Quality Assessment of DIBR-Synthesized Images by Measuring Local Geometric Distortions and Global Sharpness

Depth-image-based rendering (DIBR) is a fundamental technique in free viewpoint video, which is widely adopted to synthesize virtual viewpoints. The warping and rendering operations in DIBR generally introduce geometric distortions and sharpness change. The state-of-the-art quality indices are limited in dealing with such images since they are sensitive to geometric changes. In this paper, a new quality model for DIBR-synthesized view images is presented by measuring LOcal Geometric distortions in disoccluded regions and global Sharpness (LOGS). A disoccluded region detection method is first proposed using SIFT-flow-based warping. Then, the sizes and distortion strength of local disoccluded regions are combined to generate a score. Furthermore, a reblurring-based strategy is proposed to quantify the global sharpness. Finally, the overall quality score is calculated by pooling the scores of local disoccluded regions and global sharpness. Experiments on four public DIBR-synthesized image/video databases show the superiority of the proposed metric over the state-of-the-art quality models. The proposed method is further adopted for boosting the performances of existing quality metrics and benchmarking DIBR algorithms, both achieving very promising results.