Prefix Codebook Reordering for Non-Geometric Symbol Compression
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Solution Overview
Problem
Existing entropy coding techniques, such as exponential Golomb codes and Elias gamma/delta/omega codes, are suboptimal when the symbol distribution deviates from a geometric distribution, leading to inefficient use of bits for encoding and decoding digital signals.
Innovation Solution
Modify the prefixes of prefix-suffix entropy codes based on actual symbol probabilities, using a training dataset to reorder group probabilities and swap prefixes in the codebook, ensuring the codebook is optimized for the actual probability distribution of symbols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing entropy coding techniques (exponential Golomb codes, Elias gamma/delta/omega codes) are used, then the encoding and decoding process is simple, but the compression efficiency is poor when symbol distribution deviates from geometric distribution
Solution Approach 1:
The codebook is made dynamic by allowing prefix reordering based on actual symbol probability distributions. Instead of using fixed prefix assignments for groups, the system dynamically determines the optimal prefix sequence by comparing actual symbol probabilities with geometric distribution assumptions, and reorders prefixes to match the actual probability ranking, thereby adapting the coding structure to the specific data characteristics
Solution Approach 2:
The invention changes the parameter of prefix assignment by transitioning from fixed geometric distribution-based prefix allocation to probability-driven prefix reordering. The system calculates actual symbol probabilities from training data and uses these probabilities to determine the optimal prefix sequence, fundamentally changing how the coding parameters are configured to improve compression efficiency
2Quantity of substance
If prefix-suffix codes with fixed prefix assignments are used, then the encoding structure is simple and fast, but the bit usage is inefficient when symbol probabilities do not follow geometric distribution
Solution Approach 1:
The system performs preliminary probability computation and prefix reordering during a training phase before actual encoding. By pre-calculating symbol probabilities from training data and determining the optimal prefix sequence in advance, the system prepares an optimized codebook that reduces bit usage during subsequent encoding operations without adding runtime complexity
Solution Approach 2:
The codebook optimization process is self-adaptive, automatically adjusting prefix assignments based on the actual probability distribution of the input data. The system uses the training data itself to determine the optimal coding structure, eliminating the need for external parameter tuning or manual configuration, and enabling the codebook to serve itself in adapting to different data characteristics
Data Source
AI summary
A method may include receiving symbol probabilities from a dataset, and computing a group probability for each group of symbols in a first codebook based on the received symbol probabilities. The method may further include determining that a first group probability of a first group in the first codebook is lower than a second group probability of a second group in the first codebook, swapping a first prefix corresponding to the first group with a second prefix corresponding to the second group in response, and storing the first prefix corresponding to the second group and the second prefix corresponding to the first group in a second codebook.