Abstract:

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.