Compacted Region Transforms for Image Data Codebook Reduction
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Solution Overview
Problem
Existing methods for compressing and decompressing image data, such as vector quantization and color-space conversion, face limitations due to large codebooks and potential value range issues, which affect their effectiveness in reducing storage requirements and maintaining data accuracy.
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 while maintaining data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vector quantization is used to represent colour space, then colour data compression is achieved, but the codebook becomes very large limiting effectiveness
Solution Approach 1:
The patent segments the large codebook into multiple smaller codebooks organized in a hierarchical structure. Instead of using one large codebook containing all possible colour representations, the system divides it into parent codebooks and child codebooks, where each level handles a subset of the colour space. This segmentation reduces the size of any single codebook while maintaining comprehensive colour representation capability through the hierarchical organization.
Solution Approach 2:
The patent introduces a hierarchical dimension to the codebook structure, transforming a flat, single-level codebook into a multi-level hierarchical codebook. This adds an organizational dimension (levels/hierarchy) to the codebook, allowing navigation through multiple layers of colour representations. The hierarchical structure enables efficient indexing and selection of appropriate codevectors without requiring all codevectors to be present in a single flat structure.
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 patent changes the parameters of the colour space representation by using hierarchical codebooks that adapt to the specific data distribution. Instead of applying fixed colour-space conversion formulas that may produce out-of-range values, the system uses learned codevectors from hierarchical codebooks that are trained on the actual data characteristics. This allows the representation to stay within valid ranges while maintaining compression effectiveness.
Solution Approach 2:
The patent incorporates feedback mechanisms where the hierarchical codebook structure is trained on the actual image data characteristics. The system learns from the data distribution and adjusts the codevectors accordingly, ensuring that encoded values remain within valid ranges. The hierarchical organization allows feedback from multiple levels, refining the representation to match the actual data patterns while maintaining range validity.
Data Source
AI summary
A method of compressing a set of image value data items each representing a position in image-value space so as to define an occupied region thereof. A series of compression transforms is applied to subsets of the image data items to generate a transformed set of image data items occupying a compacted region of value space. A set of one or more reference data items is identified 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 is identified that generates an approximation of that image data item when applied to a selected one of the reference data items. Each image data item is encoded as a representation of the identified sequence of decompression transforms for that image data item. The data items and the decompression transforms are stored as compressed image data.