目的 进一步提高色域映射质量,深入研究空间色域映射算法。 方法 利用高斯拉普拉斯算子对原图的边缘细节进行提取,叠加到映射后图像上再进行二次映射,得到的图像使用结构相关性和图像色差模型进行评价,将数据与最小色差法、CUSP 和 Bala 等人提出的算法进行比较。 结果 基于高斯拉普拉斯算子的色域映射算法的结构相关性和图像色差都比 Bala 等人提出的算法要好。 对于色彩艳丽、细节丰富的图像,空间色域映射算法结构相关性和图像色差反而不如普通算法。 结论 基于高斯拉普拉斯算子的色域映射算法能够提高图像的映射质量,但是空间色域映射算法映射质量并不一定优于非空间类色域映射算法。

Objective To further improve the quality of gamut mapped image, in-depth research was performed under the frame of spatial gamut mapping algorithms in this paper. Methods Laplace of Gaussian function was used to obtain the details of original image to be added in the first mapped image. Then the image was mapped for the second time to make sure the color value was within the target gamut. The data was compared with the minimum color difference method, CUSP and the algorithm proposed by Bala et al. Results Gamut mapping algorithm based on laplace of gaussian function was better than the algorithm proposed by Bala et al in the data of structure similarity and image difference. However, the situation was opposite for images with vivid color and rich details. Conclusion Gamut mapping algorithm based on laplace of gaussian function could improve the quality of gamut mapped image, but the spatial gamut mapping algorithms were not always better than the common algorithms.