# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 4 Results
### Discussion
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\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Parameters** & **Proposed Method** & **[PERSON] [12]** & **[PERSON] [57]** & **[PERSON] [39]** & **[PERSON] [74]** & **[PERSON] [75]** \\ \hline Objectives & Reduction of communication load in limitation of video data. & Presentes a hybrid-stream scheme for separating partial and temporal information in videos to reduce the data load and improve efficiency in the mobile Internet. & Video streaming enabling lower data storage and maintain precision of data. & A video compression scheme for computationally challenging mobile devices. & Avoid encoding and sending only compressed information of texture regions through the data load and improve efficiency in the mobile Internet. & Region of Interest (ROI) videos and sending only compressed information of texture regions through the texture warping and transmit rates & Region of Interest (ROI) coding for high-quality videos requiring some portions of frames with high resolution and, thus, high transfer rates \\ \hline Merits / Features & -Identification of Region of Motion (ROM) using consecutive frame preprocessing. & -Uber's different stream model to resolve the mobile network overload problem. & -To achieve a lower computational time, the difference between the two frames between the two frames between the two frames. & Classification of images to texture-based regions to identify as static, dynamic and non-featured objects. & Allocation of more bits to texture-based regions and non-featured objects. & the ROI, thus, using more computational and memory resources. \\ - Use of JPEG compression for further information. & -CNN model is used to classify video frames. & -Similar frames are eliminated from the original video to conserve storage & -Warping of static and difference frame is extremely employed. & Maintenance of a short-term and long-term frame buffer for keeping records of previous investigations ROIs, a wide-tracked Video Metric estimate, and allocation of optimal bits. & Maintenance of a short-term and long-term frame buffer for keeping records of the same ROI for multiple previous frames in a long-term frame buffer. \\ - Secure transmission using encryption. & -Key frame identification to achieve better QoE performance. & -For evaluation of video, utilize, rate distortion estimate, and allocation of optimal bits. & -For evaluation of video, utilize, rate distortion estimate, and allocation determines an index map for improving the long-term frame in image, based on similarity and blur distortion and edges. & -After-based Video Metric estimate, and allocation determines an index map for improving the long-term frame while for keeping records of the same ROI for multiple previous frames in a long-term frame buffer. \\ \hline \hline \end{tabular}
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Table 4: Comparative analysis of the proposed method and prior art. \begin{table}
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**Parameters** & **Proposed Method** & **[PERSON] [12]** & **[PERSON] [37]** & **[PERSON] [39]** & **[PERSON] [74]** & **[PERSON] [53]** \\ \hline Dataset & Experimentation on static and moving camera videos having motion in a range from low to high. Mostly covering surveillance video data. & Action Recognition dataset videos taken from YouTube and YouTube data. & Static camera videos & Static and moving camera videos with high texture and video conferencing. & Aerial videos with dynamic videos with high texture and videos such as in video conferencing. & motion and still-camera videos such as in video conferencing. \\ \hline Findings & - Data reduction to a notable extent of values for original and reconstructed frames & An improved classification of video frames over basic CNN and SVM. & A lower value of Average classification of video to the dumping of many different frames. & - A lower value of Average classification of video to the dumping of many different frames.