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.