Residual vectors are compressed in a lossless compression scheme suitable for cloud DVR video content applications. Thus, a cloud DVR service provider can take many copies of the same file stored in the cloud and save storage space by compressing those copies while still maintaining their status as distinct copies, one per user. Vector quantization is used for compressing already-compressed video streams (e.g., MPEG streams). As vector quantization is a lossy compression scheme, the residual vector has to be stored to regenerate the original video stream at the decoding (playback) node. Entropy coding schemes like Arithmetic or Huffman coding can be used to compress the residual vectors. Additional strategies can be implemented to further optimize this residual compression. In some embodiments, the techniques operate to provide a 25-50% improvement in compression. Storage space is thus more efficiently used and video transmission may be faster in some cases.

Residual vectors are compressed in a lossless compression scheme suitable for cloud DVR video content applications. Thus, a cloud DVR service provider can take many copies of the same file stored in the cloud and save storage space by compressing those copies while still maintaining their status as distinct copies, one per user. Vector quantization is used for compressing already-compressed video streams (e.g., MPEG streams). As vector quantization is a lossy compression scheme, the residual vector has to be stored to regenerate the original video stream at the decoding (playback) node. Entropy coding schemes like Arithmetic or Huffman coding can be used to compress the residual vectors. Additional strategies can be implemented to further optimize this residual compression. In some embodiments, the techniques operate to provide a 25-50% improvement in compression. Storage space is thus more efficiently used and video transmission may be faster in some cases.

Techniques are provided herein for more efficiently storing images that have a common subject, such as product images that share the same product in the image. Each image undergoes an adaptive tiling procedure to split the image into a plurality of tiles, with each tile identifying a region of the image having pixels with the same content. The tiles across multiple images can then be clustered together and those tiles having identical content are removed. Once all duplicate tiles have been removed from the set of all tiles across the images, the tiles are once again clustered based on their encoding scheme and certain encoding parameters. Tiles within each cluster are compressed using the best compression technique for the tiles in each corresponding cluster. By removing duplicative tile content between numerous images of the same subject, the total amount of data that needs to be stored is reduced.

Techniques are disclosed for the improvement of vector quantization (VQ) codebook generation. The improved codebooks may be used for compression in cloud-based video applications. VQ achieves compression by vectorizing input video streams, matching those vectors to codebook vector entries, and replacing them with indexes of the matched codebook vectors along with residual vectors to represent the difference between the input stream vector and the codebook vector. The combination of index and residual is generally smaller than the input stream vector which they collectively encode, thus providing compression. The improved codebook may be generated from training video streams by grouping together similar types of data (e.g., image data, motion data, control data) from the video stream to generate longer vectors having higher dimensions and greater structure. This improves the ability of VQ to remove redundancy and thus increase compression efficiency. Storage space is thus reduced and video transmission may be faster.

Techniques are disclosed for the improvement of vector quantization (VQ) codebook generation. The improved codebooks may be used for compression in cloud-based video applications. VQ achieves compression by vectorizing input video streams, matching those vectors to codebook vector entries, and replacing them with indexes of the matched codebook vectors along with residual vectors to represent the difference between the input stream vector and the codebook vector. The combination of index and residual is generally smaller than the input stream vector which they collectively encode, thus providing compression. The improved codebook may be generated from training video streams by grouping together similar types of data (e.g., image data, motion data, control data) from the video stream to generate longer vectors having higher dimensions and greater structure. This improves the ability of VQ to remove redundancy and thus increase compression efficiency. Storage space is thus reduced and video transmission may be faster.

Aspects of the disclosure provide a method and an apparatus for video decoding. Processing circuitry of the apparatus can decode prediction information of a current block to be reconstructed from a coded video bitstream. The prediction information can be indicative of an inter prediction mode. The processing circuitry can add motion information of a previously decoded block to a candidate list for the current block as a new motion information candidate based on a comparison between a first hash value of the motion information of the previously decoded block and one or more hash values of motion information candidates in the candidate list. The processing circuitry can reconstruct at least one sample of the current block based on current motion information of the current block that is determined based on the candidate list.

The disclosure relates to a method for compression including codebook-based quantization of a data set and corresponding decompression method, signal; bitstream, and encoder and/or decoder device.

The disclosure relates to a method for compression including codebook-based quantization of a data set and corresponding decompression method, signal; bitstream, and encoder and/or decoder device.

A method for encoding an input signal comprising signal frames into quantized bits is disclosed, the method comprises generating, for each frame of the input signal, a signal matrix comprising matrix coefficients obtained from that frame, grouping the matrix coefficients of each signal matrix into a plurality of partition vectors, and for each partition vector, selecting one vector quantization scheme from among a plurality of vector quantization schemes and quantizing that partition vector according to the selected vector quantization scheme to obtain the quantized bits. In an adaptive mode, the method comprises grouping differently the matrix coefficients obtained from different frames, and/or selecting different vector quantization schemes for partition vectors obtained from different frames.

A method for encoding an input signal comprising signal frames into quantized bits is disclosed, the method comprises generating, for each frame of the input signal, a signal matrix comprising matrix coefficients obtained from that frame, grouping the matrix coefficients of each signal matrix into a plurality of partition vectors, and for each partition vector, selecting one vector quantization scheme from among a plurality of vector quantization schemes and quantizing that partition vector according to the selected vector quantization scheme to obtain the quantized bits. In an adaptive mode, the method comprises grouping differently the matrix coefficients obtained from different frames, and/or selecting different vector quantization schemes for partition vectors obtained from different frames.