Generation-Based Joint Luminance-Chrominance Learning for Underwater Image Quality Assessment

Underwater enhanced images (UEIs) are affected by not only the color cast and haze effect due to light attenuation and scattering, but also the over-enhancement and texture distortion caused by enhancement algorithms. However, existing underwater image quality assessment (UIQA) methods mainly focus on the inherent distortion caused by underwater optical imaging, and ignore the widespread artificial distortion, which leads to poor performance in evaluating UEIs. In this paper, a novel mapping-based underwater image quality representation is proposed. We divide underwater enhanced images into different domains and utilize a feature vector to measure the distance from the raw image domain to each enhanced image domain. The length and direction of the vector are defined as the enhancement degree and enhancement direction of the image. We construct a best enhancement direction and map other vectors to this direction to obtain the corresponding quality representation. Based on this, a novel network, called generation-based joint luminance-chrominance underwater image quality evaluation (GLCQE), is proposed, which is mainly divided into three parts: bi-directional reference generation module (BRGM), chromatic distortion evaluation network (CDEN), and sharpness distortion evaluation network (SDEN). BRGM is designed to generate two reference images about the unenhanced and the optimal enhanced versions of input UEI. In addition, the distortions in the luminance and chrominance domains of the UEI are analyzed. The luminance and chrominance channels of images are separated and input to SDEN and CDEN respectively to detect different distortions. A multi-scale feature mapping module is proposed in CDEN and SDEN to extract the feature representation of quality in chrominance and luminance of these images respectively. Moreover, a parallel spatial attention module is designed to focus on distortions in structural space by utilizing the different receptive fields of the convolution layer, due to the diverse manifestations of structural loss in the image. Finally, the mapped features extracted by two collaborative networks help the model evaluate the quality of underwater images more accurately. Extensive experiments demonstrate the superiority of our model against other representative state-of-the-art models.