Parallel Block Subset Coding for Video Compression
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Solution Overview
Problem
Existing video coding methods, such as those used in MPEG and H.264, face inefficiencies due to the lack of local appropriateness of symbol occurrence probabilities during entropy coding, leading to potential losses in coding effectiveness.
Innovation Solution
A method of image coding that groups blocks into subsets and codes them in parallel, using probabilities calculated for previously coded and decoded blocks within the same subset for entropy coding, thereby reducing memory storage needs and maintaining compression performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If blocks are coded sequentially in raster-scan order using traditional entropy coding, then the coding process is simple to implement, but the symbol occurrence probabilities lack local appropriateness leading to loss of coding effectiveness
Solution Approach 1:
The image is divided into multiple subsets of blocks (e.g., first subset, second subset, third subset) that can be coded in parallel. Each subset contains blocks that reference each other for prediction, allowing localized probability learning while enabling parallel processing across subsets.
Solution Approach 2:
The patent introduces a new dimension of parallelism by organizing blocks into multiple subsets that can be processed simultaneously. This transforms the traditional single-threaded raster-scan approach into a multi-threaded architecture where different subsets are coded in parallel, improving both efficiency and probability accuracy.
2Measurement precision
If multiple previously coded blocks are stored to calculate accurate symbol occurrence probabilities for each current block, then coding precision is improved, but memory storage requirements increase
Solution Approach 1:
Within each subset, blocks maintain local references to previously coded blocks for probability calculation. The probability learning is localized to each subset rather than requiring global access to all previously coded blocks, reducing memory requirements while maintaining accuracy within the local context.
Solution Approach 2:
By segmenting the image into multiple subsets, the patent reduces the scope of probability calculation to within each subset. This segmentation allows each subset to maintain its own probability state independently, reducing the total memory storage requirements compared to a global probability model.
3Productivity
If blocks are coded in parallel to improve processing efficiency, then productivity increases, but ensuring availability of reference blocks becomes more complex
Solution Approach 1:
The patent segments the image into multiple subsets that can be coded in parallel. Each subset is self-contained with its own reference blocks, allowing independent processing without complex inter-subset dependencies. This segmentation simplifies parallel processing by eliminating the need for complex synchronization mechanisms.
Solution Approach 2:
The patent organizes blocks into subsets in advance, with each subset containing blocks that will reference each other during coding. This preliminary organization ensures that all necessary reference blocks are available within each subset before parallel coding begins, eliminating the need for complex runtime reference management.
Data Source
AI summary
A method of coding at least one image comprising the steps of splitting the image into a plurality of blocks, of grouping said blocks into a predetermined number of subsets of blocks, of coding each of said subsets of blocks in parallel, the blocks of a subset considered being coded according to a predetermined sequential order of traversal. The coding step comprises, for a current block of a subset considered, the sub-step of predictive coding of said current block with respect to at least one previously coded and decoded block, and the sub-step of entropy coding of said current block on the basis of at least one probability of appearance of a symbol.