Underwater Image Quality Assessment Based on Multiscale and Antagonistic Energy

Due to the absorption and scattering of light by water, underwater imaging suffers from severe degradation in quality, which greatly hinders underwater exploration and research. Therefore, achieving the application of quality assessment in underwater visual tasks is very important. To effectively evaluate the quality of underwater images, a novel no-reference underwater image quality assessment (UIQA) method based on multiscale and antagonistic energy distribution, called MSAEQA, is proposed. Specifically, considering that different wavelengths of light attenuate externally in water commonly leading to the color cast of underwater images, a maximum chrominance map is constructed on the Laplacian pyramid to represent the color cast. Then, the statistical distribution of the maximum chrominance map of the underwater image is proposed as a multiscale statistical feature. Furthermore, considering that structure and texture are important attributes of underwater images, multi-scale Laplacian weighted local binary patterns and multiscale histogram of oriented gradient features are extracted as quality perceptual features to capture multiscale underwater structure and texture statistical features. Finally, the Weibull distribution is constructed to simulate the energy distribution of singular value decomposition in the CIELab color space representing the underwater antagonistic energy distribution statistics. Experimental results show that the proposed MSAEQA method exhibits the highest correlation with ground truth scores compared to state-of-the-art UIQA methods.