Selective Visual Attention: Computational Models and Applications

8.2 Use of Visual Attention in Quality Assessment

Visual attention plays an important role in image/video quality assessment (QA). The distortion that occurs on the salient areas should be treated differently from that which occurs on the less salient areas. Let us take Figure 8.5 for example [18]: (a) is a reference image; (b) is the saliency map of (a), and (c)–(f) are distorted images polluted by blur or noise. The difference is that (d) and (f) are polluted in less salient areas (river blurred and river noised), while (c) and (e) are polluted in salient areas (boat blurred and boat noised). We can easily distinguish the quality of two image pairs – the quality of (d) and (f) is better than (c) and (e), respectively. Similar to image QA, we focus much more in the video scene. We can hardly observe all the details in the frame or analyse every object in each salient area because the interval between two frames in the video is so short that we probably grasp only the most salient area. Any distortions that occurs other than in the primary salient area would likely be neglected.

Figure 8.5 Comparison of images with different perceptual qualities: (a) original image; (b) saliency map of (a); (c) image with blurred pollution in salient area; (d) image with blurred pollution in less salient area; (e) image with noised pollution in the salient area; (f) image with noised pollution in a less salient area [18]. Ma, Q., Zhang, L., Wang, B., ‘New strategy for image and video quality assessment’, ...