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Colour gamut mapping between small and large colour gamuts: Part I. gamut compression.
Optics Express
|May 3, 2018
Summary
This study evaluated gamut compression algorithms (GCAs) and color spaces for mobile applications. The vividness-preserved (VP) GCA performed best in the new Jzazbz color space.
Area of Science:
- Color Science
- Image Processing
- Computer Graphics
Background:
- Gamut mapping is crucial for color management systems.
- Real-time algorithms are needed for mobile applications.
Purpose of the Study:
- To investigate gamut compression algorithm (GCA) performance across uniform color spaces (UCSs).
- To propose and evaluate novel GCAs for real-time mobile applications.
- To assess the impact of different UCSs on GCA performance.
Main Methods:
- Comparison of novel vividness-preserved (VP) and depth-preserved (DP) GCAs with existing methods.
- Evaluation across CIELAB, CAM02-UCS, and Jzazbz uniform color spaces.
- Psychophysical experiments to assess perceptual performance.
Main Results:
- The vividness-preserved (VP) GCA demonstrated superior performance.
- The Jzazbz color space showed promise for gamut mapping applications.
- Algorithm performance varied significantly across different uniform color spaces.
Conclusions:
- The VP GCA is a highly effective method for gamut compression.
- The Jzazbz color space is well-suited for advanced gamut mapping.
- Algorithm and color space selection significantly impacts color reproduction quality.
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