# No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System
## 5 Discussion  
According to our observations, for temporal information, this edge accumulation was low even for high-motion videos. The perceptual edge difference \(D_{E}\) was calculated using Equation (12), with the results obtained from the original and distorted frames of the Bicycle Riding stereoscopic video shown in Table 2. For low-motion videos, the \(D_{E}\) values were usually in the range from 0.05 to 0.10 and, for high-motion ones between 0.10 and 0.20, as can be observed for the undistorted frames 1, 2, and 3 while those for frames 6 and 7 increased due to packet losses occurring in the previous frame. However, for error or distorted frames, edge difference accumulations could be as high as 1, depending on their percentages of transmission losses and distortions. In Table 2, it can be seen that when distortion or packet loss (P. loss) is added, the disparity index ratio of left and right views (\(D_{n}\)) significantly decreased, a score also applied to measure the dissimilarity (\(S_{m}\)) of the video. Eventually, in Equation (13), the dissimilarity and perceptual variance metrics were merged to construct the QA metric of a 3D video. To demonstrate the effectiveness of our method, we examined two video datasets. To speed up the process, only the most recent received frame was utilized to calculate the edge difference perceptual measure. Different ratings were obtained for frames 2 to 10, as shown in Table 3, with distorted or degraded frames marked as **D** with the frame number. Finally, a video's \(\widetilde{Q}\hat{A}OM_{3D}\) score can be represented as the overall mean of the \(QAOM_{3D}\) scores obtained from each frame. In our approach, the concluding quality scores of the video trials contain 75 frames for different loss settings obtained from our proposed QAOM and two popular algorithms are shown in Table 4.  
StSDLC [42] is a metric which calculates impairments in the range from 0 (low distortion) to 1 (high distortion). In our experiment, for our proposed method to be comparable with the others, we presented its values in reverse, i.e., as 1 to 0. We used the H.264 JM encoder to create several forms of impairments, using 26 and 32 quantization parameter settings to reduce the overall quality of a video and 1% and 3% packet losses simulated by the encoder and, to create distortions, different kinds of compression artifacts and manual degradation. We found that our approach performed equally well as the full-reference-based ones and, most importantly, did not require an original video or image. Therefore, it is very significant for quantifying transmitted or broadcast videos for which the original video is not available at the receiver end. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Org. Video** & **Disp. Index,** & **Ed. Diff.,** & **Imp. Video** & **Disp. Index,** & **Ed. Diff.,** \\
**Sequence** & \(D_{n}\) & \(D_{E}\) & **Sequence** & \(D_{n}\) & \(D_{E}\) \\ \hline Original 1 & 4.155 & - & Original 1 & 4.155 & - \\ Original 2 & 4.164 & 0.1097 & Original 2 & 4.164 & 0.1097 \\ Original 3 & 4.172 & 0.1099 & Original 3 & 4.172 & 0.1099 \\ Original 4 & 4.149 & 0.1120 & **1\% Packet loss 4** & **3.866** & **0.3680** \\ Original 5 & 4.166 & 0.1085 & **1\% Packet loss 5** & **3.776** & **0.3832** \\ Original 6 & 4.162 & 0.1125 & Original 6 & 4.162 & 0.0894 \\ Original 7 & 4.160 & 0.1195 & Original 7 & 4.160 & 0.1340 \\ Original 8 & 4.158 & 0.1212 & **Slotred 8** & **3.497** & **0.4167** \\ Original 9 & 4.170 & 0.1238 & **Slotred 9** & **3.557** & **0.5743** \\  & & & **Distorted \& & \\ Original 10 & 4.169 & 0.1195 & **1\% Packet loss 10** & **3.225** & **0.6608** \\ \hline \end{tabular}
\begin{tabular}{c} \hline \hline Org. = Original, Imp. = Impaired, Disp. = Disparity, Ed. = Edge, Diff. = Difference. \\ \end{tabular}
\end{table} 
Table 2: Experimental analysis of the Bicycle Riding video. method works adequately well in terms of lower computation time.