Parallel Entropy Decoding with Payload Segmentation Load Balancing
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Solution Overview
Problem
Current data compression technologies, such as the H.264 standard, face challenges in achieving efficient entropy coding due to their serial nature, which can be computationally demanding and inefficient, especially in high-speed video decoding applications with limited processing power.
Innovation Solution
The implementation of a parallel entropy coding and decoding architecture that uses context modeling to assign bits to multiple sources based on estimated probabilities, allowing for parallel entropy coding and decoding of subsequences, which are then interleaved to reconstruct the input sequence, and includes auxiliary information for load balancing across multiple decoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If serial entropy coding is used, then implementation simplicity is maintained, but processing speed and efficiency deteriorate
Solution Approach 1:
The patent divides the entropy decoding process into multiple parallel decoders, each handling a specific segment of the bitstream. The bitstream is segmented based on syntax element types (e.g., intra-coded block flags, inter-coded block flags, transform coefficients), allowing simultaneous processing of different segments by different decoders. This segmentation enables parallel processing while maintaining manageable complexity for each individual decoder.
Solution Approach 2:
The patent introduces a new dimension of parallelism by organizing decoders along the dimension of syntax element types rather than sequential processing. Multiple decoders operate simultaneously on different syntax element categories, transforming the single-threaded processing model into a multi-dimensional parallel architecture. This approach increases processing throughput without requiring each decoder to handle the entire bitstream sequentially.
2Productivity
If parallel entropy decoding is implemented, then processing efficiency is improved, but load balancing becomes challenging
Solution Approach 1:
The patent assigns different decoding strategies and context models to different decoders based on the specific syntax element types they process. Each decoder is optimized for its designated segment, with local context models tailored to the statistical characteristics of that particular syntax element category. This local optimization ensures that each decoder operates at peak efficiency for its specific task, naturally achieving load balance through differentiated specialization rather than uniform processing.
3Loss of substance
If CABAC is used instead of CAVLC, then compression ratio is improved, but computational complexity increases
Solution Approach 1:
The patent segments the computationally intensive CABAC decoding process into multiple parallel decoders, each handling specific syntax element types. By dividing the overall decoding workload across multiple independent decoder units, the computational complexity of CABAC is distributed rather than concentrated in a single sequential processor. This allows the system to achieve high compression ratios through CABAC while maintaining acceptable processing speeds through parallel execution.
Data Source
AI summary
Devices and methods for entropy decoding a bitstream of encoded data by extracting a plurality of encoded subsequences from a payload field of the bitstream for parallel decoding on a set of parallel entropy decoders. The method includes dividing the payload of concatenated encoded subsequences into segments using a distribution function and distributing the segments amongst the set of parallel entropy decoders to balance the computational load among the parallel entropy decoders. The received bitstream includes auxiliary information inserted by the encoder to enable the decoder to entropy decode segments that begin with a portion of an encoded subsequence.