Semantic Huffman Encoding With Synonymous Sets for Higher Compression
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Solution Overview
Problem
Current communication technologies face limitations in compressing information efficiently and transmitting large amounts of data reliably due to the theoretical compression limit of traditional Huffman coding based on syntactic probability, which restricts further improvements in compression efficiency.
Innovation Solution
Implement a semantic based Huffman encoding method that associates source symbols with synonymous sets through a pre-constructed synonymous mapping codebook, determines encoded codewords using a semantic Huffman codebook, and sorts these codewords according to the source symbol order to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional Huffman encoding based on syntactic probability is used, then the encoding process is simple and fast, but the compression efficiency reaches a theoretical limit and cannot be further improved
Solution Approach 1:
The patent transitions from traditional syntactic probability dimension to semantic meaning dimension by introducing synonymous sets. Instead of encoding individual source symbols based on their occurrence probability, the system groups symbols with identical meanings into synonymous sets and encodes these sets collectively, adding a semantic dimension to the encoding process that enables compression beyond the Shannon limit
Solution Approach 2:
The patent merges multiple source symbols that share the same semantic meaning into a single synonymous set. By combining these symbols and assigning a single encoded codeword to the entire set, the system reduces the total number of codewords needed and achieves higher compression efficiency while maintaining the ability to reconstruct the original information sequence
2Loss of information
If synonymous sets are introduced for semantic encoding, then compression efficiency is improved beyond the theoretical limit, but the system complexity increases due to pre-constructed codebooks and mapping processes
Solution Approach 1:
The patent performs preliminary actions by pre-constructing the synonymous mapping codebook and semantic Huffman codebook before the actual encoding process. All synonymous sets are identified and mapped in advance, allowing the encoding phase to simply look up and replace symbols with their corresponding synonymous set codewords, thereby reducing real-time computational complexity
Solution Approach 2:
The patent creates a semantic layer copy of the original information by mapping source symbols to synonymous sets based on their semantic meanings. This semantic copy retains the essential meaning of the original data while using fewer distinct elements (synonymous sets), enabling more efficient compression without losing information integrity
Data Source
AI summary
Disclosed are a semantic based Huffman encoding method, a semantic based Huffman decoding method and related devices. In the semantic based Huffman encoding method, an information sequence containing source symbols is received at first. Then, synonymous mappings are performed on the source symbols respectively based on a synonymous mapping codebook. Later, synonymous sets corresponding to the source symbols are determined respectively. Further, encoded codewords corresponding to the synonymous sets are determined respectively based on a semantic Huffman codebook. After sorting the encoded codewords according to an order of the source symbols in the information sequence to obtain an encoded sequence corresponding to the information sequence, the encoded sequence is transmitted to a receiving end for decoding.