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 values outside the converted space, 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 accurate reconstruction of original image data items.
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 compression 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 values, the system divides it into parent codebooks and child codebooks, where each level handles a portion of the colour space. This segmentation reduces the size of any single codebook while maintaining comprehensive colour representation capability.
Solution Approach 2:
The patent introduces a hierarchical dimension to the codebook structure, organizing codebooks in multiple levels rather than a single flat layer. This dimensional transformation allows the system to navigate through progressively finer colour space resolutions, achieving both compression and colour accuracy without requiring a single large codebook.
2Quantity of substance
If colour-space conversion is applied to compress image data, then storage requirements are reduced, but converted values may fall outside the expected space range affecting data accuracy
Solution Approach 1:
The patent employs dynamic clipping and remapping operations that adapt to the specific characteristics of the converted colour values. Rather than using fixed conversion parameters, the system dynamically adjusts the clipping thresholds and remapping transformations to ensure converted values remain within the valid colour space range while preserving as much original colour information as possible.
Solution Approach 2:
The patent changes the parameters of the colour space conversion process by introducing adaptive clipping and remapping transformations. These parameter adjustments allow the system to handle out-of-range converted values by transforming them back into the valid colour space, thereby maintaining data accuracy while still achieving compression benefits from the colour-space conversion.
3Manufacturing precision
If complete colour values are stored for each pixel, then colour accuracy is maintained, but storage requirements increase significantly
Solution Approach 1:
The patent merges multiple colour values that are similar or identical into shared representations within the hierarchical codebook structure. By grouping pixels with similar colours and representing them through common codebook entries, the system maintains accurate colour representation for all pixels while significantly reducing the total storage required, as identical or similar colours are stored once and referenced multiple times.
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.