## 3 Proposed Solution
### Forward Error Correction
In contrast, the video in other modes has a little more amount of blurriness. The results exhibit that coarse quantization results in increased values of both blurring and blocking. ### Computational Cost Analysis

In our proposed scheme, the complexity of the encoded \(n\) bits of matrix \(L\) which have the dimension of \((n-k)\) and \(n\), sent over the transmission channel, is at most \((n-1)\), whereas the complexity of the codewords received by the receiver after being passed through the noisy/error-prone channel is k of \((n,k)\) codes. Hence, asymptotically, the worst-case complexity of applied FEC is \(O(k(n-1))\). In comparison with our scheme, in [49], the authors achieved the reduced linear encoding complexity for LT codes of complexity \(O(N_{s}\ log(N_{s}))\) but at the cost of performance loss. The complexity given in [48] is dependent on the number of iterations involved, GoP size and redundant packet allocation for P frames; however, in this method, the packet loss pattern may affect unlike levels of distortion and drop to an average value in the larger number of simulations, which would not happen in our scheme, as we are implementing FEC on codewords, not on individual frames. The execution time was calculated for all the test videos to evaluate the run-time performance with and without FEC. Figure 12 shows the average computational time of recovered test sequences after timings were repeated multiple times. Figure 12 establishes that the average processing time (ms) of reconstructed video with FEC (214, 620, 600, 627, \begin{table}
\begin{tabular}{l|c|c c|c c} \hline \hline
**Videos** & \multicolumn{5}{c}{**Blurring (Average)**} \\ \hline  & **Flower** & **Tempete** & **Mobile** & **Hall** \\ \hline Original & [Y4.64,U.01,7V.015] & [Y5.95,U.1.69,V.09] & [Y8.48,U.22.1,V2.07] & [Y4.25,U.07,1.V.03.99] \\ Encrypted & [Y1.03,U.1.51,V.126] & [Y6.22,U.18.99,V.08] & [Y9.89,U.26.1,V.231] & [Y6.1,U.08.6,V.052] \\ Encrypted with errors & [Y1.04,U.15.9,V.14] & [Y6.52,U.20.7,V.08] & [Y9.94,U.26.7,V.23.9] & [Y5.05,U.08.9,V.053] \\ Decrypted without FEC & [Y6.8,U.13,V.13] & [Y6.19,U.29,V.08] & [Y8.88,U.25,U.23.5,V.23] & [Y4.96,U.20.7,V.043] \\ Decrypted with FEC & [Y5.96,U.19,V.07] & [Y6.07,U.17] & [Y8.62,U.28,V.21.9] & [Y4.53,U.07,V.04] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Blurring for Test Videos. Figure 11: Comparison for Blocking for Error-embedded and recovered videos without and with FEC (VF: video frames). 787, 670) is a little bit higher than reconstructing videos without FEC (186, 491, 502, 500, 697) for test videos i.e., Foreman, Mobile, Crew, Ice, FourPeople, and Video1, respectively. However, the difference in time (ms) appears insignificant, producing values of 28, 123, 98, 127, 90, and 122, respectively, for all test videos and the comparative analysis given in Table 1, which also implies that the cost-benefit trade-off from applying FEC is much in favor of its application for error-prone videos. ### Comparative Analysis

The proposed scheme can help protect video content both against unauthorized access and transmission errors while maintaining the video quality similar to that of the original video. Table 5 is a PNSR-based quantitative comparison of the proposed scheme with: state-of-the-art error correction by STBMA [67]; frame copy concealment by JM (JM-FC) [68]; and other recently proposed approaches [69]. Detailed results (see Table 5 column 6) show that the proposed scheme outperformed other techniques over all test videos. As can be seen, the PSNR difference between the original Intact video sequence, which was then H.264/AVC encoded at QPs of 22 and 32, and other methods of reconstructing the original video after errors had been introduced demonstrate that the proposed scheme (decrypted with FEC) achieved sufficient quality improvement. Figure 12: Comparison of average processing time for recovering videos without and with applying FEC.