This article proposes Gamma-Compression Gamut Mapping Algorithm (GMA) based on the concept of Image-to-Device. Considering the relations of color gamut boundaries between the devices (such as printer, monitor) and image source. the proposed method adjusts the image color distributions, fitting to the output device gamut. The proposed image-to-device GMA works to preserve its gradation and chroma with minimum losses depending on the color distributions in the segmented hue-leaves. The following three typical mapping methods are compared and discussed: (1) Device-to-Device Gamma-Compression GMA, (2) Image-to-Device Gamma-Compression GMA; (3) Clipping GMA. Two kinds of color spaces, CIEL*a*b* and CIECAM97s-JCh, are used for testing these GMAs. The psychophysical experiments in CIECAM97s-JCh space resulted in better rendition than did CIEL*a*b* space. In natural color imaging applications, the proposed Image-to-Device GMA is superior to conventional Device-to-Device GMA, and is a better way to maintain its gradation and higher chroma. [References: 9]
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机译：本文提出了基于图像到设备的概念的伽玛压缩色域映射算法（GMA）。考虑设备（例如打印机，显示器）和图像源之间的色域边界的关系。所提出的方法调整图像的颜色分布，以适应输出设备的色域。拟议的图像至设备GMA可以根据分段色相叶中的颜色分布，以最小的损失来保持其灰度和色度。比较并讨论了以下三种典型的映射方法：（1）设备到设备Gamma压缩GMA，（2）图像到设备Gamma压缩GMA; （3）裁剪GMA。使用两种颜色空间CIEL * a * b *和CIECAM97s-JCh来测试这些GMA。与CIEL * a * b *空间相比，在CIECAM97s-JCh空间中进行的心理物理实验可产生更好的再现效果。在自然彩色成像应用中，建议的“图像到设备” GMA优于常规“设备到设备” GMA，并且是保持其渐变和更高色度的更好方法。 [参考：9]
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