Grouped Binary Entropy Coding for Faster Video Decoding
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Solution Overview
Problem
Current data compression techniques, such as H.264, face challenges in achieving efficient entropy coding while maintaining computational efficiency, particularly in high-speed video decoding where CABAC is too demanding for devices with limited processing power.
Innovation Solution
The proposed solution involves using a context model to group binary symbols into consecutive strings and selecting a 2L-ary encoding tree for each string, allowing for efficient encoding and decoding through a first-in-first-out buffer system, which processes multiple symbols as a group and updates context states adaptively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If CABAC (Context Adaptive Binary Arithmetic Coding) is used for entropy coding, then compression efficiency is improved, but computational complexity increases making it unsuitable for devices with limited processing power
Solution Approach 1:
The patent segments the binary sequence processing by grouping multiple binary symbols into larger units (e.g., 2-bit, 3-bit, or 4-bit groups). Instead of processing each binary symbol individually as in traditional CABAC, the encoder groups consecutive binary symbols and processes them as composite symbols, reducing the number of encoding operations required while maintaining compression efficiency.
Solution Approach 2:
The patent merges multiple binary symbols into composite symbols that are then encoded together. By combining multiple binary decisions into a single encoding operation using context models that predict probabilities for grouped symbols, the system reduces the total number of arithmetic coding operations, thereby lowering computational complexity while preserving compression performance.
2Productivity
If binary symbols are encoded individually using traditional entropy coding, then computational simplicity is maintained, but encoding and decoding throughput is limited
Solution Approach 1:
The patent performs preliminary grouping of binary symbols into composite units before the actual encoding process. By pre-organizing the binary sequence into groups that can be processed together, the system prepares the data structure to enable parallel or batch processing operations, thereby increasing throughput without proportionally increasing computational complexity.
Solution Approach 2:
The patent employs dynamic context models that adaptively adjust probability estimates based on the grouped binary symbols being processed. The context models dynamically update their state based on previously decoded grouped symbols, allowing the encoding process to efficiently handle variable patterns in the binary data while maintaining high throughput through optimized probability prediction for grouped symbols.
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 2L-ary encoding trees having codewords associated with leaf nodes. 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 sequences of length-L strings associated with different estimated probabilities, based on the context model.