Pattern-Based Image Compression for Random Sub-Block Access
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Solution Overview
Problem
Existing image data compression methods require decompressing entire blocks to access individual values, which is inefficient for graphics processing units that randomly access small blocks of data, and caching decompressed blocks does not significantly improve efficiency.
Innovation Solution
Divide image data blocks into sub-blocks, identify patterns within these sub-blocks, and encode each sub-block with information identifying the pattern and its image element values, allowing for efficient compression and decompression of small blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If entire blocks of image data are compressed and stored, then memory bandwidth usage is reduced, but random access efficiency deteriorates because the entire block must be decompressed to access individual values
Solution Approach 1:
The patent divides image data blocks into smaller sub-blocks that can be independently compressed and accessed. This segmentation allows the system to decompress only the specific sub-block containing the desired data rather than entire blocks, resolving the contradiction between compression efficiency and random access speed by creating a hierarchical structure where fine-grained access is enabled through sub-block independence.
2Loss of time
If caching decompressed blocks is implemented, then access speed to individual values improves, but memory bandwidth usage increases due to cache management and potential redundant data transfer
Solution Approach 1:
The patent extracts only the necessary sub-blocks from compressed image data for decompression and caching, rather than managing entire blocks. This extraction approach allows the cache to store minimal necessary data, reducing memory bandwidth consumption while maintaining fast access to frequently used image elements, thus resolving the contradiction between access speed and bandwidth usage.
3Manufacturing precision
If higher quality rendering algorithms are used, then image quality improves, but memory bandwidth requirements increase
Solution Approach 1:
The patent enables different compression and access strategies for different regions or sub-blocks of image data, allowing high-quality rendering where needed while using more aggressive compression in less critical areas. This local quality approach maintains overall image quality requirements while reducing total memory bandwidth consumption by applying quality differentiation across the image data structure.
Data Source
AI summary
Methods and compression units for compressing a two-dimensional block of image element values. The method includes: dividing the two-dimensional block of image element values into a plurality of sub-blocks of image element values; identifying which pattern of a plurality of patterns is formed by the image element values of a first sub-block of the plurality of sub-blocks; and forming a compressed block of image element values by encoding the first sub-block in the compressed block of image element values with: (i) information identifying the pattern, and (ii) the image element values of the first sub-block forming the pattern.