Context-Based Pixel Predictor Selection for Image Compression
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Solution Overview
Problem
Conventional digital image compression systems do not effectively leverage relationships between pixels corresponding to residual values, leading to inefficient compression performance.
Innovation Solution
A compression system that selects context-based pixel predictors by grouping residual values into context groups based on similar statistical properties, determining a predictor for each group to generate improved prediction values and further compress the image using entropy coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional pixel predictors are used to predict pixel values, then the compression process is simple, but the compression performance is insufficient due to not leveraging relationships between pixels corresponding to residual values
Solution Approach 1:
The patent segments the image processing into distinct stages: initial prediction using a conventional pixel predictor, residual value computation, context group formation based on statistical properties, and selective application of additional pixel predictors to specific context groups. This segmentation allows the system to apply complexity only where beneficial, improving compression performance while controlling overall system complexity.
Solution Approach 2:
The patent applies local quality by determining that different regions of the image (represented by different context groups) benefit from different prediction strategies. By analyzing statistical properties of residual values in local regions and selecting pixel predictors accordingly, the system optimizes compression for each local context rather than applying a uniform approach, thereby improving overall compression performance.
2Productivity
If additional pixel predictors are determined for each context group, then compression performance improves, but the computational complexity increases
Solution Approach 1:
The patent implements partial action by applying additional pixel predictors only to specific context groups where they provide benefit, rather than applying them uniformly to all pixels. The system evaluates statistical properties of residual values and selectively determines additional pixel predictors for context groups that exhibit characteristics suitable for improved prediction, thereby reducing computational resource consumption while maintaining compression performance gains.
3Measurement precision
If residual values are grouped into context groups based on statistical properties, then prediction accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by forming context groups based on statistical properties of residual values during the encoding process, before the actual compression output is generated. This preliminary organization of residual values into context groups with similar statistical characteristics enables more accurate prediction in subsequent steps, as the pixel predictors can be selected based on pre-analyzed statistical properties rather than computed in real-time during compression.
Data Source
AI summary
In implementations of systems for digital image compression using context-based pixel predictor selection, a computing device implements a compression system to receive digital image data describing pixels of a digital image. The compression system groups first differences between values of the pixels and first prediction values of the pixels into context groups. A pixel predictor is determined for each of the context groups based on a compression criterion. The compression system generates second prediction values of the pixels using the determined pixel predictor for pixels corresponding to the first differences included in each of the context groups. Second differences between the values of the pixels and the second prediction values of the pixels are grouped into different context groups. The compression system compresses the digital image using entropy coding based on the different context groups.