Color Gamut Mapping Using Area Proportion and Hue Angle
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current color gamut mapping algorithms, especially when mapping large color gamuts to small ones, suffer from poor mapping uniformity and detail loss, leading to image blurring and halo noise due to the large difference in area and mapped line segments.
Innovation Solution
A color gamut mapping method that determines mapping point coordinates by area proportion, adjusts the mapping direction, and uses chromaticity and brightness coordinates to find intersection points on the target gamut borderline, optimizing the mapping process with adjustment parameters to enhance detail and color difference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional gamut mapping algorithms map all color points outside the target gamut to the borderline using minimum color difference principle, then the mapping process is simple, but the mapping uniformity deteriorates and detail information is lost
Solution Approach 1:
The patent segments the gamut mapping process into multiple stages: first identifying the brightest midpoint and establishing a brightness axis, then determining intersection points on the target gamut borderline, and finally calculating mapping points based on area proportions. This segmentation allows different regions to be handled differently, improving mapping uniformity while maintaining computational feasibility.
Solution Approach 2:
The patent applies local quality by treating different regions of the color gamut differently. Specifically, it distinguishes between color points inside the target gamut (which remain unchanged) and color points outside (which require mapping). For external points, it further distinguishes between different angular regions relative to the brightest midpoint, applying area-proportion-based mapping only where needed rather than uniformly to all points.
2Area of stationary object
If large area difference between source and target gamuts is mapped using conventional algorithms, then the mapping coverage is complete, but the mapped line segment difference increases causing image blur and halo noise
Solution Approach 1:
The patent introduces angular dimension (hue angle) to the mapping process. By calculating the hue angle of color points relative to the brightest midpoint and using this angle to determine positioning on the target gamut borderline, it adds a rotational dimension to the mapping. This angular-based approach distributes color points more evenly along the borderline, preventing concentration effects that cause blur and halo noise.
Solution Approach 2:
The patent changes the mapping parameters from simple minimum color difference to a composite approach involving area proportions and angular positions. It calculates mapping points using both the radial distance from the brightest midpoint and the angular position, creating a more nuanced mapping that preserves detail information while achieving complete gamut coverage.
Data Source
AI summary
A method of gamut mapping and color gamut mapping apparatus comprises: acquiring the Lab value of the color point in the original color gamut; and determining the color point in the original color gamut relative to the coordinate location in a hue plane based on the Lab value; and determining the mapping target gamut of the color point based on the coordinate location; If a color point is located outside the mapping target area, it determining the first intersection point of the line connecting the color point and the maximum brightness and the borderline of the mapping target color gamut and the second intersection point of the color point relative to the borderline of the mapping target gamut, and finally according to the preset adjustment parameter, the mapping point being determined on the borderline of the mapping target color gamut relative to the color point.