Image Data 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 out-of-range values, 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
1Measurement precision
If vector quantization is used to compress color data, then color space representation is improved, but codebook size becomes very large
Solution Approach 1:
The codebook is divided into multiple sub-codebooks, each handling a specific portion of the color space. This segmentation allows the system to maintain high color representation precision while reducing the size of each individual codebook, as each sub-codebook only needs to cover a limited region of the full color space.
Solution Approach 2:
The patent introduces a hierarchical structure where color values are represented through multiple dimensions: a coarse-level index selecting a sub-codebook and a fine-level index selecting a specific codevector within that sub-codebook. This dimensional transformation enables efficient navigation of the color space without requiring a single large codebook.
2Quantity of substance
If color-space conversion is applied to compress image data, then data storage is reduced, but encoded values may fall outside the valid range
Solution Approach 1:
The patent applies linear transformation parameters (scaling and offset factors) to map color values between different color spaces while ensuring that transformed values remain within valid ranges. By carefully selecting transformation parameters, the system achieves data compression through color-space conversion without producing out-of-range values that would compromise reliability.
3Measurement precision
If more bits are used per color component, then color resolution is increased, but storage requirements increase
Solution Approach 1:
Instead of uniformly allocating bits across all color components and all image data, the patent uses variable-length coding where more bits are allocated to regions requiring higher color resolution while fewer bits are used in regions where lower precision suffices. This partial application of high precision where needed achieves good color resolution overall while significantly reducing total storage requirements.
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.