Image Compression via Pixel Grouping and Bitplane Encoding
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Solution Overview
Problem
Existing image and video compression methods assume strong correlation between neighboring pixels, which is not applicable to synthetic images generated by computers, leading to inefficient compression.
Innovation Solution
The method involves partitioning images into blocks, grouping pixel values that are the same or similar, encoding the number of groups and pixel values, and using leftover bits for refinement, allowing for independent encoding of each block and color component, with the option to share bit budgets between components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional compression methods based on pixel correlation assumptions are used, then natural images can be compressed efficiently, but synthetic images generated by computers cannot be compressed effectively
Solution Approach 1:
The image is divided into multiple blocks, and each block is further segmented into groups based on pixel value similarity. This segmentation allows the encoder to handle different regions with different characteristics independently, making the compression method adaptable to both natural and synthetic images without relying on global pixel correlation assumptions.
Solution Approach 2:
The patent applies different encoding strategies to different blocks based on their local characteristics. By determining the number of bitplanes for encoding each block individually and grouping pixels based on local value similarity, the method achieves local optimization that works for both natural and synthetic images, rather than applying a uniform approach based on natural image assumptions.
2Measurement precision
If detailed pixel information is preserved for high quality, then image quality is maintained, but compression ratio decreases
Solution Approach 1:
The patent uses a variable number of bitplanes for encoding different blocks, determined by the complexity and characteristics of each block. This partial action approach sends only the necessary amount of information for each block - using more bitplanes for complex blocks and fewer for simple blocks - thereby achieving high overall quality while maintaining good compression ratios.
Solution Approach 2:
The method dynamically changes the encoding parameters (number of bitplanes, grouping thresholds) based on the actual content of each block. This adaptive parameter adjustment allows the system to optimize the balance between quality and compression ratio for each local region, achieving high overall image quality without uniformly sacrificing compression efficiency.
Data Source
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AI summary
Compression of graphics images and videos includes partitioning an image into blocks, determining the number of bitplanes for encoding each block, encoding the block by grouping pixel values so that within each group, the pixel values are the same or similar, sending the number of bitplanes, sending the number of groups and the pixel value of each group and sending a signal for each pixel to indicate the group for that pixel, as well as using leftover bits from a bit budget for sending refinement bits for pixels.