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Image naturalness is a potent attribute to enhance image quality, and it is related to people's perception of memory colors. Many previous experiments aimed to locate the memory colors on different media. Their findings indicate that the memory colors obtained vary between color patches and images and between experimental techniques. However, there were no satisfying models with appropriate memory color centers to scale the image naturalness. This study generated an image database including three prototypical memory colors images (sky, grass, and skin) and their local processed images. First, visual naturalness and preference data were collected from a psychophysical experiment. Then, the distribution of naturalness followed by memory color positions was analyzed. Based on the distribution pattern, the ellipsoid models for each prototypical memory color were established. The results from the present memory color centers were compared with those from the other studies. Results show that the present color centers always give better prediction accuracy in image naturalness models. Finally, an image preference model was developed based on the image naturalness and colorfulness. [ABSTRACT FROM AUTHOR]

The study aims to propose an approach of white LED spectral optimization based on mesopic luminance and color gamut volume for dim lighting conditions. Three optimal white LED spectra with relatively higher mesopic luminance and color gamut volume, the highest mesopic luminance, and the largest gamut volume are recommended for reducing energy consumption and enhancing color perception and recognition of human eyes. The theoretical simulation shows that the spectra with higher correlated color temperatures (CCT) and S/P-ratio increase the mesopic luminance and also extend the range of color gamut with the decreasing of lighting level. An evaluation model is developed to faster predict mesopic luminance, color gamut volume, and S/P ratio for lighting applications. [ABSTRACT FROM AUTHOR]

The CIECAM02 color-appearance model enjoys popularity in scientific research and industrial applications since it was recommended by the CIE in 2002. However, it has been found that computational failures can occur in certain cases such as during the image processing of cross-media color reproduction applications. Some proposals have been developed to repair the CIECAM02 model. However, all the proposals developed have the same structure as the original CIECAM02 model and solve the problems concerned at the expense of losing accuracy of predicted visual data compared with the original model. In this article, the structure of the CIECAM02 model is changed and the color and luminance adaptations to the illuminant are completed in the same space rather than in two different spaces, as in the original CIECAM02 model. It has been found that the new model (named CAM16) not only overcomes the previous problems, but also the performance in predicting the visual results is as good as if not better than that of the original CIECAM02 model. Furthermore the new CAM16 model is simpler than the original CIECAM02 model. In addition, if considering only chromatic adaptation, a new transformation, CAT16, is proposed to replace the previous CAT02 transformation. Finally, the new CAM16-UCS uniform color space is proposed to replace the previous CAM02-UCS space. A new complete solution for color-appearance prediction and color-difference evaluation can now be offered. [ABSTRACT FROM AUTHOR]