Development and evaluation of gamut extension algorithms

Abstract

In recent years, new display technologies have emerged that are capable of producing colors that exceed the color gamut of broadcast standards. On the other hand, most video content currently remains compliant with the EBU standard and as such, there is a need for color mapping algorithms that make optimal use of the wider gamut of these new displays. To identify appropriate color mapping strategies, we have developed, implemented, and evaluated several approaches to gamut extension. The color rendering performance and robustness to different image content of these algorithms were evaluated against a reference (true-color) mapping. To this end, two psychophysical experiments were conducted using a simulated and actual wide-gamut display. Results show that the preferred algorithm had a dependency on image content, especially for images with skin tones. In both experiments, however, there was preference shown for the algorithm that balances chroma and lightness modulations as a function of the input lightness. The newly designed extension algorithms consistently outperformed true-color mapping, thus confirming the benefit of appropriate mapping on wide-gamut displays.

Vision Models for Wide Color Gamut Imaging in Cinema

“…11a presents the accuracy scores computed by analyzing the psychophysical data of the setup 1 where it can be seen that, when the difference between the source gamut and the destination gamut is large, the proposed GEA yields images that are perceptually more faithful to the reference images than the other competing algorithms. The observers declared SDS [50] as the least accurate method, whereas the algorithm of [56] ranked second.…”

“…For example, labelling each color of a given image as skin or non-skin [46]; dealing with objects of low chroma and high chroma differently [47]; identifying certain memory colors such as green grass and blue sky, and rendering them independently [48]. Other approaches [49], [50] propose three types of extensions: chroma extension, extension along lines from the origin, and adaptive mapping that is a compromise between the first two strategies. Some global GEAs [51], [52], [53] aim at preserving skin tones in the reproduced images.…”

“…The true-color algorithm represents the input image in the target gamut without applying any extension. The HCM algorithm [50] analyzes the saturation of the input image and then linearly combines the output of the true-color method and the SDS method:…”

A new device color gamut boundary description algorithm based on irregular segmentation

“…5 The color gamut can be constructed by gamut boundary descriptors. The construction of a gamut boundary is also the first step in the process of performing color gamut mapping, 6 color gamut extension, 7 spatial color gamut mapping, 8,9 dependent primary control system, 10,11 and so forth. With the visualized color gamut, it can be easier to analyze the reproduction effect of a certain printing material.…”

Preferred color gamut boundaries for wide‐gamut and multiprimary displays

“…The characteristics of the destination wide gamut are of course of prime consideration, but just as important are the characteristics of the starting image, including its color space and its content and context. Preferences for the expansion method itself, meaning how the starting gamut is mapped into the available destination wide gamut, were reported by Laird et al 7 They compared matrix-based enhancements, naïve same-drive-signal (SDS) mappings, and hue-preserving linear and nonlinear chroma boosts, finding a preference for hue-preserving chroma boosts with a nonlinearity that enhanced high-chroma colors more than low-chroma colors. Additionally, the importance of treating some colors in special ways, such as protecting skintones from excessive boosting, is well recognized in gamut mapping.…”