# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 2 Previous Relevant Research
Some recent feature-based approaches also contributed significantly in the NR-based video quality assessment research. [PERSON] et al. [31] provided a potent feature map for NR-VQA that draws inspiration from [PERSON]'s law. It is shown that the first-digit patterns recovered from the video volume data's various transform domains are quality-aware attributes that can be efficiently projected towards sensory quality reporting. Based on the research findings, the authors in [32] suggested a support vector regression (SVR)-based supervised learning strategy to tackle the no-reference video quality assessments (NRVQA) issue. Authors claimed that the suggested method provides satisfactory accuracy on real aberrations and competitive intensity on conventional (fake) aberrations. [PERSON] et al. [33] suggested a fresh working prototype for no-reference video quality assessment (VQA) that is grounded on the inherent characteristics of video's space-time chips. The phrase \"space-time chips\" (ST-chips) refers to a brand-new, quality-aware feature set that we characterize as confined spatial slices of multimedia data that follow the motion flow of the local region. They demonstrated that the parameters from such frameworks are distorted-sensitive and can thus be utilized to forecast the grade of movies using generalized dispersion fitting to the band-pass histograms of space-time chips. [PERSON] et al. proposed a blind video evaluation model (no reference or NR) that is not specific to the [34] distortion. This method is based on the spatial-temporal model of video scenes in the signal processing domain (discrete cosine transform) and a computational model that classifies the motion occurring in the scenes to predict the quality video. Deep learning has received attention in recent years due to the availability of benchmark video QA databases. [PERSON] et al. [2] described a novel, deep learning-based strategy for NR-VQA that used several pre-trained convolutional neural networks (CNN) to classify the probable image and video distortions in parallel. To extract the spatial and temporal features from the stereoscopic videos, [PERSON] et al. [3] proposed a quality assessment method based on a 3D convolution neural network with capturing the disparity information. [PERSON] et al. [35] proposed a synthesized video denoising algorithm based on CNN for the elimination of temporal flicker distortion and enhanced 3D synthetic video perceptual quality. [PERSON] et al. [36] presented a multi-scale feature-directed 3D CNN for QA that used 3D convolution to catch the spatiotemporal features as well as a novel multi-scale unit to accumulate multi-scale information. [PERSON] et al. [4] proposed a no-reference image quality assessment method for measuring 3D composite images based on visual entropy-oriented multi-layer feature analysis. However, all of the above approaches combine high computational complexity, creating longer latency, and thus lack real-time capability for end-user electronics after transmission. In general, it is difficult to judge the quality of the perceived depth because the depth of ground truth is generally not available during transmission. These models can only evaluate depth quality using an estimated disparity map (calculated from an empty stereo pair or a distorted stereo pair) which is significantly affected by the accuracy of the sound. In addition, most existing 3D video quality models are validated using symmetrically encoded videos. In addition, conventional quality assurance methods depend on FR or RR criteria, making it difficult to judge from damaged transmitted or broadcast movies, especially when the original video is not available to the user or receiver. Due to the asymmetric quality of their encryption and the degradation caused by packet loss and network delays, the application of these quality measures to many real-world situations is limited.