Virtual view quality assessment based on shift compensation and visual masking effect

Depth image based rendering technology is essential for free viewpoint video systems. Because of the compression of depth images and limitations of rendering algorithms, various types of distortion might occur in the virtual viewpoints and cannot effectively be evaluated by traditional two-dimensional assessment methods. Hence, this paper proposes a method for virtual viewpoint quality assessment using the visual masking effect. First, shift is compensated for the distorted virtual viewpoint and then the compensated virtual viewpoint is objectively assessed. Next, according to human visual characteristics such as texture, magnitude, and distribution masking, the corresponding visual sensitivity map and visual masks are extracted. Finally, the visual masking and all factors are pooled to create the final quality score. As verified by the experimental results, the method proposed in this paper corresponds with the characteristics of human vision and can serve as a more effective method for assessing the quality of virtual viewpoints.