Adaptive Entropy Codebook Updates for Unknown Symbol Distributions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing entropy coding techniques assume a certain probability distribution for symbols, leading to suboptimal performance when the actual distribution differs, especially when the distribution is unknown.
Innovation Solution
A method and system for dynamically updating a codebook based on the actual probability distribution of symbols by incrementing symbol frequencies, swapping ranks, and modifying group prefixes and suffixes in real-time to optimize entropy encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed initial codebook is used for entropy encoding, then the encoding process is simple and fast, but the encoding efficiency is suboptimal when the actual symbol probability distribution differs from the assumed distribution
Solution Approach 1:
The patent implements dynamic codebook adaptation by continuously updating symbol frequencies and reassigning codewords based on observed symbol probabilities during the encoding process. The codebook transitions from a static initial state to a dynamic state that adapts to the actual data distribution, resolving the contradiction between simple fixed-codebook encoding and efficient adaptive encoding.
Solution Approach 2:
The encoding system performs self-optimization by automatically tracking symbol frequencies and updating the codebook assignments without external intervention. The encoder monitors its own input data distribution and adjusts its encoding strategy in real-time, enabling the system to improve its own encoding efficiency through self-service adaptation.
2Adaptability or versatility
If the codebook is dynamically updated based on symbol frequencies, then the encoding becomes adaptive to unknown distributions, but the computational overhead increases
Solution Approach 1:
The patent assigns preliminary group prefixes to symbols before actual encoding begins, organizing symbols into groups with predetermined prefix assignments. This preliminary organization reduces the computational complexity of dynamic updates by providing a structured framework that limits the scope of reassignment operations needed when adapting to unknown distributions.
Solution Approach 2:
The system changes parameters selectively by updating only the necessary portions of the codebook (specific symbol frequencies and their corresponding codeword assignments) rather than reconstructing the entire codebook. This selective parameter update approach maintains adaptability to unknown distributions while reducing computational overhead compared to full codebook regeneration.
3Productivity
If more bits are used per symbol in the initial codebook, then all symbols can be uniquely encoded, but the overall data compression ratio decreases
Solution Approach 1:
The patent implements feedback mechanisms where the encoder continuously monitors symbol frequencies and uses this information to adjust codebook assignments. The feedback loop allows the system to identify frequently occurring symbols and allocate shorter codewords to them, thereby improving the overall compression ratio while maintaining unique decodability for all symbols.
Solution Approach 2:
The system dynamically changes codeword parameters (length and assignment) based on observed symbol frequencies. By adjusting these parameters in response to actual data characteristics, the system achieves better compression ratios without sacrificing the precision needed for unique symbol identification, resolving the contradiction between compression efficiency and encoding precision.
Data Source
AI summary
A method may include receiving a first symbol, and encoding the first symbol based on an initial codebook including a symbol frequency and a ranking for each symbol, and a group frequency for each group of symbols. The method may include incrementing a symbol frequency of the first symbol based on receiving the first symbol, swapping a rank of a lowest ranking symbol from among symbols in the initial codebook that have a symbol frequency lower than the incremented symbol frequency of the first symbol with a rank of the first symbol, in response to the symbol frequency of the lowest ranking symbol being lower than the incremented symbol frequency of the first symbol, and storing an updated codebook in a memory accessible by an encoder and/or a decoder.