The work for this paper, “CSV: Image quality assessment based on color, structure, and visual system,” was conducted in the Multimedia and Sensors Lab, which is led by AlRegib. The paper presents a full-reference image quality estimator based on color, structure, and visual system characteristics, denoted as CSV. In contrast to the majority of existing methods, Temel and AlRegib quantify perceptual color degradations rather than absolute pixel-wise changes. They used the CIEDE2000, a color difference formulation to quantify low-level color degradations and the Earth Mover’s Distance between color name probability vectors to measure significant color degradations.

In addition to the perceptual color difference, CSV also contains structural and perceptual differences. Structural feature maps are obtained by mean subtraction and divisive normalization, and perceptual feature maps are obtained from contrast sensitivity formulations of retinal ganglion cells. The proposed quality estimator CSV has been tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases, and it is always among the top two performing quality estimators in terms of at least ranking, monotonic behavior, or linearity.