Abstract: An example device applies a primary transform to a current block of video data to create primary transform coefficients. The device determines whether intra sub-partitioning is applied to the current block of video data. The device applies a primary transform to the current block. The device also determines whether a primary transform size for the current block of video data is at least a predetermined size. Based on intra sub-partitioning being applied and the primary transform size being at least the predetermined size, the device applies a secondary transform to primary transform coefficients and codes the current block of video data based on the secondary transform.

Abstract: A device may perform a first prediction process for a first block of video data to produce a first residual. The device may apply a first transform process to the first residual to generate first transform coefficients for the first block of video data and encode the first transform coefficients. The device may perform a second prediction process for a second block of video data to produce a second residual. The device may determine that a second transform process, which includes the first transform process and at least one of a pre-adjustment operation or a post-adjustment operation, is to be applied to the second residual. The device may apply the first transform process and the pre- or post-adjustment operation to the second residual to generate second transform coefficients for the second block. The coding device may code the first and second transform coefficients.

Abstract: An example device applies a primary transform to a current block of video data to create primary transform coefficients. The device determines whether intra sub-partitioning is applied to the current block of video data. The device applies a primary transform to the current block. The device also determines whether a primary transform size for the current block of video data is at least a predetermined size. Based on intra sub-partitioning being applied and the primary transform size being at least the predetermined size, the device applies a secondary transform to primary transform coefficients and codes the current block of video data based on the secondary transform.

Abstract: An example video codec includes memory configured to store the video data and one or more processors implemented in circuitry and communicatively coupled to the memory. The one or more processors are configured to determine that a current mode of coding a current block of the video data is a single tree partitioning mode. Based on the current mode being the single tree partitioning mode, the one or more processors are configured to refrain from determining whether there is a non-DC coefficient for a chroma component of a transform unit (TU) for the current block and refrain from coding a low-frequency non-separable transformation (LFNST) index in response to the refraining of the determination of whether there is the non-DC coefficient. The one or more processors are configured to code the current block in the single partitioning mode with LFNST disabled.

Abstract: An example method includes inferring, for a current transform block of a current video block, a transform type from a plurality of transform types that includes one or more discrete cosine transforms (DCTs) and one or more discrete sine transforms (DSTs), wherein inferring the transform type comprises: determining a size of the current transform block; determining whether the current video block is partitioned using intra-subblock partitioning (ISP); and responsive to determining that the size of the current transform block is less than a threshold and that the current video block is partitioned using ISP, selecting a particular DST of the one or more DSTs as the selected transform type; transforming, using the selected transform type, the current transform block to obtain a block of reconstructed residual data for the video block; and reconstructing, based on the reconstructed residual data for the video block, the video block.

Abstract: Systems and techniques are described herein for processing video data using a neural network system. For instance, a process can include generating, by a first convolutional layer of an encoder sub-network of the neural network system, output values associated with a luminance channel of a frame. The process can include generating, by a second convolutional layer of the encoder sub-network, output values associated with at least one chrominance channel of the frame. The process can include generating a combined representation of the frame by combining the output values associated with the luminance channel of the frame and the output values associated with the at least one chrominance channel of the frame. The process can include generating encoded video data based on the combined representation of the frame.

Abstract: A method of decoding video data includes determining an intra prediction mode from a plurality of intra prediction modes for a current block of the video data, determining a low frequency non-separable transform (LFNST) kernel from a plurality of LFNST kernels for the current block based on the determined intra prediction mode, wherein at least one LFNST kernel of the plurality of LFNST kernels is associated with at least two different intra prediction modes of the plurality of intra prediction modes, applying an inverse of the determined LFNST kernel to coefficient values generated from a transform unit (TU) of the current block to generate intermediate values, applying an inverse primary transform on the intermediate values to generate residual data, and reconstructing the current block based on the residual data.

Abstract: An example device for decoding video data includes a memory configured to store video data; and one or more processors implemented in circuitry and configured to: determine that a transform block of video data has a size of 8×8 coefficients and that the transform block is transformed using a low-frequency non-separable transform (LFNST); decode at least nine non-zero transform coefficients of the transform block; inverse transform the transform block using an inverse LFNST to reproduce a residual block corresponding to the transform block; and reconstruct a block of the video data using the residual block.

Abstract: An example device includes memory configured to store video data and one or more processors implemented in circuitry and coupled to the memory. The one or more processors determine whether a chroma block of the video data is encoded using dual tree partitioning. The one or more processors determine whether transform skip mode for the chroma block is enabled. The one or more processors, based on the chroma block being encoded using dual tree partitioning and transform skip mode being enabled for the chroma block, infer a value of a low-frequency non-separable transform (LFNST) index for the chroma block.

Abstract: The present invention, with respect to a method of processing video data, provides a method of processing video data, provides a method characterized by comprising the steps of: performing a clustering for the video data; generating at least one data cluster as a result of the clustering; generating at least one Graph laplacian matrix corresponding to the at least one data cluster; performing conversion optimization on the basis of multiple graph-based models, wherein the multiple graph-based models respectively include at least one graph laplacian matrix; and generating an optimized conversion matrix according to the results of performing the conversion optimization.

Abstract: A video decoder can be configured to receive, in a syntax structure that applies to a current block, graph-related information; determine a transform matrix based on the received graph-related syntax information; perform an inverse transform based on the determined transform matrix of one or more coefficient values to generate a residual block; and reconstruct the current block of the video data based on the residual block.

Abstract: Systems and techniques are described herein for processing video data. In some aspects, a method can include obtain, by a machine learning system, input video data. The input video data includes one or more luminance components for a current frame. The method can include determining, by the machine learning system, motion information for the luminance component(s) of the current frame and motion information for one or more chrominance components of the current frame using the luminance component(s) for the current frame. In some cases, the method can include determining the motion information for the luminance component(s) based on the luma component(s) of the current frame and at least one reconstructed luma component of a previous frame. In some cases, the method can further include determining the motion information for the chrominance component(s) of the current frame using the motion information determined for the luminance component(s) of the current frame.

Abstract: A video coder is configured to code an adaptive loop filter (ALF) clipping index as a fixed-length unsigned integer. The video coder may apply, based on the ALF clipping index, an ALF to a block of a picture of the video data.

Abstract: A video coder may be configured to code video data by performing adaptive loop filtering processes. A video coder may decode a syntax element that indicates a manner in which to perform an adaptive loop filtering process, the syntax element indicating that a nonlinear operation is to be applied to the video data, where the nonlinear operation is configured to be applied in combination with a filtering operation of the adaptive loop filtering process. In addition, the video coder may perform the adaptive loop filtering process based on the syntax element.

Abstract: A video encoder and video decoder may code video data using intra prediction and a block-based delta pulse code modulation (BDPCM) mode. The BDPCM mode may include a vertical mode and a horizontal mode. The video encoder and video decoder may be configured to align the direction of an intra prediction mode to the direction of a BDPCM mode for both luma and chroma blocks.

Abstract: Techniques are described herein for processing video data using a neural network system. For instance, a process can include generating, by a first convolutional layer of an encoder sub-network of the neural network system, output values associated with a luminance channel of a frame. The process can include generating, by a second convolutional layer of the encoder sub-network, output values associated with at least one chrominance channel of the frame. The process can include generating, by a third convolutional layer based on the output values associated with the luminance channel of the frame and the output values associated with the at least one chrominance channel of the frame, a combined representation of the frame. The process can further include generating encoded video data based on the combined representation of the frame.

Abstract: A video decoder configured to set a context index variable to a first value, wherein the first value for the context index variable is associated with a first context; context decode a first bin for a syntax element indicating a transform using the first context; determine a new value for the context index variable based on a value of the first bin; context decode a second bin for the syntax element indicating the transform using a context associated with the new value; determine an inverse transform from a set of inverse transform candidates based on the first bin and the second bin; and apply the inverse transform to a set of coefficients to determine a block of residual data.

Abstract: A video decoder may receive, in a bitstream that comprises an encoded representation of video data, information indicating whether a residual block is partitioned and information indicating a partition tree type for the residual block based on the residual block being partitioned, wherein the residual block is indicative of a difference between a current block and a prediction block. The video decoder may determine, based on the received information that the residual block is partitioned and the partition tree type for the residual block, a plurality of residual sub-blocks into which the residual block is partitioned according to the partition tree type. The video decoder may produce the residual data for the current block based at least in part on the residual block being partitioned according to the partition tree type into the plurality of residual sub-blocks and may decode the current block using the residual data.

Abstract: A video decoder can be configured to determine a number of allowed non-zero coefficients for a block of video data based on a size of the block; obtain a set of dequantized coefficients for the block, wherein the set of dequantized coefficients comprises a first subset of dequantized coefficients that includes non-zero dequantized coefficients and a second subset of dequantized coefficients that includes all zero coefficients, wherein a number of coefficients in the first subset of dequantized coefficients is equal to the number of allowed non-zero coefficients for the block of video data; apply an inverse low-frequency non-separable transform (LFNST) to the first subset of dequantized coefficients to determine a first intermediate subset of coefficients; and apply an inverse separable transform to the first intermediate subset of coefficients and at least a portion of the second subset of coefficients to determine a block of reconstructed residual values.

Abstract: A device for decoding video data includes a memory configured to store video data and one or more processors implemented in circuitry and configured to store sets of adaptive loop filter (ALF) parameters in a one-dimensional array in the memory, the one-dimensional array having a predefined size of N memory elements, N being a positive integer value, wherein the one or more processors are configured to store, in one or more of the memory elements of the array, both corresponding ALF parameters and a temporal layer identifier (ID) value indicating a temporal layer from which the corresponding ALF parameters are to be estimated; decode one or more blocks of the video data; and filter the one or more blocks using the ALF parameters of the one-dimensional array. The device may further encode the one or more blocks prior to decoding the one or more blocks.