Tree-Structured Entropy Coding for Low-Complexity Audio and Video
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Solution Overview
Problem
Existing entropy encoding and decoding methods for audio and video signals have low coding efficiency and high complexity, particularly in methods like Advanced Audio Coding (AAC), which require significant memory and computational resources due to inefficient handling of entropy coding outputs.
Innovation Solution
The use of a tree structure-based approach for entropy encoding and decoding, where codewords are detected and probabilities are calculated based on a tree structure with varying node probabilities, allowing for adaptive encoding and decoding processes that optimize symbol detection and bitstream generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional entropy encoding methods (e.g., AAC) are used, then coding is performed, but coding efficiency is low and complexity is high
Solution Approach 1:
The patent segments the probability space into multiple tree structures, where each tree represents a different probability distribution model. This segmentation allows the encoder to select appropriate trees for different symbol ranges, improving coding efficiency while managing complexity through structured division of the encoding space
Solution Approach 2:
The patent implements dynamic selection of tree structures based on the current symbol being encoded. The encoder dynamically switches between different probability models (trees) depending on the symbol range, adapting the encoding process to local statistics and thereby improving overall coding efficiency
2Adaptability or versatility
If additional information (e.g., section_data) is added to entropy coding output range, then more data can be encoded, but the amount of additional data increases
Solution Approach 1:
The patent merges the probability information and symbol encoding into a unified tree structure framework. By combining what would traditionally be separate data elements (probability models and symbol codes) into an integrated hierarchical structure, the patent reduces redundant information and minimizes additional data requirements while maintaining enhanced encoding capability
3Adaptability or versatility
If 1024 codebooks are needed to encode a signal of size 1024, then complete coverage is achieved, but memory space and complexity increase significantly
Solution Approach 1:
The patent employs a nested hierarchical structure where tree structures are organized in levels, with parent nodes representing broader symbol ranges and child nodes representing more specific ranges. This nesting allows complete signal coverage through hierarchical decomposition rather than requiring separate codebooks for each symbol, dramatically reducing memory requirements while maintaining full adaptability
Solution Approach 2:
The patent transitions from a flat codebook structure to a multi-dimensional hierarchical tree structure. By organizing probability models across multiple dimensions (levels of the tree), the system achieves complete signal coverage through structured navigation rather than exhaustive storage, reducing memory space from O(N) to O(log N) complexity
Data Source
AI summary
Encoding and decoding of an audio signal or a video signal. By performing entropy encoding and decoding based on a tree structure, coding efficiency is increased, complexity is reduced, and additional information can be reduced.