Blind Quality Assessment Using Channel-Based Structural, Dispersion Rate Scores, and Overall Saturation and Hue for Underwater Images

In underwater subsea environments light attenuation, water turbidity, and limitations of the optical devices make the captured images suffer from poor contrast and quality, proportional degradation, low visibility, and low color richness. In recent years, various image enhancement techniques have been applied to improve the image quality, resulting in a new challenge, i.e., the quality assessment of the underwater images. In this study, we introduce an innovative and versatile blind quality assessment method for underwater images without using any references. Our approach leverages structural and contour-based metrics, combined with dispersion rate analysis, to quantify image degradation and color richness within an opponent color space. Specifically, we measure the proportional degradation by computing the edge magnitude using the directional Kirsch kernels, strengthened by image contour and saliency maps. To assess the color quality, chrominance dispersion rates and the overall saturation and hue are used to capture color distortions introduced by enhancement methods. The final quality score is obtained via a multiple linear regression model trained on extensive data sets. Experiments on three benchmark data sets have demonstrated the superior accuracy, consistency, and computational efficiency of the proposed method for both raw and enhanced underwater images.