Evaluating Quality of DIBR-synthesized Views based on Texture and Perceptual Hashing Similarity

Dongsheng Zheng, Huan Zhang, Jiangzhong Cao, Xu Zhang, Ximei Yao, Wing-Kuen Ling

Abstract: Depth-Image-Based Rendering (DIBR) technology is widely used in 3D video systems to synthesize virtual views. However, the DIBR rendering process tends to introduce local and global distortions, especially local geometric distortion, that will severely affect the perception. In addition, traditional 2D quality metrics may fail to handle this issue since only global distortion is considered. Therefore, in order to evaluate the quality of virtual views more accurately, we propose a full reference DIBR-synthesized views quality assessment model that considers both local and global aspects. Local standard deviation texture images of the reference and distorted images are used to detect local distortions due to local distortions in the virtual view result in a large degree of variation in texture information. The intensity similarity and gradient similarity of the texture images are fused to obtain the final local distortion map. The perceptual hash similarity between the reference and the distorted image is used to quantify the global sharpness due to its powerful frequency domain analysis capability. Depending on the experimental results on the IRCCyN/IVC and IETR databases, the performance of our metric is competitive with the state-of-the-art methods.