Cluster-based color space optimizations

Cheryl Lau, W. Heidrich, Rafał K. Mantiuk
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引用次数: 58

Abstract

Transformations between different color spaces and gamuts are ubiquitous operations performed on images. Often, these transformations involve information loss, for example when mapping from color to grayscale for printing, from multispectral or multiprimary data to tristimulus spaces, or from one color gamut to another. In all these applications, there exists a straightforward “natural” mapping from the source space to the target space, but the mapping is not bijective, resulting in information loss due to metamerism and similar effects. We propose a cluster-based approach for optimizing the transformation for individual images in a way that preserves as much of the information as possible from the source space while staying as faithful as possible to the natural mapping. Our approach can be applied to a host of color transformation problems including color to gray, gamut mapping, conversion of multispectral and multiprimary data to tristimulus colors, and image optimization for color deficient viewers.
基于集群的色彩空间优化
不同颜色空间和色域之间的转换是在图像上执行的普遍操作。通常,这些转换涉及信息丢失,例如,从彩色映射到灰度打印,从多光谱或多原色数据映射到三刺激空间,或从一个色域映射到另一个色域。在所有这些应用中,都存在从源空间到目标空间的直接的“自然”映射,但这种映射不是双射的,导致由于同质性和类似效果而导致的信息丢失。我们提出了一种基于集群的方法来优化单个图像的转换,以尽可能多地保留来自源空间的信息,同时尽可能忠实于自然映射。我们的方法可以应用于许多颜色转换问题,包括颜色到灰度、色域映射、多光谱和多原色数据到三刺激颜色的转换,以及针对色差观众的图像优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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