Parallel Entropy Coding with Context-Based Bitstream Partitioning
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Solution Overview
Problem
Current entropy coding methods, such as H.264's CAVLC and CABAC, face challenges in efficiently handling situations where the number of parallel decoding processing units differs from the number of distinct estimated probabilities, leading to computational demands and limited processing power issues, especially in high-quality video decoding.
Innovation Solution
A method and architecture that utilize parallel entropy coders to encode an input sequence into multiple subsequences, which are then combined into a single bitstream, allowing for parallel decoding and interleaving based on a context model to reconstruct the original sequence, enabling efficient encoding and decoding even with varying processing unit availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If CABAC is used for entropy coding to achieve greater compression, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent segments the entropy coding process into multiple parallel contexts, where each context handles a specific subset of probability models. This allows the complex CABAC algorithm to be divided into manageable parallel tasks that can be executed simultaneously, reducing the computational burden on single processing units while maintaining compression efficiency.
Solution Approach 2:
The patent introduces parallelism as a new dimension for processing entropy-coded data. By organizing multiple entropy coding contexts to operate in parallel rather than sequentially, the system achieves both improved compression efficiency and reduced computational complexity through distributed processing across multiple units.
2Productivity
If the number of parallel decoding processing units is increased to improve decoding speed, then processing throughput is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent designs a universal parallel processing architecture where multiple decoding processing units can handle different entropy coding contexts using the same fundamental decoding logic. This multi-functional design allows the system to scale decoding speed by adding processing units without proportionally increasing device complexity, as each unit performs similar functions with different parameter configurations.
3Adaptability or versatility
If the number of parallel decoding processing units differs from the number of distinct estimated probabilities, then flexibility in resource allocation is improved, but mapping complexity increases
Solution Approach 1:
The patent introduces a context model as an intermediary layer between the parallel decoding processing units and the probability estimates. This context model acts as a mediator that maps multiple probability estimates to a manageable number of processing units, or vice versa, thereby enabling flexible resource allocation while reducing the direct mapping complexity between processing units and probability models.
Data Source
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AI summary
A method for entropy coding data using parallel entropy coders to encode an input sequence as a plurality of encoded subsequences, which are then combined to form an output bitstream. The input sequence is separated into subsequences on the basis of a context model. A method for entropy decoding a bitstream of encoded data by extracting a plurality of encoded subsequences from the bitstream and entropy decoding the encoded subsequences in parallel to generate a plurality of decoded subsequences, which are then interleaved based on a context model to produce a reconstructed sequence.