Color Image Compression Using Predictive Block Sorting
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Solution Overview
Problem
Existing data compression methods, such as chunking and delta compression, are ineffective for compressing multimedia files like images, as they do not provide further compression beyond storing redundant portions, and traditional block sorting techniques are limited in handling color image data effectively.
Innovation Solution
An image processing system employing a prefilter engine to analyze and filter color image data by identifying regional relationships, a sort engine for lexicographic block sorting, and a postfilter engine for adaptive filtering, utilizing techniques like Burrows-Wheeler Transform and entropy encoding to generate a compact representation of image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional block sorting techniques are used on color image data, then text data sorting capability is achieved, but compression effectiveness for multimedia files is insufficient
Solution Approach 1:
The patent transforms color image data from its original format into a different parameter representation that enables effective block sorting. By changing the data parameters (color space transformation, pixel value encoding), the system makes image data compatible with sorting-based compression algorithms that were originally designed for text, thereby resolving the contradiction between achieving compression effectiveness and maintaining handling capability for color images.
2Quantity of substance
If chunking or delta compression is applied to multimedia files, then redundant portions are stored once, but further compression beyond this point is not achieved
Solution Approach 1:
The patent divides the image data into blocks and applies sorting operations to each block, creating segments that can be independently compressed. This segmentation approach builds upon chunking by further dividing the data structure to reveal additional redundancy patterns within blocks, enabling compression beyond what simple chunking or delta compression achieves.
Solution Approach 2:
The patent performs preliminary sorting and transformation operations on the image data before the main compression process. By pre-processing the data to organize it in a sorted or semi-sorted state, the system creates conditions that enable more effective subsequent compression, achieving higher compression ratios than methods that apply compression directly without preliminary organization.
3Reliability
If lossless compression is achieved through data transformation and sorting, then data integrity is maintained, but computational complexity increases
Solution Approach 1:
The patent creates transformed copies of the original image data through sorting and transformation operations, rather than directly modifying the original data structure. These copied and reorganized versions enable compression while the original data relationship is preserved, allowing lossless compression. The system works with copies in transformed space while maintaining the ability to reconstruct the original data exactly.
Data Source
AI summary
Methods of image compression are described. A stream of color image data is filtered with a prediction routine using a pixel neighborhood. The filtered stream of color image data is sorted with a block sorting routing. A version of the color image data is compressed based on the sorted and filtered stream of color image data.