A device includes a first bitstream engine and a second bitstream engine. The first bitstream engine is configured to decode a first portion of a first video frame of a plurality of video frames to generate first decoded portion data. The first bitstream engine is also configured to generate synchronization information based on completion of decoding the first portion. The second bitstream engine is configured to, based on the synchronization information, initiate decoding of a second portion of a particular video frame to generate second decoded portion data. The second bitstream engine uses the first decoded portion data during decoding of the second portion of the particular video frame. The particular video frame includes the first video frame or a second video frame of the plurality of video frames.

A device includes a first bitstream engine and a second bitstream engine. The first bitstream engine is configured to decode a first portion of a first video frame of a plurality of video frames to generate first decoded portion data. The first bitstream engine is also configured to generate synchronization information based on completion of decoding the first portion. The second bitstream engine is configured to, based on the synchronization information, initiate decoding of a second portion of a particular video frame to generate second decoded portion data. The second bitstream engine uses the first decoded portion data during decoding of the second portion of the particular video frame. The particular video frame includes the first video frame or a second video frame of the plurality of video frames.

Embodiments include methods for image compression and decompression. A sending computing device may determine a type of packing used for a chunk of image data, generate metadata describing the type of packing used for the chunk of image data, pack the chunk of image data according to the determined type of packing, and send the packed chunk of image data and the metadata to a second computing device. A receiving computing device may decode the metadata describing the type of packing used for the chunk of image data, determine the type of packing used for the chunk of image data based on the decoded metadata, and unpack the chunk of image data according to the determined type of packing used for the chunk of image data.

A hardware-friendly transform method in codecs for plenoptic point clouds. Given that existing video-based point cloud compression codec (V-PCC) is based on multimedia processor video codecs embedded in System-on-Chip (SoC) mobile devices, the remaining V-PCC steps should be as efficient as possible to ensure fair power consumption. In this sense, the method seeks to reduce the complexity of the transform, using integer transforms and imposing limits on the number of distinct transform dimensions, in which these limits are designed in order to minimize the losses of coding efficiency.

A method, apparatus and storage medium for performing video coding are provided. The method includes obtaining a plurality of image frames in a video sequence; determining a feature map for each of the plurality of image frames and determining an offset map based on the feature map; determining an aligned feature map by performing a temporal deformable convolution (TDC) on the feature map and the offset map; and generating a plurality of aligned frames based on the aligned feature map.

A method, apparatus and storage medium for performing video coding are provided. The method includes obtaining a plurality of image frames in a video sequence; determining a feature map for each of the plurality of image frames and determining an offset map based on the feature map; determining an aligned feature map by performing a temporal deformable convolution (TDC) on the feature map and the offset map; and generating a plurality of aligned frames based on the aligned feature map.

In an image processing device (i.e. encoder or decoder), the number of loop filter stages is lowered by combining bilateral loop filtering (or Hadamard loop filtering) with either sample Adaptive Offset Filtering (SAO) or Adaptive Loop Filtering (ALF). This avoids the implementation problems associated with too many loop filter stages and provides approximately the same compression efficiency gain as having separate loop filter stages.

At least a method and an apparatus are presented for efficiently encoding or decoding video. For example, one or more chroma residual scaling parameters are determined based one or more luma mapping parameters and based on a corrective value of the one or more chroma residual scaling parameters. The video is encoded or decoded based on the determined one or more chroma residual scaling parameters.

Neural image compression using substitutional quality factor learning in a latent space, including receiving a compressed bitstream and a target quality factor indicating a target compression quality, calculating a decoded latent representation of the compressed bitstream, and calculating a reconstructed image based on the decoded latent representation of the compressed bitstream and the target quality factor, computing a shared feature based on a network forward computation using shared decoding parameters (SDP) of one or more layers of a convolutional neural network, computing estimated adaptive decoding parameters (ADP) for the one or more layers of the convolutional neural network based on the shared feature, the adaptive decoding parameters, and the target quality factor, and computing an output tensor based on the estimated ADP in the one or more layers of the convolutional neural network and the shared feature.

Neural image compression using substitutional quality factor learning in a latent space, including receiving a compressed bitstream and a target quality factor indicating a target compression quality, calculating a decoded latent representation of the compressed bitstream, and calculating a reconstructed image based on the decoded latent representation of the compressed bitstream and the target quality factor, computing a shared feature based on a network forward computation using shared decoding parameters (SDP) of one or more layers of a convolutional neural network, computing estimated adaptive decoding parameters (ADP) for the one or more layers of the convolutional neural network based on the shared feature, the adaptive decoding parameters, and the target quality factor, and computing an output tensor based on the estimated ADP in the one or more layers of the convolutional neural network and the shared feature.