Stereo Image Quality Assessment Using Visual Attention and Distortion Predictors

Abstract

Several metrics have been reported in the literature to assess stereo image quality, mostly based on visual attention or human visual sensitivity based distortion prediction with the help of disparity information, which do not consider the combined aspects of human visual processing. In this paper, visual attention and depth assisted stereo image quality assessment model (VAD-SIQAM) is devised that consists of three main components, i.e., stereo attention predictor (SAP), depth variation (DV), and stereo distortion predictor (SDP). Visual attention is modeled based on entropy and inverse contrast to detect regions or objects of interest/attention. Depth variation is fused into the attention probability to account for the amount of changed depth in distorted stereo images. Finally, the stereo distortion predictor is designed by integrating distortion probability, which is based on low-level human visual system (HVS), responses into actual attention probabilities. The results show that regions of attention are detected among the visually significant distortions in the stereo image pair. Drawbacks of human visual sensitivity based picture quality metrics are alleviated by integrating visual attention and depth information. We also show that positive correlation with ground-truth attention and depth maps are increased by up to 0.949 and 0.936 in terms of the Pearson and the Spearman correlation coefficients, respectively.

3D-MAD: A Full Reference Stereoscopic Image Quality Estimator Based on Binocular Lightness and Contrast Perception

“…You et al [11] investigated the capabilities of some 2D IQA algorithms for stereoscopic IQA and found that applying SSIM on stereopairs combined with a mean absolute difference to compute the disparity map distortion always yields the best performance within all possible combinations considered in the paper. Hwang and Wu [47] designed a visual attention and depth-assisted stereo image quality model, which consists of three main components: a stereo attention predictor, a depth variation predictor, and a stereo distortion predictor. Xing et al [48] proposed a perceptual quality estimator which operates based on three main factors (crosstalk level, camera baseline, and scene content) contributing to crosstalk perception in evaluating quality levels of stereoscopic presentations; the quality score of this approach is computed as the average of the SSIM-weighted disparity map.…”

Image Deblurring via Adaptive Proximal Conjugate Gradient Method

“…Each elements of the imaging system contributes to the degradations of the system. In the past years, this subject received an increasing amount of interest [42] [43] [44] [45][46]. The main goal of image restoration is to restore the original image from the degraded scene.…”

A Simple Quality Assessment Index for Stereoscopic Images Based on 3D Gradient Magnitude

“…For measuring the perceived quality of stereoscopic images, several metrics have been proposed by integrating 3D perceptual properties. Hwang and Wu [ 16 ] fused the impacts of visual attention, depth variation, and stereo distortion in the stereo image quality assessment. Bensalma and Larabi [ 17 ] devised a binocular energy quality metric (BEQM) by modeling the complex cells responsible for the construction of the binocular energy.…”