Image Data Compression with Compacted Value-Space 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 value range issues, which hinder effective data reduction and storage efficiency in computer graphics systems.
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 reducing storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vector quantization is used to represent colour space, then colour data can be compressed, but the codebook becomes very large limiting compression effectiveness
Solution Approach 1:
The patent segments the colour space representation by dividing it into multiple subspaces, each with its own smaller codebook. Instead of using one large codebook to represent the entire colour space, the method creates multiple smaller codebooks that collectively cover the full range, reducing the complexity of any single codebook while maintaining compression effectiveness.
Solution Approach 2:
The patent introduces an additional dimension to the compression approach by using a hierarchy of codebooks organized in multiple levels or dimensions. This allows the system to navigate through different codebook layers rather than relying on a single large codebook, effectively reducing the complexity burden on any individual codebook structure.
2Quantity of substance
If colour-space conversion is applied to compress image data, then storage can be reduced, but certain encodings produce values outside the range of the converted space
Solution Approach 1:
The patent dynamically adjusts the parameters and ranges of the codebooks based on the specific characteristics of the image data being compressed. By adapting the codebook ranges to match the actual data distribution, the system ensures that all encoded values remain within valid ranges for the target colour space, preventing out-of-range encoding issues while maintaining compression efficiency.
Data Source
AI summary
There is 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.