Codebook Generation for Cloud Video Compression
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Solution Overview
Problem
Conventional codebook-based vector quantization (VQ) compression techniques face efficiency degradation when applied to pre-compressed video streams that have undergone entropy coding, as the structure necessary for effective compression is removed, leading to reduced compression efficiency.
Innovation Solution
Performing entropy decoding on pre-compressed video streams to restore structure, and generating improved VQ codebooks by grouping components such as image, motion, and control data segments to form longer, more structured vectors, which are then used to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If entropy coding is applied to pre-compress video streams, then compression efficiency is improved, but the structure necessary for effective codebook-based VQ compression is removed
Solution Approach 1:
The patent applies entropy decoding as a preliminary action before codebook-based VQ compression to restore the structural information that was removed during entropy coding. This allows the subsequent VQ process to operate on data with preserved structural characteristics, enabling more effective compression.
Solution Approach 2:
The patent introduces an intermediary processing step (entropy decoding) between the pre-compression stage and the codebook-based VQ compression stage. This intermediary restores the necessary structure without returning to the original uncompressed state, enabling the VQ process to function effectively.
2Loss of substance
If conventional codebook-based VQ is applied to pre-compressed streams, then data reduction is achieved, but compression efficiency deteriorates due to structure removal
Solution Approach 1:
Entropy decoding is performed as a preliminary action to restore structural information before applying codebook-based VQ compression. This ensures that the VQ process operates on data with preserved structural characteristics, maintaining high compression efficiency while achieving significant data reduction.
Solution Approach 2:
The patent changes the parameter state of the video data by reversing entropy coding, transforming the data from an entropy-coded state to a pre-compressed state with restored structure. This parameter change enables subsequent VQ compression to achieve both data reduction and high compression efficiency.
3Measurement precision
If codebooks are trained across multiple videos over time, then representativeness is improved, but the quality degradation from entropy coding persists
Solution Approach 1:
By applying entropy decoding as a preliminary action before codebook training, the patent ensures that the training data retains its structural characteristics. This allows codebooks to be trained on representative data with preserved structure, achieving both high representativeness and structural integrity.
Solution Approach 2:
The patent introduces entropy decoding as an intermediary step in the codebook training process, ensuring that the training data undergoes structural restoration. This intermediary process enables codebooks to learn from structurally preserved data, improving both representativeness and reliability.
Data Source
AI summary
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.