Adaptive Huffman Coding for Narrowed Differential Index Ranges
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Solution Overview
Problem
Huffman coding is not optimally efficient for audio signals with varying statistics, often consuming more bits than fixed-length coding, especially due to the phenomenon of auditory masking, which affects the encoding of subbands with large energy.
Innovation Solution
The solution involves designing a Huffman table with fewer code words by narrowing down the range of differential indices based on auditory masking properties, allowing for shorter code lengths and reduced bit consumption by selecting the appropriate Huffman table for encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Huffman coding is used to encode quantization indices, then encoding efficiency is improved for signals matching the Huffman table statistics, but bit consumption increases for signals with different statistics
Solution Approach 1:
The patent applies dynamic selection of coding methods by introducing a statistics comparison mechanism. The encoder dynamically determines whether to use Huffman coding or fixed-length coding based on comparing signal statistics with predefined Huffman table characteristics, allowing adaptive optimization for different signal types
Solution Approach 2:
The patent changes the coding parameter selection based on signal statistics. By monitoring whether signal statistics match predefined Huffman table characteristics, the system switches between Huffman coding and fixed-length coding parameters to optimize encoding performance for different audio signals
2Productivity
If both Huffman coding and fixed length coding are included with selection, then encoding optimality is improved for varying statistics, but device complexity increases
Solution Approach 1:
The patent segments the coding system into distinct Huffman coding and fixed-length coding paths, with a selection mechanism that routes different signal types to appropriate coding methods. This modular segmentation allows efficient implementation with clear decision boundaries
Data Source
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AI summary
In this invention, the design of the Huffman table can be done offline with a large input sequence database. The range of the quantization indices (or differential indices) for Huffman coding is identified. For each value of range, all the input signal which have the same range will be gathered and the probability distribution of each value of the quantization indices (or differential indices) within the range is calculated. For each value of range, one Huffman table is designed according to the probability. And in order to improve the bits efficiency of the Huffman coding, apparatus and methods to reduce the range of the quantization indices (or differential indices) are also introduced.