Blind Quality Prediction for View Synthesis Based on Heterogeneous Distortion Perception

Abstract

The quality of synthesized images directly affects the practical application of virtual view synthesis technology, which typically uses a depth-image-based rendering (DIBR) algorithm to generate a new viewpoint based on texture and depth images. Current view synthesis quality metrics commonly evaluate the quality of DIBR-synthesized images, where the DIBR process is computationally expensive and time-consuming. In addition, the existing view synthesis quality metrics cannot achieve robustness due to the shallow hand-crafted features. To avoid the complicated DIBR process and learn more efficient features, this paper presents a blind quality prediction model for view synthesis based on HEterogeneous DIstortion Perception, dubbed HEDIP, which predicts the image quality of view synthesis from texture and depth images. Specifically, the texture and depth images are first fused based on discrete cosine transform to simulate the distortion of view synthesis images, and then the spatial and gradient domain features are extracted in a Two-Channel Convolutional Neural Network (TCCNN). Finally, a fully connected layer maps the extracted features to a quality score. Notably, the ground-truth score of the source image cannot effectively represent the labels of each image patch during training due to the presence of local distortions in view synthesis image. So, we design a Heterogeneous Distortion Perception (HDP) module to provide effective training labels for each image patch. Experiments show that with the help of the HDP module, the proposed model can effectively predict the quality of view synthesis. Experimental results demonstrate the effectiveness of the proposed model.