Gamut Instance Segmentation for Memory-Constrained Color Mapping
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Solution Overview
Problem
Current gamut mapping methods face challenges in efficiently mapping colors from a source gamut to a target gamut, especially with wide gamut content on displays, due to differences in color gamuts and the need for flexible memory and computational usage, often requiring complex geometric operations or large memory footprints.
Innovation Solution
The method involves defining a color gamut using gamut instances formed by gamut hulls and components, allowing for convex and non-convex representations to reduce memory footprint and computational load, and enabling flexible gamut boundary information representation based on available resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single Gamut Boundary Description (GBD) with high precision is used, then measurement precision is improved, but device complexity and memory footprint increase
Solution Approach 1:
The patent segments the gamut boundary description into multiple alternative GBDs, each representing different levels of precision. Instead of storing one highly detailed GBD that consumes大量 memory, the system divides the gamut boundary into multiple simpler representations that can be selected based on available memory and computational resources. This segmentation allows the system to achieve high precision when needed while maintaining flexibility to use simpler representations when resources are constrained.
Solution Approach 2:
The patent changes the parameter of precision by providing multiple alternative GBDs with different precision levels. Each alternative GBD represents the same gamut boundary but with varying degrees of detail and computational complexity. The system can dynamically select which precision level to use based on available memory, processing power, and required accuracy, thus resolving the contradiction between precision and complexity.
2Measurement precision
If a single Gamut Boundary Description (GBD) with high precision is used, then measurement precision is improved, but loss of energy increases due to complex geometric operations
Solution Approach 1:
The patent segments the gamut boundary description into multiple alternative GBDs with different computational complexities. By dividing the single high-precision GBD into multiple alternatives, the system can select a simpler GBD for applications where computational energy is constrained, thereby reducing energy consumption while still providing accurate enough representations for the specific use case.
Solution Approach 2:
The patent changes the computational parameter by offering alternative GBDs with varying levels of geometric complexity. Simpler GBDs require fewer geometric operations and less computational energy, while more complex GBDs provide higher precision when computational resources are abundant. This parameter change allows the system to optimize energy consumption based on available resources.
3Measurement precision
If gamut boundary information is stored with high detail, then measurement precision is improved, but quantity of substance (memory usage) increases
Solution Approach 1:
The patent segments the gamut boundary information into multiple alternative representations stored in a data structure. Instead of allocating memory for one highly detailed GBD, the system stores multiple alternative GBDs where each alternative requires less memory individually. The system can then select the appropriate level of detail based on available memory, thus reducing the memory footprint while maintaining the ability to provide high precision when needed.
4Manufacturing precision
If complex geometric operations are used for accurate gamut mapping, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the gamut mapping process into multiple alternative approaches, each corresponding to a different level of geometric operation complexity. By providing alternative Gamut Boundary Descriptions, the system enables selective use of simple versus complex geometric operations based on the specific mapping requirements and available computational resources, thus reducing device complexity while maintaining precision when needed.
Data Source
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AI summary
To describe the actual color gamut, a hierarchical structure is proposed which comprises, from bottom to top: Gamut Components (GC): each GC is a surface, generally described as a set of connected elementary triangles or polygons. Gamut Hulls (GH): each GH is a closed surface formed by the concatenation of connex Gamut Components (GC). Gamut Instances (Gl) : each Gl is an alternative Gamut Boundary Description (GBD) of the same actual gamut and is built by the union of the volume(s) bordered by at least one Gamut Hull (GH). Such a Gamut Boundary Information may be notably used for gamut mapping operations. Among advantages of the invention, are flexibility and adaptation to available memory and bandwidth capabilities.