Perceptual image quality assessment based on structural similarity and visual masking

We propose an improved objective image quality assessment method based on the structural similarity and visual masking, called the Perceptual Image Quality Assessment (PIQA). The PIQA contains three similarity measures: the luminance comparison measure, the structure comparison measure, the contrast comparison measure as same as the Structure Similarity (SSIM) and its variants. Firstly, in order to improve the ability of distinguishing the structure information in blurred images and noisy images, we modify the structure comparison measure by using the improved structure tensor which is more efficient for describing the structure information in global areas. Secondly, based on the perceptual characters of Human Visual System (HVS) perceptual process, the contrast masking and neighborhood masking are integrated to the contrast comparison measure. Finally, three measures are pooled together to compute the PIQA metric. Comparing with the state-of-the-art methods including Multi-scale SSIM (MS-SSIM), Visual Signal to Noise Ratio (VSNR) and Visual Information Fidelity (VIF) criterion, simulation results show that our approach is highly consistent with HVS perceptual process, and also delivers better performance.

► Proposing an improved structure comparison measure based on structure tensor.
► Formatting a new JND threshold based on visual masking.
► Giving a penalty function to be fit for visual perception.
► Integrating structural similarity and visual masking to get our measure.