Binary Entropy Coding With Probability Set Mapping to Cut Overhead
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Solution Overview
Problem
Existing binary entropy coding methods face inefficiencies due to overhead in parallel encoding and two-codeset variable-to-variable encoding, particularly when dealing with a large number of sources, leading to increased computational demands and secondary codeword usage.
Innovation Solution
Selecting a new probability set for entropy encoding and decoding, with a mapping that reduces the number of probabilities, thereby minimizing overhead and secondary codewords, using a method that optimizes relative entropy mapping to improve coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel encoding with multiple sources is used, then coding efficiency is improved, but overhead information increases
Solution Approach 1:
The patent merges multiple probability sources into a unified probability model. Instead of maintaining separate probability estimates for each source, the invention combines them into a single adaptive probability model that processes all symbols uniformly, thereby eliminating the need for overhead information about source lengths while preserving coding efficiency.
Solution Approach 2:
The patent creates a universal probability model that serves all encoding needs without requiring source-specific parameters. The adaptive probability estimator functions as a multi-source model that automatically adapts to different symbol distributions without needing explicit source identification or length information in the bitstream.
2Measurement precision
If a large number of sources are used in context modeling, then coding precision is improved, but device complexity increases
Solution Approach 1:
The patent changes the parameter representation from multiple discrete source indices to a continuous adaptive probability estimate. Instead of selecting from a fixed set of sources, the system maintains a single probability parameter that adapts continuously based on observed symbol frequencies, reducing complexity while maintaining precision.
Solution Approach 2:
The patent implements a dynamic probability model that adapts in real-time to changing symbol distributions. The probability estimate evolves dynamically based on recent symbol observations, allowing the system to capture complex patterns without requiring a large static number of predefined sources.
3Reliability
If secondary codeset is used for flush events, then completeness of encoding is improved, but productivity decreases
Solution Approach 1:
The patent extracts the need for secondary codesets by using a unified probability model that can handle all encoding scenarios including flush events. The adaptive probability estimator naturally manages partial subsequences without requiring special secondary coding paths, eliminating the productivity penalty associated with switching between codesets.
Solution Approach 2:
The patent segments the encoding process into parallel independent streams that can be processed simultaneously. Each stream uses the same unified probability model, allowing flush events to be handled naturally within the parallel processing framework without requiring sequential secondary encoding operations.
Data Source
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AI summary
A method for decoding an encoded bitstream to obtain a sequence of symbols, the symbols belonging to a finite alphabet, wherein a context model specifies a predefined probability set, and wherein each symbol of the sequence of symbols is associated with a probability from the predefined probability set on the basis of the context model, the method comprising: reading, from the bitstream, information identifying a new probability set, wherein the new probability set is not identical to the predefined probability set; assigning, to each of the symbols of the sequence of symbols, a respective probability from the new probability set based upon a mapping, wherein the mapping maps each of the probabilities of the predefined probability set to a respective one of the probabilities from the new probability set; and entropy decoding the encoded bitstream on the basis of their respective assigned probabilities from the new probability set.