Adaptive Golomb Entropy Coding for Changing Image Distributions
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Solution Overview
Problem
Existing universal variable length coding (VLC) techniques are sensitive to input distribution changes, particularly in image and video coding systems, as the distribution of input data varies with quantized bit-planes, neighboring residual values, and other contexts, leading to suboptimal compression performance.
Innovation Solution
Conditional progressive Golomb coding (PGC) is employed, where parameters are adjusted based on context, allowing for optimal parameter selection either online or offline, enabling adaptive entropy coding by grouping contexts and using progressive Golomb coding to improve compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a universal VLC is applied to all input data, then the coding structure is simple and easy to implement, but the compression performance deteriorates when the input distribution changes
Solution Approach 1:
The patent implements dynamic VLC by making the codebook adaptive to input distribution changes. The system continuously monitors the distribution of input symbols and adjusts the VLC parameters accordingly, transforming a static universal code into a dynamic adaptive code that optimizes compression for current data characteristics while maintaining implementation feasibility through structured adaptation mechanisms.
Solution Approach 2:
The patent changes VLC parameters based on observed input distribution characteristics. By monitoring statistical properties of the input data and adjusting codebook parameters (such as code length assignments and symbol mappings) to match the current distribution, the system achieves optimal compression performance for varying data patterns without requiring complete reimplementation of the coding structure.
2Productivity
If VLC parameters are adapted to match input distribution, then compression efficiency is improved, but the system complexity increases
Solution Approach 1:
The patent segments the adaptation process into distinct functional modules: input distribution monitoring, parameter estimation, codebook selection, and encoding execution. This modular segmentation allows each component to be optimized independently and enables parallel processing of adaptation tasks, reducing overall system complexity while maintaining high compression efficiency through coordinated operation of specialized sub-systems.
Solution Approach 2:
The patent performs preliminary analysis of input distribution characteristics before actual encoding begins. By pre-computing optimal VLC parameters based on observed data patterns and preparing adaptive codebooks in advance, the system eliminates the need for complex real-time parameter optimization during encoding, thereby reducing computational complexity while preserving compression performance.
3Productivity
If progressive coding with context-dependent parameters is used, then compression performance is optimized for varying distributions, but the coding process becomes more complex
Solution Approach 1:
The patent applies local quality by using context-dependent VLC parameters that are specifically optimized for local data characteristics. Different segments of the input data, which exhibit different distribution patterns, are encoded using locally optimized codebooks tailored to their specific statistical properties. This local optimization approach achieves high compression performance for heterogeneous data while managing complexity through context-based parameter selection rather than global optimization.
Data Source
AI summary
An apparatus and method for performing variable length coding (VLC) within an adaptive entropy image coding process are presented. VLC is performed using a progressive coding technique, such as progressive Golomb coding (PGC) whose operation is based on a set of parameters. Grouping is performed within image distribution contexts, such as number of quantized bit planes (qn), previous residual, predictor type, and color, so that each group having similar contexts is associated with a set of progressive coding parameters. The grouping can be performed on-line or off-line. In operation, the progressive coding parameters then change in response to changing image distribution contexts toward optimizing image coding, such as increasing peak signal-to-noise ratio (PSNR).