No Reference Quality Assessment for 3D Synthesized Views by Local Structure Variation and Global Naturalness Change

Depth image based rendering (DIBR) has been widely used to generate different virtual viewpoints of the same scene from the new perspective. However, DIBR tends to introduce annoying artifacts including blurring, discontinuity, blocking, and stretching, etc.. Thus, to improve DIBR performance, it is important to accurately measure the visual quality of synthesized views. In this paper, we propose a novel and effective no reference (NR) quality assessment method for 3D synthesized views by local variation and global change (LVGC). More specifically, we firstly compute the Gaussian derivatives for the input image to extract structure and chromatic features. Then, we use the local binary pattern (LBP) operator to encode the structure and chromatic feature maps, which are used to calculate quality-aware features to measure the local structural and chromatic distortion. Besides, we extract luminance features by global change to evaluate the naturalness of 3D synthesized views. With these extracted features, we utilize random forest regression (RFR) to train the quality prediction model from visual features to human ratings. Experimental results on three public benchmark databases demonstrate the effectiveness of our method on estimating visual quality of 3D synthesized views.