## 5 Discussion  
In this work, we examined an FEC-based framework to enhance the objective video quality of videos that are affected by noisy transmission channels without compromising confidentiality. The foundation of this work was to devise a simple framework that provides improved visual quality to the viewer by incorporating forward error correction in the encoding process. The video is first compressed to accommodate bandwidth limitations due to its large size and then is selectively encrypted for privacy preservation and protection against unauthorized access. FEC is deployed on the encrypted bitstream during the encoding process, offering a joint crypto-encoding framework. It is observed in the results presented in the previous section that the proposed method can recover the errors and sufficiently supports quality enhancement. The comparison of the recovered videos with the original videos was evaluated through the PSNR values of the Y, U, and V components of the videos to examine the results in terms of luminance and chrominance factors. PSNR\({}_{611}\) was used to calculate the collective effect of luma and color components in both erroneous and recovered videos. The results suggested that the PNSR values in each component increased in the FEC decoded videos as compared to the PSNR values of the videos decoded without FEC. The Y component controls the luminance (brightness) and the other two components, U and V, are used to represent chrominance (color). Since the human eye is more sensitive to brightness as compared to color, the results suggested a significant improvement in the Y component. The smaller PSNR values of the encrypted videos showed the better performance of the encryption algorithm. The increased PSNR values meant that the recovered videos had reduced amounts of distortion and the visual quality was restored after decoding with the FEC method. Moreover, it was observed that the application of this approach was not confined to a specific video resolution, as it provided improved outcomes on both CIF and HD test video sequences. The effect of PSNR also varied at different QPs on the test video sequences. Objective visual quality at QP 12 provided better outcomes in terms of color components, whereas a significant increase in luminance was observed at QP 34. As larger QP values preserve less detail in the quantization process, QP 48 provided a minor increase in the perceptual quality of the video. Therefore, it was clearly observed that increasing the QP provided less improvement in video quality as compared to smaller QP values. In summary, the results suggest that it is possible to employ our technique to achieve better objective video quality, avoiding the need for retransmission and complex computational cost. There is a potential trade-off between error correction capability and bandwidth expansion, which depends on the number of redundant bits added to generate codewords using data bits. In the \((c,d)\) encoding scheme, \(r\) bits are added to \(d\) data bits to form a codeword having \(c\) bits. In our (15, 11) encoding scheme, the bandwidth expansion was \(((c-d)/d)\times 100=((15-11)/11)\times 100=36.36\%\). If we further reduce the block size, such as in a (7, 4) encoding scheme, the number of redundant bits will be increased in the original bitstream of data, resulting in a bandwidth expansion of 75%. By contrast, a larger block size such as (255, 247) will incorporate a smaller number of redundant bits in the original bitstream, resulting in lower bandwidth expansion but at the cost of reduced error correction efficiency, specifically, the bandwidth expansion in this case will be 3.24%. Although this approach provides flexibility to adjust the redundancy level according to the available resources, its limitation lies in the fact that choosing a large block size will not be as efficient if the channel is highly noisy and has larger errors.