Video quality assessment based on LOG filtering of videos and spatiotemporal slice images

Center-surrounded receptive fields, which can be well simulated by the Laplacian of Gaussian (LOG) filter, have been found in the cells of the retina and lateral geniculate nucleus (LGN). With center-surrounded receptive fields, the human visual system (HVS) can reduce the visual redundancy by extracting the edges and contours of objects. Furthermore, current researches on image quality assessment (IQA) have shown that human's perception of image quality can be estimated by the correlation degree between the extracted perceptual-aware features of the reference and test images. Thus, this paper assesses the quality of a video by measuring the similarity of perceptual-aware features from LOG filtering between the test video and reference video. Considering the spatial and temporal channel of the human visual system both include the second derivative of Gaussian function, we first construct a three-dimensional LOG (3D LOG) filter to simulate human visual filter and to extract the perceptual-aware features for the design of VQA algorithms. Moreover, since the correlation measuring based on 2D LOG filtering of video spatiotemporal slice (STS) images can capture the distortion of spatiotemporal motion structure accurately and effectively, then we apply the 2D LOG filtering to video STS images and using maximum pooling for distortion of vertical and horizontal STS images to improve prediction accuracy. The performance of proposed algorithms is validated on the LIVE VQA database. The Spearman's rank correlation coefficients of the proposed algorithms are all above 0.82, which shows that our methods are better than that of most mainstream VQA methods.