Image Entropy Coding With Multidimensional Probability Learning
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Solution Overview
Problem
Current video coding techniques, such as those used in MPEG and H.264, suffer from a lack of local adequacy in probability learning due to sequential traversal methods, leading to inefficiencies and limitations in parallel decoding capabilities.
Innovation Solution
A method that employs a multidimensional neighborhood approach for learning probabilities, allowing for parallel decoding by initializing and storing probabilities from adjacent sub-sequences, enabling better local adaptation and synchronization during the coding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sequential raster-scan traversal is used for entropy coding, then the coding process is simple to implement, but the probability learning lacks local adequacy and parallel decoding is prevented
Solution Approach 1:
The image is divided into multiple independent sub-sequences that can be processed in parallel. Each sub-sequence is assigned to a different processing thread, allowing simultaneous entropy coding while maintaining local probability learning within each segment. This segmentation resolves the contradiction by enabling parallel processing without sacrificing the simplicity of the coding approach.
Solution Approach 2:
The patent introduces a new dimension of processing by organizing blocks into sub-sequences that can be traversed in multiple directions (horizontal, vertical, diagonal) rather than strictly sequential raster-scan. This multi-dimensional approach allows parallel processing while maintaining local context adequacy for probability learning.
2Device complexity
If sequential traversal order is used for coding symbols, then probability learning is simple, but parallel decoding of multiple symbols is prevented
Solution Approach 1:
The symbol sequence is segmented into multiple independent sub-sequences that can be decoded in parallel. Each sub-sequence maintains its own probability context, allowing multiple decoding threads to operate simultaneously without interfering with each other, thus improving productivity while keeping individual thread complexity manageable.
Solution Approach 2:
The patent performs preliminary organization of blocks into sub-sequences during the encoding phase, preparing the data structure in advance to enable parallel decoding. This preliminary action allows the decoding stage to efficiently utilize multiple processing units without increasing the complexity of the decoding algorithm itself.
3Ease of operation
If raster-scan traversal is used, then coding is straightforward, but probability learning at line starts is inadequate due to reliance on end-of-previous-line symbols
Solution Approach 1:
The patent introduces multi-dimensional traversal directions (horizontal, vertical, diagonal) for organizing sub-sequences. This allows probability learning to draw from spatially adjacent blocks in multiple directions rather than only from the end of the previous line, significantly improving probability learning precision while maintaining coding straightforwardness through systematic organization.
Solution Approach 2:
The patent applies local quality by allowing each sub-sequence to have its own optimized traversal direction and probability context. This enables local adaptation of the coding approach to match the local characteristics of different image regions, improving probability learning precision without complicating the overall coding process.
Data Source
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AI summary
A method is provided for encoding an image represented by a sequence of symbols. The method includes the following steps: defining subsequences within the sequence; and going through the subsequences applying the following sub-steps for a routine subsequence: determining an adjacent routine encoded group including two previously encoded subsequences, one of which belongs to an adjacent encoded group associated with a subsequence that is separate from the routine subsequence; initializing a routine probability of occurrence of a symbol, taking into account two known probabilities of occurrence of the symbol, relating to two previously encoded subsequences of the adjacent routine encoded group; the consecutive entropic encoding of the symbols, and updating the routine probability of occurrence; and storing the routine probability of occurrence after encoding the last symbol.