNN filter process: boundary strength (range of values: 0, 1, 2), long/short and strong/weak filter (values: 2 for long &amp; strong filter, 1 for short &amp; strong filter, 0 for short &amp; weak filter (In VVC, strong filter condition must be met to have long filter)). Similar to other examples as discussed above, the generated planes can be used as separate input planes to the CNN filter or some/all of the planes can be combined together. 
     To feed pictures or coded regions to a CNN filter, downsampling/upsampling may happen. For example, luma and chroma components have different resolutions in YUV  420 , YUV 422  color format video, etc. In this case, downsampling/upsampling of color components may be needed to create input planes for the CNN filter. Some techniques include: upsample the chroma components to have the same resolution as the luma component; downsample the luma component to have same the resolution as the chroma components; or convert one luma pixel plane into several smaller pixel planes with the same size as the chroma planes. 
     When downsampling/upsampling of the color plane is needed, the corresponding information planes introduced in this disclosure may be downsampled/upsampled as well. The downsampling/upsampling can follow the same rule as the corresponding pixel planes. In case of converting one luma pixel plane into several smaller pixel planes to align the size with chroma, in one example, video encoder  200  or video decoder  300  may keep only one downsampled plane, instead of keeping all of the planes like luma pixels do. 
       FIG. 6  is a block diagram illustrating an example video encoder  200  that may perform the techniques of this disclosure.  FIG. 6  is provided for purposes of explanation and should not be considered limiting of the techniques as broadly exemplified and described in this disclosure. For purposes of explanation, this disclosure describes video encoder  200  in the context of video coding standards such as the ITU-T H.265/HEVC video coding standard and the VVC video coding standard in development. However, the techniques of this disclosure are not limited to these video coding standards and are applicable generally to other video encoding and decoding standards. 
     In the example of  FIG. 6 , video encoder  200  includes video data memory  230 , mode selection unit  202 , residual generation unit  204 , transform processing unit  206 , quantization unit  208 , inverse quantization unit  210 , inverse transform processing unit  212 , reconstruction unit  214 , filter unit  216 , decoded picture buffer (DPB)  218 , and entropy encoding unit  220 . Any or all of video data memory  230 , mode selection unit  202 , residual generation unit  204 , transform processing unit  206 , quantization unit  208 , inverse quantization unit  210 , inverse transform processing unit  212 , reconstruction unit  214 , filter unit  216 , DPB  218 , and entropy encoding unit  220  may be implemented in one or more processors or in processing circuitry. For instance, the units of video encoder  200  may be implemented as one or more circuits or logic elements as part of hardware circuitry, or as part of a processor, ASIC, or FPGA. Moreover, video encoder  200  may include additional or alternative processors or processing circuitry to perform these and other functions. 
     Video data memory  230  may store video data to be encoded by the components of video encoder  200 . Video encoder  200  may receive the video data stored in video data memory  230  from, for example, video source  104  ( FIG. 1 ). DPB  218  may act as a reference picture memory that stores reference video data for use in prediction of subsequent video data by video encoder  200 . Video data memory  230  and DPB  218  may be formed by any of a variety of memory devices, such as dynamic random access memory (DRAM), including synchronous DRAM (SDRAM), magnetoresistive RAM (MRAM), resistive RAM (RRAM), or other types of memory devices. Video data memory  230  and DPB  218  may be provided by the same memory device or separate memory devices. In various examples, video data memory  230  may be on-chip with other components of video encoder  200 , as illustrated, or off-chip relative to those components. 
     In this disclosure, reference to video data memory  230  should not be interpreted as being limited to memory internal to video encoder  200 , unless specifically described as such, or memory external to video encoder  200 , unless specifically described as such. Rather, reference to video data memory  230  should be understood as reference memory that stores video data that video encoder  200  receives for encoding (e.g., video data for a current block that is to be encoded). Memory  106  of  FIG. 1  may also provide temporary storage of outputs from the various units of video encoder  200 . 
     The various units of  FIG. 6  are illustrated to assist with understanding the operations performed by video encoder  200 . The units may be implemented as fixed-function circuits, programmable circuits, or a combination thereof. Fixed-function circuits refer to circuits that provide particular functionality, and are preset on the operations that can be performed. Programmable circuits refer to circuits that can be programmed to perform various tasks, and provide flexible functionality in the operations that can be performed. For instance, programmable circuits may execute software or firmware that cause the programmable circuits to operate in the manner defined by instructions of the software or firmware. Fixed-function circuits may execute software instructions (e.g., to receive parameters or output parameters), but the types of operations that the fixed-function circuits perform are generally immutable. In some examples, one or more of the units may be distinct circuit blocks (fixed-function or programmable), and in some examples, the one or more units may be integrated circuits. 
     Video encoder  200  may include arithmetic logic units (ALUs), elementary function units (EFUs), digital circuits, analog circuits, and/or programmable cores, formed from programmable circuits. In examples where the operations of video encoder  200  are performed using software executed by the programmable circuits, memory  106  ( FIG. 1 ) may store instructions (e.g., object code) of the software that video encoder  200  receives and executes, or another memory within video encoder  200  (not shown) may store such instructions. 
     Video data memory  230  is configured to store received video data. Video encoder  200  may retrieve a picture of the video data from video data memory  230  and provide the video data to residual generation unit  204  and mode selection unit  202 . Video data in video data memory  230  may be raw video data that is to be encoded. 
     Mode selection unit  202  includes motion estimation unit  222 , motion compensation unit  224 , and intra-prediction unit  226 . Mode selection unit  202  may include additional functional units to perform video prediction in accordance with other prediction modes. As examples, mode selection unit  202  may include a palette unit, an intra-block copy unit (which may be part of motion estimation unit  222  and/or motion compensation unit  224 ), an affine unit, a linear model (LM) unit, or the like. 
     Mode selection unit  202  generally coordinates multiple encoding passes to test combinations of encoding parameters and resulting rate-distortion (RD) values for such combinations. The encoding parameters may include partitioning of CTUs into CUs, prediction modes for the CUs, transform types for residual data of the CUs, quantization parameters for residual data of the CUs, and so on. Mode selection unit  202  may ultimately select the combination of encoding parameters having rate-distortion values that are better than the other tested combinations. 
     Video encoder  200  may partition a picture retrieved from video data memory  230  into a series of CTUs, and encapsulate one or more CTUs within a slice. Mode selection unit  202  may partition a CTU of the picture in accordance with a tree structure, such as the QTBT structure or the quad-tree structure of HEVC described above. As described above, video encoder  200  may form one or more CUs from partitioning a CTU according to the tree structure. Such a CU may also be referred to generally as a “video block” or “block.” 
     In general, mode selection unit  202  also controls the components thereof (e.g., motion estimation unit  222 , motion compensation unit  224 , and intra-prediction unit  226 ) to generate a prediction block for a current block (e.g., a current CU, or in HEVC, the overlapping portion of a PU and a TU). For inter-prediction of a current block, motion estimation unit  222  may perform a motion search to identify one or more closely matching reference blocks in one or more reference pictures (e.g., one or more previously coded pictures stored in DPB  218 ). In particular, motion estimation unit  222  may calculate a value representative of how similar a potential reference block is to the current block, e.g., according to sum of absolute difference (SAD), sum of squared differences (SSD), mean absolute difference (MAD), mean squared differences (MSD), or the like. Motion estimation unit  222  may generally perform these calculations using sample-by-sample differences between the current block and the reference block being considered. Motion estimation unit  222  may identify a reference block having a lowest value resulting from these calculations, indicating a reference block that most closely matches the current block. 
     Motion estimation unit  222  may form one or more motion vectors (MVs) that defines the positions of the reference blocks in the reference pictures relative to the position of the current block in a current picture. Motion estimation unit  222  may then provide the motion vectors to motion compensation unit  224 . For example, for uni-directional inter-prediction, motion estimation unit  222  may provide a single motion vector, whereas for bi-directional inter-prediction, motion estimation unit  222  may provide two motion vectors. Motion compensation unit  224  may then generate a prediction block using the motion vectors. For example, motion compensation unit  224  may retrieve data of the reference block using the motion vector. As another example, if the motion vector has fractional sample precision, motion compensation unit  224  may interpolate values for the prediction block according to one or more interpolation filters. Moreover, for bi-directional inter-prediction, motion compensation unit  224  may retrieve data for two reference blocks identified by respective motion vectors and combine the retrieved data, e.g., through sample-by-sample averaging or weighted averaging. 
     As another example, for intra-prediction, or intra-prediction coding, intra-prediction unit  226  may generate the prediction block from samples neighboring the current block. For example, for directional modes, intra-prediction unit  226  may generally mathematically combine values of neighboring samples and populate these calculated values in the defined direction across the current block to produce the prediction block. As another example, for DC mode, intra-prediction unit  226  may calculate an average of the neighboring samples to the current block and generate the prediction block to include this resulting average for each sample of the prediction block. 
     Mode selection unit  202  provides the prediction block to residual generation unit  204 . Residual generation unit  204  receives a raw, uncoded version of the current block from video data memory  230  and the prediction block from mode selection unit  202 . Residual generation unit  204  calculates sample-by-sample differences between the current block and the prediction block. The resulting sample-by-sample differences define a residual block for the current block. In some examples, residual generation unit  204  may also determine differences between sample values in the residual block to generate a residual block using residual differential pulse code modulation (RDPCM). In some examples, residual generation unit  204  may be formed using one or more subtractor circuits that perform binary subtraction. 
     In examples where mode selection unit  202  partitions CUs into PUs, each PU may be associated with a luma prediction unit and corresponding chroma prediction units. Video encoder  200  and video decoder  300  may support PUs having various sizes. As indicated above, the size of a CU may refer to the size of the luma coding block of the CU and the size of a PU may refer to the size of a luma prediction unit of the PU. Assuming that the size of a particular CU is 2N×2N, video encoder  200  may support PU sizes of 2N×2N or N×N for intra prediction, and symmetric PU sizes of 2N×2N, 2N×N, N×2N, N×N, or similar for inter-prediction. Video encoder  200  and video decoder  300  may also support asymmetric partitioning for PU sizes of 2N×nU, 2N×nD, nL×2N, and nR×2N for inter-prediction. 
     In examples where mode selection unit  202  does not further partition a CU into PUs, each CU may be associated with a luma coding block and corresponding chroma coding blocks. As above, the size of a CU may refer to the size of the luma coding block of the CU. The video encoder  200  and video decoder  300  may support CU sizes of 2N×2N, 2N×N, or N×2N. 
     For other video coding techniques such as intra-block copy mode coding, affine-mode coding, and linear model (LM) mode coding, as some examples, mode selection unit  202 , via respective units associated with the coding techniques, generates a prediction block for the current block being encoded. In some examples, such as palette mode coding, mode selection unit  202  may not generate a prediction block, and instead may generate syntax elements that indicate the manner in which to reconstruct the block based on a selected palette. In such modes, mode selection unit  202  may provide these syntax elements to entropy encoding unit  220  to be encoded. 
     As described above, residual generation unit  204  receives the video data for the current block and the corresponding prediction block. Residual generation unit  204  then generates a residual block for the current block. To generate the residual block, residual generation unit  204  calculates sample-by-sample differences between the prediction block and the current block. 
     Transform processing unit  206  applies one or more transforms to the residual block to generate a block of transform coefficients (referred to herein as a “transform coefficient block”). Transform processing unit  206  may apply various transforms to a residual block to form the transform coefficient block. For example, transform processing unit  206  may apply a discrete cosine transform (DCT), a directional transform, a Karhunen-Loeve transform (KLT), or a conceptually similar transform to a residual block. In some examples, transform processing unit  206  may perform multiple transforms to a residual block, e.g., a primary transform and a secondary transform, such as a rotational transform. In some examples, transform processing unit  206  does not apply transforms to a residual block. 
     Quantization unit  208  may quantize the transform coefficients in a transform coefficient block, to produce a quantized transform coefficient block. Quantization unit  208  may quantize transform coefficients of a transform coefficient block according to a quantization parameter (QP) value associated with the current block. Video encoder  200  (e.g., via mode selection unit  202 ) may adjust the degree of quantization applied to the transform coefficient blocks associated with the current block by adjusting the QP value associated with the CU. Quantization may introduce loss of information, and thus, quantized transform coefficients may have lower precision than the original transform coefficients produced by transform processing unit  206 . 
     Inverse quantization unit  210  and inverse transform processing unit  212  may apply inverse quantization and inverse transforms to a quantized transform coefficient block, respectively, to reconstruct a residual block from the transform coefficient block. Reconstruction unit  214  may produce a reconstructed block corresponding to the current block (albeit potentially with some degree of distortion) based on the reconstructed residual block and a prediction block generated by mode selection unit  202 . For example, reconstruction unit  214  may add samples of the reconstructed residual block to corresponding samples from the prediction block generated by mode selection unit  202  to produce the reconstructed block. 
     Filter unit  216  may perform one or more filter operations on reconstructed blocks. For example, filter unit  216  may perform deblocking operations to reduce blockiness artifacts along edges of CUs. When performing deblocking operations, filter unit  216  (or a deblocking filter unit thereof) may initially calculate boundary strength values. The deblocking filter unit of filter unit  216  may then use the boundary strength values to determine other deblocking parameters, such as t C  and beta (β), which may generally represent filtering strength and coefficients to be used for deblocking and/or deblocking decision functions. Operations of filter unit  216  may be skipped, in some examples. 
     Filter unit  216  may be configured to perform the various techniques of this disclosure, e.g., to determine one or more of neural network models (NN models)  232  to be used to filter a decoded picture and/or whether to apply NN model filtering. Mode selection unit  202  may perform RD calculations using both filtered and unfiltered pictures to determine RD costs to determine whether to perform NN model filtering, and then provide data to entropy encoding unit  220  representing, e.g., whether or not to perform NN model filtering, one or more of NN models  232  to use for a current picture, or the like. 
     In particular, filter unit  216  may receive a decoded (reconstructed) picture from reconstruction unit  214 . Filter unit  216  may also obtain (e.g., receive) additional data from one or more other units, e.g., mode selection unit  202 , motion estimation unit  222 , motion compensation unit  224 , intra-prediction unit  226 , transform processing unit  206 , quantization unit  208 , another filtering unit (e.g., separate from or contained within filter unit  216 ), or the like. For example, filter unit  216  may perform both NN-based filtering and other types of filtering, such as deblocking filtering, SAO filtering, ALF filtering, or the like. Filter unit  216  may thus obtain deblocking parameters, such as boundary strength values, for blocks of the current picture. Filter unit  216  may provide the boundary strength values (and/or other received data) to a NN filtering unit of filter unit  216 . 
     The NN filtering unit may use the additional data (e.g., boundary strength values and/or other data received from other units of video encoder  200 ) to select NN models from NN models  232  to be used to perform NN-based filtering and to perform the actual NN-based filtering. For example, the NN filtering unit of filter unit  216  may provide the additional data (e.g., the boundary strength values) to the selected one or more NN models of NN models  232  in the form of additional input layers. In some examples, filter unit  216  may modify the additional data, e.g., by converting representation formats (such as between floating point and decimal) or modifying ranges of the additional values to correspond to input sample ranges. 
     Video encoder  200  stores reconstructed blocks in DPB  218 . For instance, in examples where operations of filter unit  216  are not needed, reconstruction unit  214  may store reconstructed blocks to DPB  218 . In examples where operations of filter unit  216  are needed, filter unit  216  may store the filtered reconstructed blocks to DPB  218 . Motion estimation unit  222  and motion compensation unit  224  may retrieve a reference picture from DPB  218 , formed from the reconstructed (and potentially filtered) blocks, to inter-predict blocks of subsequently encoded pictures. In addition, intra-prediction unit  226  may use reconstructed blocks in DPB  218  of a current picture to intra-predict other blocks in the current picture. 
     In general, entropy encoding unit  220  may entropy encode syntax elements received from other functional components of video encoder  200 . For example, entropy encoding unit  220  may entropy encode quantized transform coefficient blocks from quantization unit  208 . As another example, entropy encoding unit  220  may entropy encode prediction syntax elements (e.g., motion information for inter-prediction or intra-mode information for intra-prediction) from mode selection unit  202 . Entropy encoding unit  220  may perform one or more entropy encoding operations on the syntax elements, which are another example of video data, to generate entropy-encoded data. For example, entropy encoding unit  220  may perform a context-adaptive variable length coding (CAVLC) operation, a CABAC operation, a variable-to-variable (V2V) length coding operation, a syntax-based context-adaptive binary arithmetic coding (SBAC) operation, a Probability Interval Partitioning Entropy (PIPE) coding operation, an Exponential-Golomb encoding operation, or another type of entropy encoding operation on the data. In some examples, entropy encoding unit  220  may operate in bypass mode where syntax elements are not entropy encoded. 
     Video encoder  200  may output a bitstream that includes the entropy encoded syntax elements needed to reconstruct blocks of a slice or picture. In particular, entropy encoding unit  220  may output the bitstream. 
     The operations described above are described with respect to a block. Such description should be understood as being operations for a luma coding block and/or chroma coding blocks. As described above, in some examples, the luma coding block and chroma coding blocks are luma and chroma components of a CU. In some examples, the luma coding block and the chroma coding blocks are luma and chroma components of a PU. 
     In some examples, operations performed with respect to a luma coding block need not be repeated for the chroma coding blocks. As one example, operations to identify a motion vector (MV) and reference picture for a luma coding block need not be repeated for identifying a MV and reference picture for the chroma blocks. Rather, the MV for the luma coding block may be scaled to determine the MV for the chroma blocks, and the reference picture may be the same. As another example, the intra-prediction process may be the same for the luma coding block and the chroma coding blocks. 
       FIG. 7  is a block diagram illustrating an example video decoder  300  that may perform the techniques of this disclosure.  FIG. 7  is provided for purposes of explanation and is not limiting on the techniques as broadly exemplified and described in this disclosure. For purposes of explanation, this disclosure describes video decoder  300  according to the techniques of VVC and HEVC (ITU-T H.265). However, the techniques of this disclosure may be performed by video coding devices that are configured to other video coding standards. 
     In the example of  FIG. 7 , video decoder  300  includes coded picture buffer (CPB) memory  320 , entropy decoding unit  302 , prediction processing unit  304 , inverse quantization unit  306 , inverse transform processing unit  308 , reconstruction unit  310 , filter unit  312 , and decoded picture buffer (DPB)  314 . Any or all of CPB memory  320 , entropy decoding unit  302 , prediction processing unit  304 , inverse quantization unit  306 , inverse transform processing unit  308 , reconstruction unit  310 , filter unit  312 , and DPB  314  may be implemented in one or more processors or in processing circuitry. For instance, the units of video decoder  300  may be implemented as one or more circuits or logic elements as part of hardware circuitry, or as part of a processor, ASIC, or FPGA. Moreover, video decoder  300  may include additional or alternative processors or processing circuitry to perform these and other functions. 
     Prediction processing unit  304  includes motion compensation unit  316  and intra-prediction unit  318 . Prediction processing unit  304  may include additional units to perform prediction in accordance with other prediction modes. As examples, prediction processing unit  304  may include a palette unit, an intra-block copy unit (which may form part of motion compensation unit  316 ), an affine unit, a linear model (LM) unit, or the like. In other examples, video decoder  300  may include more, fewer, or different functional components. 
     CPB memory  320  may store video data, such as an encoded video bitstream, to be decoded by the components of video decoder  300 . The video data stored in CPB memory  320  may be obtained, for example, from computer-readable medium  110  ( FIG. 1 ). CPB memory  320  may include a CPB that stores encoded video data (e.g., syntax elements) from an encoded video bitstream. Also, CPB memory  320  may store video data other than syntax elements of a coded picture, such as temporary data representing outputs from the various units of video decoder  300 . DPB  314  generally stores decoded pictures, which video decoder  300  may output and/or use as reference video data when decoding subsequent data or pictures of the encoded video bitstream. CPB memory  320  and DPB  314  may be formed by any of a variety of memory devices, such as dynamic random access memory (DRAM), including synchronous DRAM (SDRAM), magnetoresistive RAM (MRAM), resistive RAM (RRAM), or other types of memory devices. CPB memory  320  and DPB  314  may be provided by the same memory device or separate memory devices. In various examples, CPB memory  320  may be on-chip with other components of video decoder  300 , or off-chip relative to those components. 
     Additionally or alternatively, in some examples, video decoder  300  may retrieve coded video data from memory  120  ( FIG. 1 ). That is, memory  120  may store data as discussed above with CPB memory  320 . Likewise, memory  120  may store instructions to be executed by video decoder  300 , when some or all of the functionality of video decoder  300  is implemented in software to be executed by processing circuitry of video decoder  300 . 
     The various units shown in  FIG. 7  are illustrated to assist with understanding the operations performed by video decoder  300 . The units may be implemented as fixed-function circuits, programmable circuits, or a combination thereof. Similar to  FIG. 6 , fixed-function circuits refer to circuits that provide particular functionality, and are preset on the operations that can be performed. Programmable circuits refer to circuits that can be programmed to perform various tasks, and provide flexible functionality in the operations that can be performed. For instance, programmable circuits may execute software or firmware that cause the programmable circuits to operate in the manner defined by instructions of the software or firmware. Fixed-function circuits may execute software instructions (e.g., to receive parameters or output parameters), but the types of operations that the fixed-function circuits perform are generally immutable. In some examples, the one or more units may be distinct circuit blocks (fixed-function or programmable), and in some examples, the one or more units may be integrated circuits. 
     Video decoder  300  may include ALUs, EFUs, digital circuits, analog circuits, and/or programmable cores formed from programmable circuits. In examples where the operations of video decoder  300  are performed by software executing on the programmable circuits, on-chip or off-chip memory may store instructions (e.g., object code) of the software that video decoder  300  receives and executes. 
     Entropy decoding unit  302  may receive encoded video data from the CPB and entropy decode the video data to reproduce syntax elements. Prediction processing unit  304 , inverse quantization unit  306 , inverse transform processing unit  308 , reconstruction unit  310 , and filter unit  312  may generate decoded video data based on the syntax elements extracted from the bitstream. 
     In general, video decoder  300  reconstructs a picture on a block-by-block basis. Video decoder  300  may perform a reconstruction operation on each block individually (where the block currently being reconstructed, i.e., decoded, may be referred to as a “current block”). 
     Entropy decoding unit  302  may entropy decode syntax elements defining quantized transform coefficients of a quantized transform coefficient block, as well as transform information, such as a quantization parameter (QP) and/or transform mode indication(s). Inverse quantization unit  306  may use the QP associated with the quantized transform coefficient block to determine a degree of quantization and, likewise, a degree of inverse quantization for inverse quantization unit  306  to apply. Inverse quantization unit  306  may, for example, perform a bitwise left-shift operation to inverse quantize the quantized transform coefficients. Inverse quantization unit  306  may thereby form a transform coefficient block including transform coefficients. 
     After inverse quantization unit  306  forms the transform coefficient block, inverse transform processing unit  308  may apply one or more inverse transforms to the transform coefficient block to generate a residual block associated with the current block. For example, inverse transform processing unit  308  may apply an inverse DCT, an inverse integer transform, an inverse Karhunen-Loeve transform (KLT), an inverse rotational transform, an inverse directional transform, or another inverse transform to the transform coefficient block. 
     Furthermore, prediction processing unit  304  generates a prediction block according to prediction information syntax elements that were entropy decoded by entropy decoding unit  302 . For example, if the prediction information syntax elements indicate that the current block is inter-predicted, motion compensation unit  316  may generate the prediction block. In this case, the prediction information syntax elements may indicate a reference picture in DPB  314  from which to retrieve a reference block, as well as a motion vector identifying a location of the reference block in the reference picture relative to the location of the current block in the current picture. Motion compensation unit  316  may generally perform the inter-prediction process in a manner that is substantially similar to that described with respect to motion compensation unit  224  ( FIG. 6 ). 
     As another example, if the prediction information syntax elements indicate that the current block is intra-predicted, intra-prediction unit  318  may generate the prediction block according to an intra-prediction mode indicated by the prediction information syntax elements. Again, intra-prediction unit  318  may generally perform the intra-prediction process in a manner that is substantially similar to that described with respect to intra-prediction unit  226  ( FIG. 6 ). Intra-prediction unit  318  may retrieve data of neighboring samples to the current block from DPB  314 . 
     Reconstruction unit  310  may reconstruct the current block using the prediction block and the residual block. For example, reconstruction unit  310  may add samples of the residual block to corresponding samples of the prediction block to reconstruct the current block. 
     Filter unit  312  may perform one or more filter operations on reconstructed blocks. For example, filter unit  312  may perform deblocking operations to reduce blockiness artifacts along edges of the reconstructed blocks. Operations of filter unit  312  are not necessarily performed in all examples. For example, video decoder  300  may explicitly or implicitly determine whether to perform neural network model filtering using NN models  322 , e.g., using any or all of the various techniques discussed herein. Moreover, video decoder  300  may explicitly or implicitly determine one or more of NN models  322  and/or a grid size for a current picture to be decoded and filtered. Accordingly, filter unit  312 , when filtering is switched on, may use one or more of NN models  322  to filter a portion of a current decoded picture. 
     In some examples, filter unit  312  may perform deblocking operations to reduce blockiness artifacts along edges of CUs, PUs, or TUs. When performing deblocking operations, filter unit  312  (or a deblocking filter unit thereof) may initially calculate boundary strength values. The deblocking filter unit of filter unit  312  may then use the boundary strength values to determine other deblocking parameters, such as t C  and beta (β), which may generally represent filtering strength and coefficients to be used for deblocking and/or deblocking decision functions. Operations of filter unit  312  may be skipped, in some examples. 
     Filter unit  312  may be configured to perform the various techniques of this disclosure, e.g., to determine one or more of neural network models (NN models)  322  to be used to filter a decoded picture and/or whether to apply NN model filtering. Entropy decoding unit  302  may decode data representing whether or not to perform boundary filtering of blocks of a particular picture, slice, tile, or other unit. 
     Filter unit  312  may receive a decoded (reconstructed) picture from reconstruction unit  310 . Filter unit  312  may also obtain (e.g., receive) additional data from one or more other units, e.g., prediction processing unit  304 , motion compensation unit  316 , intra-prediction unit  318 , inverse transform processing unit  308 , inverse quantization unit  306 , another filtering unit (e.g., separate from or contained within filter unit  312 ), or the like. For example, filter unit  312  may perform both NN-based filtering and other types of filtering, such as deblocking filtering, SAO filtering, ALF filtering, or the like. Filter unit  312  may thus obtain deblocking parameters, such as boundary strength values, for blocks of the current picture. Filter unit  312  may provide the boundary strength values (and/or other received data) to a NN filtering unit of filter unit  312 . 
     The NN filtering unit may use the additional data (e.g., boundary strength values and/or other data received from other units of video decoder  300 ) to select NN models from NN models  322  to be used to perform NN-based filtering and to perform the actual NN-based filtering. For example, the NN filtering unit of filter unit  312  may provide the additional data (e.g., the boundary strength values) to the selected one or more NN models of NN models  322  in the form of additional input layers. In some examples, filter unit  312  may modify the additional data, e.g., by converting representation formats (such as between floating point and decimal) or modifying ranges of the additional values to correspond to input sample ranges. 
     Video decoder  300  may store the reconstructed (and filtered) blocks in DPB  314 . For instance, in examples where operations of filter unit  312  are not performed, reconstruction unit  310  may store reconstructed blocks to DPB  314 . In examples where operations of filter unit  312  are performed, filter unit  312  may store the filtered reconstructed blocks to DPB  314 . As discussed above, DPB  314  may provide reference information, such as samples of a current picture for intra-prediction and previously decoded pictures for subsequent motion compensation, to prediction processing unit  304 . Moreover, video decoder  300  may output decoded pictures from DPB  314  for subsequent presentation on a display device, such as display device  118  of  FIG. 1 . 
       FIG. 8  is a flowchart illustrating an example method for encoding a current block in accordance with the techniques of this disclosure. The current block may comprise a current CU. Although described with respect to video encoder  200  ( FIGS. 1 and 3 ), it should be understood that other devices may be configured to perform a method similar to that of  FIG. 8 . 
     In this example, video encoder  200  initially predicts the current block ( 350 ). For example, video encoder  200  may form a prediction block for the current block. Video encoder  200  may then calculate a residual block for the current block ( 352 ). To calculate the residual block, video encoder  200  may calculate a difference between the original, uncoded block and the prediction block for the current block. Video encoder  200  may then transform and quantize coefficients of the residual block ( 354 ). Next, video encoder  200  may scan the quantized transform coefficients of the residual block ( 356 ). During the scan, or following the scan, video encoder  200  may entropy encode the coefficients ( 358 ). For example, video encoder  200  may encode the coefficients using CAVLC or CABAC. Video encoder  200  may then output the entropy encoded data of the block ( 360 ). 
     Video encoder  200  may also decode the current block after encoding the current block, to use the decoded version of the current block as reference data for subsequently coded data (e.g., in inter- or intra-prediction modes). Thus, video encoder  200  may inverse quantize and inverse transform the coefficients to reproduce the residual block ( 362 ). Video encoder  200  may combine the residual block with the prediction block to form a decoded block ( 364 ). Video encoder  200  may decode all blocks of a current picture in this manner, thereby forming a fully decoded picture. Video encoder  200  may further filter the decoded picture including the decoded blocks according to any of the various techniques of this disclosure ( 366 ). Video encoder  200  may then store the decoded picture in DPB  218  ( 368 ). 
       FIG. 9  is a flowchart illustrating an example method for decoding a current block in accordance with the techniques of this disclosure. The current block may comprise a current CU. Although described with respect to video decoder  300  ( FIGS. 1 and 4 ), it should be understood that other devices may be configured to perform a method similar to that of  FIG. 9 . 
     Video decoder  300  may receive entropy encoded data for the current block, such as entropy encoded prediction information and entropy encoded data for coefficients of a residual block corresponding to the current block ( 370 ). Video decoder  300  may entropy decode the entropy encoded data to determine prediction information for the current block and to reproduce coefficients of the residual block ( 372 ). Video decoder  300  may predict the current block ( 374 ), e.g., using an intra- or inter-prediction mode as indicated by the prediction information for the current block, to calculate a prediction block for the current block. Video decoder  300  may then inverse scan the reproduced coefficients ( 376 ), to create a block of quantized transform coefficients. Video decoder  300  may then inverse quantize and inverse transform the quantized transform coefficients to produce a residual block ( 378 ). Video decoder  300  may ultimately decode the current block by combining the prediction block and the residual block ( 380 ). Video decoder  300  may also filter the decoded video data ( 382 ), e.g., using one or more NN models as discussed above according to the techniques of this disclosure. Video decoder  300  may further store the (filtered) decoded video data ( 384 ), e.g., in DPB  314 . 
       FIG. 10  is a flowchart illustrating an example method of filtering decoded video data according to the techniques of this disclosure. The method of  FIG. 10  may be performed by video encoder  200  or video decoder  300 . For example, the method of  FIG. 10  may be performed by a neural network (NN) filtering unit of filter unit  216  of video encoder  200 , e.g., during step  366  of the method of  FIG. 8 . As another example, the method of  FIG. 10  may be performed by a NN filtering unit of filter unit  312  of video decoder  300 , e.g., during step  382  of the method of  FIG. 9 . For purposes of example and explanation, the method of  FIG. 10  is explained with respect to video decoder  300 , and in particular, a NN filtering unit of filter unit  312  of video decoder  300 . 
     Initially, the NN filtering unit of filter unit  312  receives decoded video data ( 400 ). The decoded video data may be at least a portion of a picture, e.g., a set of blocks, a tile, a slice, one or more slices, one or more tiles, a sub-picture, or an entire picture. 
     Although not shown in the example of  FIG. 10 , a deblocking filter and/or other filtering unit of filter unit  312 , such as an SAO filter, ALF filter, and/or another NN filtering unit may initially filter the decoded video data. Thus, the received decoded video data may have been previously filtered (e.g., deblocked, SAO filtered, ALF filtered, and/or NN filtered) prior to reception by the NN filtering unit performing the method of  FIG. 10  as discussed herein. As such, the term “decoded video data” should be understood to include filtered, decoded video data (which may include, e.g., deblocked, decoded video data). 
     Accordingly, the at least portion may include multiple blocks including deblocked edges, e.g., deblocked by a deblocking filter of filter unit  312 . The deblocking filter may calculate boundary strength values for boundaries between the blocks. The boundary strength values may generally represent whether and to what degree samples near the block boundaries are modified by a deblocking filter. 
     The NN filtering unit of filter unit  312  may receive additional data from one or more other units of video decoder  300  ( 402 ). For example, the NN filtering unit of filter unit  312  may receive the boundary strength values from the deblocking filter of filter unit  312 . The NN filtering unit of filter unit  312  may, additionally or alternatively, receive other data from other units, such as coding unit (CU) partitioning data, prediction unit (PU) partitioning data, transform unit (TU) partitioning data, deblocking filtering data, quantization parameter (QP) data, intra-prediction data, inter-prediction data, data representing distance (e.g., POC distance) between the decoded picture and one or more reference pictures, or motion information for one or more decoded blocks of the decoded picture. 
     The NN filtering unit of filter unit  312  may then determine one or more neural network (NN) models of NN models  322  to be used for filtering the at least portion of the current picture ( 404 ). The NN filtering unit of filter unit  312  may then use the received additional data and the determined one or more NN models to filter the at least portion of the current picture ( 406 ). In some examples, the NN filtering unit of filter unit  312  may modify the additional data, e.g., by adjusting a range for values of the additional data to conform to a range of input sample values and/or by converting the values of the additional data to a different format, such as integer or floating point. The NN filtering unit of filter unit  312  may convert the additional data to one or more input planes, similar to the luminance and/or chrominance planes to be filtered. 
     In this manner, the method of  FIG. 10  represents an example of a method of filtering decoded video data, including receiving, by a neural network filtering unit of a video decoding device, data for a decoded picture of video data; receiving, by the neural network filtering unit, data from one or more other units of the video decoding device, the data from the one or more other units being different than the data for the decoded picture, and wherein receiving the data from the one or more other units comprises receiving boundary strength data from a deblocking unit; determining, by the neural network filtering unit, one or more neural network models to be used to filter a portion of the decoded picture; and filtering, by the neural network filtering unit, the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device, including the boundary strength data. 
     Certain examples of the techniques of this disclosure are summarized in the following clauses: 
     Clause 1: A method of filtering decoded video data, the method comprising: receiving, by a filtering unit of a video decoding device, data from one or more other units of the video decoding device; determining, by the filtering unit, one or more neural network models to be used to filter a portion of a decoded picture of video data; and filtering, by the filtering unit, the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device. 
     Clause 2: The method of clause 1, wherein receiving the data from the one or more other units comprises receiving the data from one or more of: an intra-prediction unit of the video decoding device; an inter-prediction unit of the video decoding device; a transform processing unit of the video decoding device; a quantization unit of the video decoding device; a loop filter unit of the video decoding device; a pre-processing unit of the video decoding device; or a second filtering unit of the video decoding device. 
     Clause 3: The method of clause 2, wherein the filtering unit comprises a first neural network based filtering unit. 
     Clause 4: The method of any of clauses 2 and 3, wherein the loop filter unit comprises at least one of a deblocking filtering unit, a sample adaptive offset (SAO) filtering unit, or an adaptive loop filtering (ALF) unit. 
     Clause 5: The method of any of clauses 1-4, wherein receiving the data comprises receiving one or more of coding unit (CU) partitioning data, prediction unit (PU) partitioning data, transform unit (TU) partitioning data, deblocking filtering data, quantization parameter (QP) data, intra-prediction data, inter-prediction data, data representing distance between the decoded picture and one or more reference pictures, or motion information for one or more decoded blocks of the decoded picture. 
     Clause 6: The method of clause 5, wherein the deblocking filtering data includes one or more of boundary strength values, whether long or short filters were used for deblocking, or whether strong or weak filters were used for deblocking. 
     Clause 7: The method of any of clauses 5 and 6, wherein the intra-prediction data includes an intra-prediction mode. 
     Clause 8: The method of any of clauses 5-7, wherein the data representing the distance comprises data representing a difference between a picture order count (POC) value for the decoded picture and a POC value for a reference picture used to predict a block of the decoded picture. 
     Clause 9: The method of any of clauses 1-8, further comprising performing functionality attributed to one or more of the other units by the filtering unit. 
     Clause 10: The method of any of clauses 1-9, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises providing the data from the one or more other units of the video decoding device as one or more additional input planes to a convolutional neural network (CNN). 
     Clause 11: The method of any of clauses 1-10, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises adjusting output of the one or more neural network models using the data from the one or more other units of the video decoding device. 
     Clause 12: The method of any of clauses 1-11, further comprising adjusting the data from the one or more other units of the video decoding device prior to filtering the portion of the decoded picture. 
     Clause 13: The method of clause 12, wherein adjusting the data comprises converting values of the data between integer representation and floating point representation. 
     Clause 14: The method of any of clauses 12 and 13, wherein adjusting the data comprises scaling values of the data to be within a range of values suitable for the one or more neural network models. 
     Clause 15: The method of any of clauses 1-14, wherein receiving the data comprises receiving partition data for the decoded picture, and wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises: setting values at positions in an input plane collocated with positions of boundary samples defining partition boundaries in the decoded picture, as indicated by the partition data, to a first value; setting values at positions in the input plane collocated with positions of internal samples that are non-boundary samples to a second value; and filtering the portion of the decoded picture using the input plane as an input to at least one of the one or more neural network models. 
     Clause 16: The method of clause 15, wherein the first value comprises 1 and the second value comprises 0. 
     Clause 17: The method of any of clauses 15 and 16, wherein the partition data comprises coding unit (CU) partition data and the input plane comprises a first partition plane, the method further comprising: receiving prediction unit (PU) partition data; forming a second input plane using the PU partition data; receiving transform unit (TU) partition data; and forming a third input plane using the TU partition data, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises filtering the portion of the decoded picture using the first input plane, the second input plane, and the third input plane as inputs to at least one of the one or more neural network models. 
     Clause 18: The method of any of clauses 1-14, wherein receiving the data comprises receiving deblocking filter data for the decoded picture, and wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises: converting the deblocking filter data for the decoded picture to one or more input planes for at least one of the one or more neural network models; and filtering the portion of the decoded picture using the one or more input planes as inputs to the at least one of the one or more neural network models. 
     Clause 19: The method of clause 18, wherein the deblocking filter data comprises one or more of boundary strength data, long or short filter data, or strong or weak filter data. 
     Clause 20: The method of any of clauses 10 or 15-19, further comprising upsampling or downsampling data of the input plane(s). 
     Clause 21: The method of any of clauses 1-20, further comprising: encoding a current picture; and decoding the current picture to form the decoded picture. 
     Clause 22: The method of clause 21, wherein determining comprises determining according to a rate-distortion computation. 
     Clause 23: A device for filtering decoded video data, the device comprising one or more means for performing the method of any of clauses 1-22. 
     Clause 24: The device of clause 23, wherein the one or more means comprise one or more processors implemented in circuitry. 
     Clause 25: The device of clause 23, further comprising a display configured to display the decoded video data. 
     Clause 26: The device of clause 23, wherein the device comprises one or more of a camera, a computer, a mobile device, a broadcast receiver device, or a set-top box. 
     Clause 27: The device of clause 23, further comprising a memory configured to store the video data. 
     Clause 28: A computer-readable storage medium having stored thereon instructions that, when executed, cause a processor to perform the method of any of clauses 1-22. 
     Clause 29: A device for filtering decoded video data, the device comprising a filtering unit comprising: means for receiving data from one or more units other than the filtering unit of the video decoding device; means for determining one or more neural network models to be used to filter a portion of a decoded picture of video data; and means for filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device. 
     Clause 30: A method of filtering decoded video data, the method comprising: receiving, by a neural network filtering unit of a video decoding device, data for a decoded picture of video data; receiving, by the neural network filtering unit, data from one or more other units of the video decoding device, the data from the one or more other units being different than the data for the decoded picture, and wherein receiving the data from the one or more other units of the video decoding device comprises receiving boundary strength data from a deblocking unit of the video decoding device; determining, by the neural network filtering unit, one or more neural network models to be used to filter a portion of the decoded picture; and filtering, by the neural network filtering unit, the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device, including the boundary strength data. 
     Clause 31: The method of clause 30, wherein receiving the data from the one or more other units of the video decoding device further comprises receiving the data from one or more of: an intra-prediction unit of the video decoding device; an inter-prediction unit of the video decoding device; a transform processing unit of the video decoding device; a quantization unit of the video decoding device; a loop filter unit of the video decoding device; a pre-processing unit of the video decoding device; or a second neural network filtering unit of the video decoding device. 
     Clause 32: The method of clause 31, wherein the loop filter unit comprises at least one of a sample adaptive offset (SAO) filtering unit or an adaptive loop filtering (ALF) unit. 
     Clause 33: The method of clause 32, wherein receiving the data further comprises receiving one or more of coding unit (CU) partitioning data, prediction unit (PU) partitioning data, transform unit (TU) partitioning data, deblocking filtering data, quantization parameter (QP) data, intra-prediction data, inter-prediction data, data representing distance between the decoded picture and one or more reference pictures, or motion information for one or more decoded blocks of the decoded picture. 
     Clause 34: The method of clause 33, wherein the deblocking filtering data includes one or more of whether long or short filters were used for deblocking or whether strong or weak filters were used for deblocking. 
     Clause 35: The method of clause 33, wherein the intra-prediction data includes an intra-prediction mode. 
     Clause 36: The method of clause 33, wherein the data representing the distance comprises data representing a difference between a picture order count (POC) value for the decoded picture and a POC value for a reference picture used to predict a block of the decoded picture. 
     Clause 37: The method of clause 30, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises providing the data from the one or more other units of the video decoding device as one or more additional input planes to a convolutional neural network (CNN). 
     Clause 38: The method of clause 30, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises adjusting output of the one or more neural network models using the data from the one or more other units of the video decoding device. 
     Clause 39: The method of clause 30, further comprising adjusting the data from the one or more other units of the video decoding device prior to filtering the portion of the decoded picture. 
     Clause 40: The method of clause 39, wherein adjusting the data comprises converting values of the data between integer representation and floating point representation. 
     Clause 41: The method of clause 39, wherein adjusting the data comprises scaling values of the data to be within a range of values suitable for the one or more neural network models. 
     Clause 42: The method of clause 30, wherein receiving the data comprises receiving partition data for the decoded picture, and wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises: setting values at positions in an input plane collocated with positions of boundary samples defining partition boundaries in the decoded picture, as indicated by the partition data, to a first value; setting values at positions in the input plane collocated with positions of internal samples that are non-boundary samples to a second value; and filtering the portion of the decoded picture using the input plane as an input to at least one of the one or more neural network models. 
     Clause 43: The method of clause 42, wherein the first value comprises 1 and the second value comprises 0. 
     Clause 44: The method of clause 42, wherein the partition data comprises coding unit (CU) partition data and the input plane comprises a first partition plane, the method further comprising: receiving prediction unit (PU) partition data; forming a second input plane using the PU partition data; receiving transform unit (TU) partition data; and forming a third input plane using the TU partition data, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises filtering the portion of the decoded picture using the first input plane, the second input plane, and the third input plane as inputs to at least one of the one or more neural network models. 
     Clause 45: The method of clause 30, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises: converting deblocking filter data for the decoded picture from the deblocking unit to one or more input planes for at least one of the one or more neural network models; and filtering the portion of the decoded picture using the one or more input planes as inputs to the at least one of the one or more neural network models. 
     Clause 46: The method of clause 30, further comprising: encoding a current picture; and decoding the current picture to form the decoded picture. 
     Clause 47: The method of clause 46, wherein determining the one or more neural network models comprises determining the one or more neural network models according to a rate-distortion computation. 
     Clause 48: A device for filtering decoded video data, the device comprising: a memory configured to store a decoded picture of video data; and one or more processors implemented in circuitry and configured to execute a neural network filtering unit to: receive data from one or more other units of the device, the data from the one or more other units of the device being different than data for the decoded picture, and wherein to receive the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to receive boundary strength data from a deblocking unit of the device; determine one or more neural network models to be used to filter a portion of the decoded picture; and filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, including the boundary strength data. 
     Clause 49: The device of clause 48, wherein to receive the data from the one or more other units of the device, the one or more processors are further configured to execute the neural network filtering unit to receive data from one or more of: an intra-prediction unit of the device; an inter-prediction unit of the device; a transform processing unit of the device; a quantization unit of the device; a loop filter unit of the device; a pre-processing unit of the device; or a second neural network filtering unit of the device. 
     Clause 50: The device of clause 48, wherein to receive the data from the one or more other units of the device, the one or more processors are further configured to execute the neural network filtering unit to receive one or more of coding unit (CU) partitioning data, prediction unit (PU) partitioning data, transform unit (TU) partitioning data, deblocking filtering data, quantization parameter (QP) data, intra-prediction data, inter-prediction data, data representing distance between the decoded picture and one or more reference pictures, or motion information for one or more decoded blocks of the decoded picture. 
     Clause 51: The device of clause 48, wherein to filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to provide the data from the one or more other units of the device as one or more additional input planes to a convolutional neural network (CNN). 
     Clause 52: The device of clause 48, wherein to filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to adjust output of the one or more neural network models using the data from the one or more other units of the device. 
     Clause 53: The device of clause 48, wherein to filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to adjust the data from the one or more other units of the device prior to filtering the portion of the decoded picture. 
     Clause 54: The device of clause 48, wherein to filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to: convert deblocking filter data for the decoded picture from the deblocking unit to one or more input planes for at least one of the one or more neural network models; and filter the portion of the decoded picture using the one or more input planes as inputs to the at least one of the one or more neural network models. 
     Clause 55: The device of clause 48, further comprising a display configured to display the decoded picture of the video data. 
     Clause 56: The device of clause 48, wherein the device comprises one or more of a camera, a computer, a mobile device, a broadcast receiver device, or a set-top box. 
     Clause 57: A computer-readable storage medium having stored thereon instructions that, when executed, cause a processor of a video decoding device to execute a neural network filtering unit to: receive data for a decoded picture of video data; receive data from one or more other units of the video decoding device, the data from the one or more other units of the video decoding device being different than the data for the decoded picture, and wherein the instructions that cause the processor to receive the data from the one or more other units of the video decoding device comprise instructions that cause the processor to receive boundary strength data from a deblocking unit of the video decoding device; determine one or more neural network models to be used to filter a portion of the decoded picture; and filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device, including the boundary strength data. 
     Clause 58: A device for filtering decoded video data, the device comprising a filtering unit comprising: means for receiving data for a decoded picture of video data; means for receiving data from one or more other units of the video decoding device, the data from the one or more other units being different than the data for the decoded picture, and wherein the means for receiving the data from the one or more other units of the video decoding device comprises means for receiving boundary strength data from a deblocking unit of the video decoding device; means for determining one or more neural network models to be used to filter a portion of the decoded picture; and means for filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device, including the boundary strength data. 
     Clause 59: A method of filtering decoded video data, the method comprising: receiving, by a neural network filtering unit of a video decoding device, data for a decoded picture of video data; receiving, by the neural network filtering unit, data from one or more other units of the video decoding device, the data from the one or more other units being different than the data for the decoded picture, and wherein receiving the data from the one or more other units of the video decoding device comprises receiving boundary strength data from a deblocking unit of the video decoding device; determining, by the neural network filtering unit, one or more neural network models to be used to filter a portion of the decoded picture; and filtering, by the neural network filtering unit, the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device, including the boundary strength data. 
     Clause 60: The method of clause 59, wherein receiving the data from the one or more other units of the video decoding device further comprises receiving the data from one or more of: an intra-prediction unit of the video decoding device; an inter-prediction unit of the video decoding device; a transform processing unit of the video decoding device; a quantization unit of the video decoding device; a loop filter unit of the video decoding device; a pre-processing unit of the video decoding device; or a second neural network filtering unit of the video decoding device. 
     Clause 61: The method of clause 60, wherein the loop filter unit comprises at least one of a sample adaptive offset (SAO) filtering unit or an adaptive loop filtering (ALF) unit. 
     Clause 62: The method of any of clauses 59-61, wherein receiving the data further comprises receiving one or more of coding unit (CU) partitioning data, prediction unit (PU) partitioning data, transform unit (TU) partitioning data, deblocking filtering data, quantization parameter (QP) data, intra-prediction data, inter-prediction data, data representing distance between the decoded picture and one or more reference pictures, or motion information for one or more decoded blocks of the decoded picture. 
     Clause 63: The method of clause 62, wherein the deblocking filtering data includes one or more of whether long or short filters were used for deblocking or whether strong or weak filters were used for deblocking. 
     Clause 64: The method of any of clauses 62 and 63, wherein the intra-prediction data includes an intra-prediction mode. 
     Clause 65: The method of any of clauses 62-64, wherein the data representing the distance comprises data representing a difference between a picture order count (POC) value for the decoded picture and a POC value for a reference picture used to predict a block of the decoded picture. 
     Clause 66: The method of any of clauses 59-65, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises providing the data from the one or more other units of the video decoding device as one or more additional input planes to a convolutional neural network (CNN). 
     Clause 67: The method of any of clauses 59-66, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises adjusting output of the one or more neural network models using the data from the one or more other units of the video decoding device. 
     Clause 68: The method of any of clauses 59-67, further comprising adjusting the data from the one or more other units of the video decoding device prior to filtering the portion of the decoded picture. 
     Clause 69: The method of clause 68, wherein adjusting the data comprises converting values of the data between integer representation and floating point representation. 
     Clause 70: The method of any of clauses 68 and 69, wherein adjusting the data comprises scaling values of the data to be within a range of values suitable for the one or more neural network models. 
     Clause 71: The method of any of clauses 59-70, wherein receiving the data comprises receiving partition data for the decoded picture, and wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises: setting values at positions in an input plane collocated with positions of boundary samples defining partition boundaries in the decoded picture, as indicated by the partition data, to a first value; setting values at positions in the input plane collocated with positions of internal samples that are non-boundary samples to a second value; and filtering the portion of the decoded picture using the input plane as an input to at least one of the one or more neural network models. 
     Clause 72: The method of clause 71, wherein the first value comprises 1 and the second value comprises 0. 
     Clause 73: The method of any of clauses 71 and 72, wherein the partition data comprises coding unit (CU) partition data and the input plane comprises a first partition plane, the method further comprising: receiving prediction unit (PU) partition data; forming a second input plane using the PU partition data; receiving transform unit (TU) partition data; and forming a third input plane using the TU partition data, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises filtering the portion of the decoded picture using the first input plane, the second input plane, and the third input plane as inputs to at least one of the one or more neural network models. 
     Clause 74: The method of any of clauses 59-73, wherein filtering the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the video decoding device comprises: converting deblocking filter data for the decoded picture from the deblocking unit to one or more input planes for at least one of the one or more neural network models; and filtering the portion of the decoded picture using the one or more input planes as inputs to the at least one of the one or more neural network models. 
     Clause 75: The method of any of clauses 59-74, further comprising: encoding a current picture; and decoding the current picture to form the decoded picture. 
     Clause 76: The method of any of clauses 59-75, wherein determining the one or more neural network models comprises determining the one or more neural network models according to a rate-distortion computation. 
     Clause 77: A device for filtering decoded video data, the device comprising: a memory configured to store a decoded picture of video data; and one or more processors implemented in circuitry and configured to execute a neural network filtering unit to: receive data from one or more other units of the device, the data from the one or more other units of the device being different than data for the decoded picture, and wherein to receive the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to receive boundary strength data from a deblocking unit of the device; determine one or more neural network models to be used to filter a portion of the decoded picture; and filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, including the boundary strength data. 
     Clause 78: The device of clause 77, wherein to receive the data from the one or more other units of the device, the one or more processors are further configured to execute the neural network filtering unit to receive data from one or more of: an intra-prediction unit of the device; an inter-prediction unit of the device; a transform processing unit of the device; a quantization unit of the device; a loop filter unit of the device; a pre-processing unit of the device; or a second neural network filtering unit of the device. 
     Clause 79: The device of any of clauses 77 and 78, wherein to receive the data from the one or more other units of the device, the one or more processors are further configured to execute the neural network filtering unit to receive one or more of coding unit (CU) partitioning data, prediction unit (PU) partitioning data, transform unit (TU) partitioning data, deblocking filtering data, quantization parameter (QP) data, intra-prediction data, inter-prediction data, data representing distance between the decoded picture and one or more reference pictures, or motion information for one or more decoded blocks of the decoded picture. 
     Clause 80: The device of any of clauses 77-79, wherein to filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to provide the data from the one or more other units of the device as one or more additional input planes to a convolutional neural network (CNN). 
     Clause 81: The device of any of clauses 77-80, wherein to filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to adjust output of the one or more neural network models using the data from the one or more other units of the device. 
     Clause 82: The device of any of clauses 77-81, wherein to filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to adjust the data from the one or more other units of the device prior to filtering the portion of the decoded picture. 
     Clause 83: The device of any of clauses 77-82, wherein to filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to: convert deblocking filter data for the decoded picture from the deblocking unit to one or more input planes for at least one of the one or more neural network models; and filter the portion of the decoded picture using the one or more input planes as inputs to the at least one of the one or more neural network models. 
     Clause 84: The device of any of clauses 77-83, further comprising a display configured to display the decoded picture of the video data. 
     Clause 85: The device of any of clauses 77-84, wherein the device comprises one or more of a camera, a computer, a mobile device, a broadcast receiver device, or a set-top box. 
     It is to be recognized that depending on the example, certain acts or events of any of the techniques described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the techniques). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially. 
     In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media, or communication media including any medium that facilitates transfer of a computer program from one place to another, e.g., according to a communication protocol. In this manner, computer-readable media generally may correspond to (1) tangible computer-readable storage media which is non-transitory or (2) a communication medium such as a signal or carrier wave. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementation of the techniques described in this disclosure. A computer program product may include a computer-readable medium. 
     By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage, or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, but are instead directed to non-transitory, tangible storage media. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. 
     Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the terms “processor” and “processing circuitry,” as used herein may refer to any of the foregoing structures or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules configured for encoding and decoding, or incorporated in a combined codec. Also, the techniques could be fully implemented in one or more circuits or logic elements. 
     The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC) or a set of ICs (e.g., a chip set). Various components, modules, or units are described in this disclosure to emphasize functional aspects of devices configured to perform the disclosed techniques, but do not necessarily require realization by different hardware units. Rather, as described above, various units may be combined in a codec hardware unit or provided by a collection of interoperative hardware units, including one or more processors as described above, in conjunction with suitable software and/or firmware. 
     Various examples have been described. These and other examples are within the scope of the following claims.