Recursive Image Compression Using Gradient-Entropy Sub-Blocks
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Solution Overview
Problem
Existing digital image compression techniques, particularly lossy algorithms, require significant time and processor power for compression and decompression, and often result in observable loss of information due to their reliance on transform encoding methods.
Innovation Solution
The Parent-Child Cluster (PCC) compression technique reduces data dimensionality by clustering RGB pixels in localized image regions, using a spatial domain approach that lowers computational load and incorporates a key encoding scheme tailored to the human visual system, allowing for high fidelity in low entropy regions and high dynamic range in high entropy regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If transform encoding methods are used for lossy compression, then compression ratio is improved, but processor power requirement increases substantially
Solution Approach 1:
The image is divided into multiple sub-blocks, and the compression process is applied recursively to each sub-block. This segmentation allows the algorithm to process smaller regions independently, reducing the overall computational burden while maintaining compression effectiveness.
Solution Approach 2:
The algorithm applies gradient analysis and entropy analysis selectively to determine the appropriate compression level for each sub-block. By performing partial analysis only where needed (based on gradient and entropy thresholds), the algorithm achieves good compression ratios without substantially increasing processor power requirements across the entire image.
2Loss of information
If transform encoding methods are used for lossy compression, then compression ratio is improved, but decompression time increases
Solution Approach 1:
The image is divided into multiple sub-blocks, and the compression process is applied recursively to each sub-block. This segmentation allows the algorithm to process smaller regions independently, reducing the overall computational burden while maintaining compression effectiveness.
Solution Approach 2:
The algorithm applies gradient analysis and entropy analysis selectively to determine the appropriate compression level for each sub-block. By performing partial analysis only where needed (based on gradient and entropy thresholds), the algorithm achieves good compression ratios without substantially increasing processor power requirements across the entire image.
3Power
If clustering is applied to reduce data dimensionality, then computational load is reduced, but quantization error increases
Solution Approach 1:
The algorithm dynamically adjusts the clustering approach based on local image characteristics. By computing gradient and entropy values for each sub-block, the algorithm adapts the compression level and clustering intensity to match the local complexity, thereby reducing quantization error in important regions while maintaining low computational load overall.
Solution Approach 2:
Different compression strategies are applied to different regions of the image based on their local characteristics. Sub-blocks with low gradient and low entropy undergo more aggressive compression, while regions with high gradient or high entropy retain more detail. This local quality approach minimizes overall quantization error while maintaining computational efficiency.
Data Source
AI summary
A system includes a controller configured to: obtain palette keys for an image; and for each of a plurality of sub-blocks of the image, obtain a compressed set of palette keys responsive to gradient analysis and entropy analysis. The system also includes a spatial light modulator coupled to the controller and configured to project an image responsive to the palette keys and the compressed set of palette keys.