Refined CIECAM02 for bright surround conditions

Displays such as mobile phones are viewed under surround conditions that vary from dark night to bright sunlight. The overall goal of this study is to test the appropriateness of CIECAM02 for mobile displays, and modify it according to any insufficiency found. Firstly, the testing of CIECAM02 is described using the visual data from 2-inch display of three achromatic backgrounds (gray, black, and white), and three surround conditions (dark, dim, and average). Secondly, CIECAM02 was tested under four surround conditions (dark, dim, average, and bright), and three displays (2-, 4-, and 7-inch), with only the gray background used, to focus more on the surround conditions. Those twelve experimental phases were used to refine CIECAM02, considering the surround factors. The surround parameters (c, F, and NC) were optimized, and Ncb was modified from the CIECAM02; the modified model is named MobileCAM-v1. Colourfulness prediction by MobileCAM-v1 has been improved, especially for a black background under average surround condition. A further refined version of CIECAM02, MobileCAM-v2, was developed, for mobile displays viewed under different surround conditions. A set of equations based on surrounding conditions was first derived, to be able to accurately define surround parameters. The MobileCAM-v2 model gave the largest improvement in brightness, followed by lightness, and with colourfulness the smallest. The improvement is significant for bright surround conditions, improving the performance of CIECAM02 in predicting the visual results.close

Analyzing the metrics of the perceptual space in a new multistage physiological colour vision model

In this work, the metric of a new multistage colour vision model, ATTD05, is assessed and a new colour difference formula is suggested. Firstly, the uniformity of the ATTD05 colour space was compared with that of CIECAM02 for some Munsell samples, because if the model yields a uniform perceptual space, we will be able to implement a colour difference formula as a Euclidian distance between two points. Secondly, we developed a new space based on the perceptual descriptors of the model: brightness, hue, colourfulness, and saturation. After that, we calculated the free parameters of the space that better fit the measured and experimental data of two datasets (small-magnitude and large-magnitude colour differences), by minimizing the performance factor (PF/3). Finally, we compare colour differences calculated for ATTD05 and for other models. The PF/3 and the STRESS parameters were used to decide which model predicts better perceptual differences and, therefore, which model was the more uniform.Spanish Ministry for Education and Science; contract grant numbers: DPI2005-08999-C02-02 and DPI2008-06455-C02-02