Huffman Table Selection for Masked Differential Index Coding
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Solution Overview
Problem
In audio signal encoding, varying signal statistics can lead to inefficient bit consumption by Huffman coding, often exceeding that of fixed-length coding, especially due to auditory masking effects where large energy in one subband masks neighboring subbands, resulting in suboptimal encoding.
Innovation Solution
The solution involves exploring auditory masking properties to narrow the range of differential indices, allowing for the design of Huffman tables with fewer code words, which reduces bit consumption by using shorter code lengths for encoding differential indices.
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 significantly when signal statistics differ from the predefined Huffman table
Solution Approach 1:
The patent applies dynamics by making the Huffman table selection adaptive rather than static. The encoder dynamically selects from multiple predefined Huffman tables based on the actual statistical characteristics of the input signal. This allows the coding system to adapt to varying signal conditions, maintaining encoding efficiency while preventing excessive bit consumption when signal statistics differ from any single predefined table.
Solution Approach 2:
The patent changes the parameter of Huffman table selection based on signal statistics. By comparing signal characteristics against multiple predefined tables and selecting the best match, the system optimizes the coding parameters dynamically. This resolves the contradiction by allowing the system to switch between different coding configurations rather than being locked into a single static table.
2Reliability
If both Huffman coding and fixed-length coding are included with selection based on bit consumption, then extreme cases are handled, but the solution is not optimal for all signals with different statistics
Solution Approach 1:
The patent segments the Huffman coding space into multiple predefined tables, each optimized for specific signal statistical characteristics. Rather than using a single unified coding scheme or binary choice between Huffman and fixed-length, the system divides the solution space into specialized segments that can be selected based on the input signal properties, achieving better overall optimality.
Solution Approach 2:
The patent creates a universal coding framework that incorporates multiple Huffman tables serving different signal types. This multi-functional approach allows the same encoding structure to handle diverse signal statistics effectively, surpassing the limitations of a simple binary choice between Huffman and fixed-length coding methods.
3Measurement precision
If scaling factor is reduced below masking power threshold, then quantization error is reduced in subbands with large errors, but bits consumption for Huffman encoding necessarily increases
Solution Approach 1:
The patent changes the Huffman table parameter selection based on the scaled quantization values. When scaling factors are applied to reduce quantization error, the resulting values may have different statistical distributions that require different Huffman tables. The system adapts the coding table parameters to match the scaled signal characteristics, preventing excessive bit consumption despite the increased precision requirements.
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.