# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 4 Results
### Discussion  
Table 4 compares the authors' method with the methods of five similar methods or systems of recent years, assessed in their own terms. As might be expected, no one method is without weaknesses or limitations. The authors' method requires further software development, after the current research phase, as the processing latency at the sender is, at the moment, a concern. The work in Reference [12] seems to result in rather limited load reduction, whereas the method of Reference [37] is sensitive to the behavior of SSIM, which appears to be more suitable for assessing relative quality compared to an original image, rather than as a means of comparison across differing frames. The method of Reference [39] can result in low quality video, which might make it difficult to identify objects in surveillance video. Then, the method in Reference [74] is computationally complex, which currently makes it unsuitable for some sensor devices and certainly appears less computationally efficient than the authors' core frame differencing method. Needless to say, there are many positive features, some of which appear in the Merits row of Table 4. In general, it appears that there may be trade-offs between the method of load reduction and the computational efficiency, as in Reference [74]. In fact, whatever the computational gains, the resulting overall video quality is important for the type of applications, that is surveillance ones, that are of interest to this paper's authors. Thus, in some cases, such as with Reference [37], the quality can be erratic. Returning to a discussion of the current paper's research, the results show that transmitted video data are usually reduced by a significant amount through use of the proposed method of video compression allied with motion detection. Additionally, the accuracy of the information and the video quality in general are maintained, as was justified by SSIM and PSNR measurements. Not only is energy conserved by saving excess data from being transferred but also, even with processing overheads for each consecutive frame, acceptable transmission latencies were observed in respect to known applications of an IoMT. Considering the processing overhead and time delays, the frame rates at the receiver were proportionate. Network conditions in the wireless network may also have contributed to delays, though such delays were not this study's focus, which was the video response, and probably warrants a wider investigation given a more extensive network testbed. Average bit-rates increased due to the application of encryption but it is noticeable that the overall bit-rates were generally low, despite the presence of encryption. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Video Sequence** & \multicolumn{3}{c}{**Average PSNR (db)**} \\ \hline  & **Y** & **U** & **V** \\ \hline Walk & 39.0173807 & 40.73244 & 40.99809 \\ Ground & 37.1191006 & 38.05364 & 37.36613 \\ Exercise & 37.1012336 & 40.90042 & 43.62024 \\ Duval Street & 43.7660886 & 48.99123 & 49.87238 \\ Football & 41.0030631 & 42.71924 & 44.67114 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average Peak Signal-to-Noise Ratio (PSNR) for Y, U, V channels of 260 random video frames for all five videos.