NIQSV+: A No-Reference Synthesized View Quality Assessment Metric

• Published in: IEEE transactions on image processing 2018-04, Vol.27 (4), p.1652-1664
• Main Authors: Shishun Tian, Lu Zhang, Morin, Luce, Deforges, Olivier
• Format: Article
• Language: English
• Subjects: Computer Science; DIBR; Distortion; Distortion measurement; Image coding; MVD; no-reference; Quality assessment; Signal and Image Processing; Three-dimensional displays; Two dimensional displays; view synthesis
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2017.2781420

Benefiting from multi-view video plus depth and depth-image-based-rendering technologies, only limited views of a real 3-D scene need to be captured, compressed, and transmitted. However, the quality assessment of synthesized views is very challenging, since some new types of distortions, which are inherently different from the texture coding errors, are inevitably produced by view synthesis and depth map compression, and the corresponding original views (reference views) are usually not available. Thus the full-reference quality metrics cannot be used for synthesized views. In this paper, we propose a novel no-reference image quality assessment method for 3-D synthesized views (called NIQSV+). This blind metric can evaluate the quality of synthesized views by measuring the typical synthesis distortions: blurry regions, black holes, and stretching, with access to neither the reference image nor the depth map. To evaluate the performance of the proposed method, we compare it with four full-reference 3-D (synthesized view dedicated) metrics, five full-reference 2-D metrics, and three no-reference 2-D metrics. In terms of their correlations with subjective scores, our experimental results show that the proposed no-reference metric approaches the best of the state-of-the-art full reference and no-reference 3-D metrics; and outperforms the widely used no-reference and full-reference 2-D metrics significantly. In terms of its approximation of human ranking, the proposed metric achieves the best performance in the experimental test.