Development of Image-dependent Gamut Mapping Algorithm

本研究旨在开发一种跨不同颜色媒体的图像依赖色域映射算法（GMA），重点关注以下五个主要项目。(A)基于图像依赖概念的非线性色域映射模型的基本设计。(B) 3D图像到设备的色域映射算法（3D I-D GMA）的发展(c)窄色域图像的色域扩展算法研究。(D)从图像中提取关键颜色的愉悦色彩变换算法研究。(E)图像紧凑三维色域边界描述符（GBD）的发展及其在三维I-D gma2000年、2001年和2002年的研究成果分别总结为(1)~(3)、(4)~(6)和(7)~(9)。(1)图像相关GMA和色度划分非线性2D I-D GMA的概念模型(2)三维图像色域外壳表面的自动生成(3)光滑三维色域外壳表面的Overhauser样条函数描述(4)提出的3D I-D GMA的映射实验(5)基于r-image方法的紧凑三维色域边界描述符（GBD）(6)基于高斯直方图规范的色域扩展算法(7)基于主成分匹配的分割彩色区域的愉悦色彩转换(8)(9)基于r-image GBDIn的3D I-D GMA的心理物理实验表明，3D I-D GMA比传统的2D D-D GMA、Clipping GMA和典型的3D D-D GMA具有更好的性能。所提出的I-D GMA特别适用于具有高度饱和连续色调的大色域CG图像。此外，本文还提出了一种基于r-image的GBD方法，可以快速比较图像和器件之间的色域大小。

This research has been addressed to develop an Image-dependent Gamut Mapping Algorithm (GMA) across the different color media focusing on the following five major items.(A) Basic design of nonlinear gamut mapping model based on the concept of Image-dependence.(B) Development of 3D Image-to-Device Gamut Mapping Algorithm (3D I-D GMA)c Research on Gamut Expansion Algorithm for narrow gamut images.(D) Research on Pleasant Color Transform Algorithm by key color extraction from image.(E) Development of compact 3D Gamut Boundary Descriptor (GBD) for image and its application to 3D I-D GMAOur R&D results during 2000, 2001, and 2002 are summarized in (1)〜(3), (4)〜(6) and (7)〜(9), respectively as follows.(1) Conceptual model of Image-dependent GMA and Hue-divided nonlinear 2D I-D GMA(2) Automatic formation of 3D Image Gamut Shell Surface(3) Description of smoothed 3D Gamut Shell Surface by Overhauser Spline Function(4) Mapping Experiments by the proposed 3D I-D GMA(5) Compact 3D Gamut Boundary Descriptor (GBD) by r-image method(6) Gamut expansion algorithm based on Gaussian Histogram Specification(7) Pleasant color conversion by Principal Component Matching in segmented colored areas(8) Compression of 3D r-image GBD by SVD and Wavelet image compression techniques(9) Psychophysical experiments on 3D I-D GMA using r-image GBDIn conclusion, 3D I-D GMA resulted in the better performance than the conventional 2D D-D GMA, Clipping GMA, and typical 3D D-D GMAs. The proposed I-D GMA worked very well especially for the wide-gamut CG image with highly saturated continuous tone. Besides, a novel GBD by r-image method was very useful for the quick comparison of the gamut sizes between image and device.