Symbol Sequence Compression Using Run-Pair Merging
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Solution Overview
Problem
Existing methods for compressing 3D mesh topology data, such as Edgebreaker, do not effectively address the statistical redundancy in symbol sequences, leading to suboptimal compression ratios for large 3D meshes.
Innovation Solution
A method that combines frequently occurring symbol pairs into a new symbol and encodes this new symbol along with other symbols using context models, followed by binary sequence generation and compression encoding, allowing for improved compression by removing redundancy in 'runs' of symbols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Edgebreaker algorithm is used to encode topology data, then compression ratio is improved (less than 1.5 bits per triangle), but statistical redundancy in symbol sequences is not effectively addressed, leading to suboptimal compression for large 3D meshes
Solution Approach 1:
The patent combines frequently occurring symbol pairs (such as 'CR' for Count-Rewind and 'RC' for Rewind-Count) into new composite symbols. This merging reduces the total number of distinct symbols in the sequence, thereby addressing statistical redundancy and improving compression ratios for large 3D meshes while maintaining the effectiveness of the Edgebreaker algorithm.
2Productivity
If symbol sequences are encoded using traditional entropy coding, then compression is achieved, but frequent symbol patterns such as 'CR' and 'RC' are not exploited, resulting in suboptimal compression ratios
Solution Approach 1:
The patent changes the parameter representation by introducing composite symbols that represent frequent patterns. Instead of encoding each individual symbol (C, R, L, E, S) separately, the system transforms the symbol sequence by replacing frequent pairs with new symbols, thereby changing the statistical parameters of the sequence and enabling more efficient entropy coding.
3Productivity
If more context models are used to encode different positions in runs of symbols, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the symbol sequence processing into distinct phases: first identifying and replacing frequent symbol pairs with composite symbols, then applying context models to the modified sequence. This segmentation allows the use of multiple context models only where necessary (for different run positions), improving compression efficiency while managing device complexity through structured processing.
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.