# Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
## 6 Video Quality Assessment Metrics  
Video quality assessment metrics are essential for evaluating video content's fidelity and perceptual quality [24]. One such metric is as follows:  
* The Structural Similarity Index Measure (SSIM) evaluates the structural similarity between two images by measuring luminance, contrast, and structure. SSIM ranges from \(-1\) to \(1\), where \(1\) indicates perfect similarity. The index is calculated using a formula that incorporates the mean, variance, and covariance of the original and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **FPS** & **SI** & **TI** & **Description** \\ \hline BigBuckBunny & 30 & 96 & 97 & Animation, high motion \\ BirdOfPrey & 30 & 44 & 68 & Natural scenes, smooth motion \\ Cheetah & 25 & 64 & 37 & Animal, camera motion \\ CostaRica & 25 & 45 & 52 & Natural scenes, smooth motion \\ CSGO & 60 & 70 & 52 & Game, average motion \\ FCB & 30 & 80 & 46 & Sports, average motion \\ FrozenBanff & 24 & 100 & 88 & Natural scenes, smooth motion \\ Mtv & 25 & 112 & 114 & Human, average motion \\ PuppiesBath & 24 & 35 & 45 & Animal, smooth motion \\ RoastDuck & 30 & 60 & 84 & Food, smooth motion \\ RushHour & 30 & 52 & 20 & Human, smooth motion \\ Ski & 30 & 61 & 82 & Sports, high motion \\ SlideEditing & 25 & 160 & 86 & Screen content, smooth motion \\ TallBuildings & 30 & 81 & 13 & Architecture, static \\ TearsOfSteel1 & 24 & 53 & 66 & Movie, smooth motion \\ TearsOfSteel2 & 24 & 56 & 11 & Movie, static \\ TrafficAndBuilding & 30 & 66 & 15 & Architecture, static \\ Transformer & 24 & 72 & 56 & Movie, average motion \\ Valentines & 24 & 40 & 52 & Human, smooth motion \\ ZapHighlight & 25 & 97 & 89 & Animation, high motion \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications and variation in reference videos [21].  
Figure 3: A snapshot of Waterloo Database video sequences [21].  
processed images, represented by \(\mu x\), \(\mu y\), \(\sigma x2\), \(\sigma y2\), and \(\sigma xy\). The formula for SSIM is shown in Equation (1): \[SSIM(x,y)=\frac{(2\mu xy+c1)(2\sigma xy+c2)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c1 \right)\left(\sigma_{x}^{2}+\sigma_{y}^{2}+c2\right)}\] (1) where \(c1\) and \(c2\) are constants used to stabilize the division when dealing with a weak denominator.
* Mean Squared Error (MSE) evaluates the average squared difference between corresponding pixels of the original and processed videos, providing a quantitative measure of reconstruction accuracy. Often used alongside SSIM, MSE is calculated by averaging the squared differences in pixel values between the original and processed images, as shown in Equation (2): \[MSE=\frac{1}{mn}{\sum_{i=0}^{m-1}{\sum_{j=0}^{n-1}(I(i,j)-K(i,j))}^{2}}\] (2) where \(I\) and \(K\) represent the intensity values of the corresponding pixels in the original and processed images, and \(m\) and \(n\) are the dimensions of the images.  
These metrics enable objective comparisons between encoding methods, supporting informed decision-making in video processing applications like compression, streaming, and transmission optimization.  
* Peak Signal-to-Noise Ratio (PSNR) is expressed in decibels (dB) and compares the peak signal power to the power of the noise, thus quantifying the level of degradation introduced during compression or transmission. It is computed as the ratio of the maximum possible pixel value squared (usually 255 for 8-bit images) to the MSE between the original and processed images, as shown in Equation (3): \[PSNR=10.\text{log}_{10}\left(\frac{MAX^{2}}{MSE}\right)\] (3)