# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 2 Previous Relevant Research  
S3D QA algorithms could be categorized based on the perspective binocular perception. The first consists of 2D-based 3D QA models which do not utilize depth information from stereo pairs. In [18], some well-known 2D image quality metrics (PSNR, SSIM, MSSIM, VSNR, VQM, Weighted SNR, and JND) are introduced and their capabilities for the stereoscopic image were investigated. Structural Similarity (SSIM) is a Full-Reference (FR) objective video quality metric proposed by [PERSON] et al. [19] which is based on the  
Figure 2: Binocular disparity and corresponding depth perception in the brain. assumption that human visual perception is highly adapted for extracting structures from a scene. It compares local patterns of pixel intensities normalized for luminance and contrast. In [20], several 2D objective video quality metrics (SSIM, Universal Quality Index (UQI), C4, and RR Image QA (RRIQA)) for left and right images are combined using an average image distortion approach and visual acuity technique based on disparity distortion for the QA of a stereo image. The approaches in [21] apply 2D QA algorithms independently on the left and right perspectives, and then aggregate the two scores (by different means) to forecast 3D quality. In addition, [PERSON] et al. [22] presented a 3D quality score as the weighted sum of the left and right perspectives' quality scores. According to [PERSON] et al. [23], the binocular sense of the quality of asymmetric MPEG-2 distorted stereo pictures is approximately the average of the two views, but the perception of asymmetric blur in a distorted stereo image is mostly influenced by the higher-quality view. Since most approaches accumulate standard 2D image or video features rather than considering the psychovisual aspects of 3D, the outcome directed the investigation of a binocular rivalry or acuity measure by obtaining the disparity between the left and right views. The second category of models considers the binocular (depth) information and estimates the disparity map in the overall process. [PERSON] et al. [1] presented an NR QA approach imitating the HVS perception route, and from the fused and single view, they derived the features throughout the global feature fusion sub-network. [PERSON] et al. [14] developed a new NR stereoscopic image quality perception model that incorporated monocular and binocular features by the relationship between visual features and stereoscopic perception. [PERSON] et al. [24] developed an FR 3D QA algorithm that computes the differences between the quality scores of the left and right videos calculated from reference and distorted views, as well as a distorted disparity map. C4 [25] and SSIM [26] are used to compute these three quality scores, and different combinations of them are employed to obtain the final results. Their findings suggest that discrepancy information can improve the SSIM-based 3D QA algorithm (called 3D-SSIM). [PERSON] et al. [27] expanded the concept of forecasting the 3D quality of stereo pairs by using SSIM and the mean absolute difference (MAD) of their predicted disparity maps. [PERSON] et al. [12] presented a 3D QA technique based on assessing the difference in binocular energy between reference and tested stereo pairs, which takes into account the potential influence of binocular effects on perceived 3D quality. In the RR 3D QA proposed by [PERSON] et al. [28], just the depth map's edge information is sent, and the PSNR of the reference frame is used to estimate the overall score. [PERSON] et al. [29] proposed an NR 3D QA algorithm which extracts features and estimates the disparity map, with a logistic regression model then used to predict the 3D quality scores. [PERSON] et al. [30] introduced a 3D QA model based on the suppression theory of binocular fusion, in which 2D Image Quality Metric (IQM) distortion maps are generated for both the left and right views. Then, a binocular spatial sensitivity module is incorporated with these maps to generate the final quality metric.