Dual Codeset Entropy Coding with Context Trees for Fast Decoding
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Solution Overview
Problem
Current entropy coding methods, such as CAVLC and CABAC in H.264/AVC, are computationally demanding, making them inefficient for devices with limited processing power, particularly in high-quality video decoding applications.
Innovation Solution
The proposed solution employs encoding trees with primary and secondary codewords, using a context model to determine probabilities and generate Huffman code sets, allowing for efficient encoding and decoding of binary sequences by traversing the trees based on estimated probabilities and outputting codewords accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CABAC is used for entropy coding, then compression efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent segments the codebook into multiple codebooks corresponding to different symbol probabilities. This segmentation allows the system to select appropriate codebooks based on probability thresholds, achieving efficient compression without requiring the full complexity of CABAC for all cases.
Solution Approach 2:
The patent implements dynamic codebook selection based on estimated symbol probabilities. The system adapts by choosing different codebooks (e.g., first codebook for high probability, second codebook for low probability) according to the characteristics of the data being encoded, optimizing the balance between compression efficiency and computational complexity.
2Productivity
If traditional entropy coding methods are used, then compression is achieved, but processing speed decreases due to computational demands
Solution Approach 1:
The patent performs preliminary estimation of symbol probabilities before the actual encoding process. By pre-determining which codebook to use based on probability thresholds, the system avoids complex computations during the encoding phase, thereby improving processing speed while maintaining compression efficiency.
Solution Approach 2:
The patent uses multiple simpler codebooks instead of a single complex adaptive codebook. Each codebook is designed for specific probability ranges and can be selected and discarded based on the data characteristics, reducing the computational burden compared to continuously adapting a single complex codebook like CABAC.
Data Source
AI summary
A method for encoding an input sequence of symbols to produce a bitstream and a method of decoding the bitstream to generate a reconstructed binary sequence. Encoding employs an encoding tree having primary codewords associated with leaf nodes and secondary codewords associated with internal nodes. A flush event may cause output of secondary codewords. A context model is used to select an encoding tree corresponding to an estimated probability at the encoder. The same context model is used by the decoder to select a decoding tree. The decoder interleaves bits from decoded bit sequences associated with different estimated probabilities based on the context model.