Entropy Coding Buffer Layout for Adaptive Parallel Bitstreams
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Solution Overview
Problem
Existing entropy coding methods, such as Huffman codes and arithmetic coding, face challenges in adapting to time-varying source statistics and handling skewed probability distributions, leading to inefficiencies and increased complexity, especially when dealing with higher-order probability modeling and dominant alphabet values.
Innovation Solution
The proposed solution involves an encoder and decoder system that assigns parameters to each symbol based on previous symbols, using multiple entropy encoders and decoders to create partial bitstreams, which are then interleaved in quasi-fixed size tranches to facilitate parallel processing and minimize coding delay, allowing for efficient coding of binary data with adaptive probability estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If Huffman codes with VLC tables are used for entropy coding, then implementation is efficient and easy to construct, but adaptation to time-varying source statistics is demanding in terms of algorithmic complexity and implementation costs
Solution Approach 1:
The invention segments the probability distribution into multiple discrete probability models (e.g., 16 different probability models for different symbol ranges). Each model is associated with a separate VLC table, allowing the system to select the most appropriate model and corresponding VLC table based on the current source statistics, thereby achieving adaptability while maintaining the efficiency of VLC-based coding.
Solution Approach 2:
The invention introduces dynamic selection of probability models and VLC tables based on the current source statistics. The system dynamically adapts to time-varying characteristics by selecting from multiple pre-defined probability models and their corresponding VLC tables, enabling efficient adaptation without the high complexity of fully adaptive Huffman coding.
2Productivity
If arithmetic coding is used to handle time-varying source statistics and skewed probability distributions, then coding efficiency is improved, but implementation complexity increases substantially compared to VLC
Solution Approach 1:
The invention applies different coding strategies to different parts of the probability distribution. By segmenting the probability space into multiple discrete models and using VLC tables optimized for each model, the system achieves high coding efficiency for various probability distributions without requiring the complex arithmetic coding mechanism throughout, thus reducing overall implementation complexity.
3Measurement precision
If multiple sets of VLC tables are used for higher-order probability modeling, then coding accuracy is improved, but device complexity and memory requirements increase
Solution Approach 1:
The invention designs VLC tables that can serve multiple purposes. Each VLC table is associated with a specific probability model and can be used for encoding different types of symbols that fall within that model's probability range. This multi-functional approach allows the system to achieve higher-order probability modeling accuracy while managing the number of VLC tables through intelligent reuse and selection.
Data Source
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AI summary
An encoder for encoding a sequence of symbols is described which comprises an assigner configured to assign a number of parameters to each symbol of the sequence of symbols based on information contained within previous symbols of the sequence of symbols; a plurality of entropy encoders each of which is configured to convert the symbols forwarded to the respective entropy encoder into a respective bitstream; and a selector configured to forward each symbol to a selected one of the plurality of entropy encoders, the selection depending on the number of parameters assigned to the respective symbol.