Stereoscopic Image Quality Assessment Based on both Distortion and Disparity

Abstract

Understanding the characteristics of high-quality stereoscopic 3D (S3D) images has great significance for S3D image classification, quality assessment, and quality enhancement. Existing works assess the quality of an S3D image from a single perspective and use databases with subjective opinion scores obtained by conducting subjective experiments in labs. So the performance of these works in real applications is unclear and questionable. In this paper, we propose an S3D image quality assessment index based on two important factors, namely DisTortion and DisParity (DTDP). Distortion reflects the extent of distortion of each view of an S3D image, respectively. Disparity is a distinguishing factor for an S3D image as compared with a monocular image. We design some features to represent these two factors and use a random forest regression (RFR) to learn the mapping between the features and subjective opinion scores. The database we choose is the NVIDIA 3D VISION LIVE Highest Rated database which is from real application. The images and the subjective opinion scores are directly come from the website viewers all over the world. Experimental results demonstrate a superior performance of the proposed DTDP index as compared to the existing methods.