Symbol Sequence Encoding for Redundancy-Reduced 3D Mesh Compression
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Solution Overview
Problem
Existing methods for compressing 3D mesh topology data, such as Edgebreaker, suffer from statistical redundancy due to the uneven frequency of mode symbols, leading to suboptimal compression ratios for large meshes.
Innovation Solution
A method that combines frequent symbol pairs into new symbols, uses context models for encoding, and generates binary sequences to remove redundancy, allowing for improved compression by replacing run pairs and using bit flipping and unary representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Edgebreaker algorithm is used to encode topology data, then the compression ratio can be reduced to less than 1.5 bits per triangle, but statistical redundancy remains due to uneven frequency of mode symbols
Solution Approach 1:
The patent combines frequent symbol pairs (C and R) into a new symbol type, reducing the alphabet size from 5 symbols to effectively 3 symbols. This merging of frequent symbols eliminates the statistical redundancy caused by their high frequency of occurrence, while maintaining the compression efficiency achieved by Edgebreaker.
Solution Approach 2:
The patent changes the parameter of symbol frequency distribution by introducing a secondary encoding stage that processes the output of Edgebreaker. This parameter change transforms the uneven frequency distribution into a more uniform distribution, removing statistical redundancy while preserving the original compression ratio.
2Loss of information
If frequent symbol pairs are combined into new symbols, then statistical redundancy is removed, but the encoding process becomes more complex
Solution Approach 1:
The patent segments the encoding process into two distinct stages: first applying Edgebreaker to generate the initial symbol sequence, then applying a secondary encoding stage to process frequent symbol pairs. This segmentation allows each stage to be optimized independently, managing complexity while achieving redundancy removal.
Solution Approach 2:
The patent performs preliminary identification and marking of frequent symbol pairs (C and R) before the actual encoding process. This preliminary action prepares the data structure in advance, making the subsequent encoding process more systematic and manageable, thereby controlling complexity.
3Productivity
If context models are used for encoding different positions in runs, then compression efficiency is improved, but the number of context models increases complexity
Solution Approach 1:
The patent applies different context models to different positions within runs of symbols, treating each position with the quality of encoding appropriate to its local characteristics. This local quality approach improves compression efficiency by adapting to local patterns while limiting the total number of context models to a manageable set.
Data Source
AI summary
A method for compressing a symbol sequence, wherein each symbol may have one out of three or more possible symbol values, said method comprises the steps of modifying the symbol sequence by replacing each run pair, which contains a first run of symbols of a most frequent symbol value and a second run of symbols of a second most frequent symbol value, by a further symbol value not comprised in the three or more possible symbol values, generating a binary sequence comprising all replaced run pairs and compression encoding the binary sequence and the modified symbol sequence.