No-reference image quality assessment (NR-IQA)
is crucial for evaluating perceptual quality without reference
images. Existing NR-IQA models for contrast-distorted
images primarily rely on luminance-based Natural Scene
Statistics (NSS), often neglecting chromatic information.
This study introduces two perceptually motivated color
features—colorfulness (CIELab) and color naturalness
(CIELuv)—into the NR-IQA framework. Experiments on
three benchmark databases (TID2013, CID2013, and CSIQ)
demonstrate that incorporating these color features consistently
improves predictive accuracy, with up to 30% higher PLCC
and notable reductions in RMSE. These findings confirm that
color cues complement luminance-based features and enhance
the reliability of contrast-distortion assessment.

NR-IQA, NSS, Contrast distortions, image colorfulness, naturalness, perception quality metrics