Image Compression Using Compacted Region Transforms
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Solution Overview
Problem
Existing methods for compressing image data, such as vector quantization and color-space conversion, face limitations due to large codebooks and potential values outside the converted range, which affect their effectiveness in reducing storage requirements.
Innovation Solution
A method involving selective application of compression transforms to compact image data in value space, followed by identification of reference data items and decompression transforms to encode and store data efficiently, allowing for effective compression and decompression of image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vector quantization is used to compress colour data, then the colour space can be represented well, but the codebook becomes very large, limiting compression effectiveness
Solution Approach 1:
The invention divides the image data into multiple blocks and applies different compression transforms to different blocks based on their local characteristics. This segmentation allows the system to use smaller, more manageable transform sets for each block rather than requiring a large universal codebook, thereby maintaining colour representation accuracy while reducing overall storage requirements
Solution Approach 2:
The invention changes the compression approach by using multiple possible compression transforms with different parameters rather than a single large codebook. Each block can be compressed using the most appropriate transform from the set, allowing adaptive parameter selection that maintains accuracy while avoiding the need for a very large fixed codebook
2Quantity of substance
If colour-space conversion is applied to compress image data, then storage requirements are reduced, but certain encodings produce values outside the range of the converted space
Solution Approach 1:
The invention employs dynamic transform selection where the compression transform applied to each block is chosen based on the block's local characteristics. This dynamic approach ensures that the transformed values remain within the valid range for the target colour space, maintaining data integrity while achieving compression. The system adapts the transform parameters to the local data distribution rather than applying a fixed conversion
Solution Approach 2:
The invention changes the approach by using multiple compression transforms with different parameters rather than a single colour-space conversion. By selecting the appropriate transform parameters for each block based on its characteristics, the system ensures that encoded values remain within the valid range of the converted space, preventing out-of-range values while achieving effective compression
Data Source
AI summary
A method of compressing image data comprising a set of image values each representing a position in image-value space so as to define an occupied region thereof. The method comprises selectively applying a series of compression transforms to subsets of the image data items to generate a transformed set of image data items occupying a compacted region of value space. The method further comprises identifying a set of one or more reference data items that quantizes the compacted region in value space. For each image data item in the set of image data items, a sequence of decompression transforms from a fixed set of decompression transforms is identified that generates an approximation of that image data item when applied to a selected one of the one or more reference data items. Each image data item in the set of image data items is encoded as a representation of the identified sequence of decompression transforms for that image data item. The encoded image data items, set of reference data items and the fixed set of decompression transforms are stored as compressed image data.