Utilizing typical color appearance models to represent perceptual brightness and colorfulness for digital images

Rui Gong; Qing Wang; Xiaopeng Shao; Conghao Zhou

doi:10.1117/1.OE.55.12.123107

20 December 2016 Utilizing typical color appearance models to represent perceptual brightness and colorfulness for digital images

Rui Gong, Qing Wang, Xiaopeng Shao, Conghao Zhou

Author Affiliations +

Optical Engineering, Vol. 55, Issue 12, 123107 (December 2016). https://doi.org/10.1117/1.OE.55.12.123107

Abstract

This study aims to expand the applications of color appearance models to representing the perceptual attributes for digital images, which supplies more accurate methods for predicting image brightness and image colorfulness. Two typical models, i.e., the CIELAB model and the CIECAM02, were involved in developing algorithms to predict brightness and colorfulness for various images, in which three methods were designed to handle pixels of different color contents. Moreover, massive visual data were collected from psychophysical experiments on two mobile displays under three lighting conditions to analyze the characteristics of visual perception on these two attributes and to test the prediction accuracy of each algorithm. Afterward, detailed analyses revealed that image brightness and image colorfulness were predicted well by calculating the CIECAM02 parameters of lightness and chroma; thus, the suitable methods for dealing with different color pixels were determined for image brightness and image colorfulness, respectively. This study supplies an example of enlarging color appearance models to describe image perception.

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Rui Gong, Qing Wang, Xiaopeng Shao, and Conghao Zhou "Utilizing typical color appearance models to represent perceptual brightness and colorfulness for digital images," Optical Engineering 55(12), 123107 (20 December 2016). https://doi.org/10.1117/1.OE.55.12.123107

Received: 12 July 2016; Accepted: 8 November 2016; Published: 20 December 2016

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KEYWORDS

Light sources and illumination

Visualization

Image segmentation

Binary data

Image processing algorithms and systems

Algorithm development

Image quality

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