# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 2 Related Research and Background
### Video Processing
#### 2.2.1 Video Compression  
In an IoMT, the employment of resource-challenged devices has made it necessary to find means of data transmission which can still ensure reduced latencies for a real-time service, adjustable data sizes to accommodate bandwidth restrictions, and the need to reduce energy consumption, as well as power given battery power sources. Among all types of data in IoMT, video data is one of the complex data types, due to, amongst others, its frame structure, color channels, and usually a fragile compressed video stream, which is susceptible to bit errors, as there is a high degree of synchronization across a compressed bitstream. During video streaming, a highly efficient transmission is needed with the advantage of high video quality. Fulfilling either of those requirements will suppress the other, that is, reducing the efficiency of the transmission will reduce the video quality, assuming a fixed bandwidth capacity. Video compression is normally employed to achieve data reduction and, hence, to decrease the bitrate. An encoding scheme, based on HEVC presented in Reference [35] focused on compressing high quality videos, currently with 4K and 8K pixel/frame resolutions. These are notably superior video qualities, consequently with a large amount of information to process. Consequently, for such high-resolution videos, compression schemes also increase in complexity, as does the output bit-rate. The fast coding scheme proposed in Reference [35] serves that purpose, reducing processing time by 12.35% as a result of decreased complexity, while, compared to more complex codecs only results in a bit-rate increment of 0.81% for the reduced computing time. For a reliable video streaming in real-time, there is IoVT platform in Reference [20]. This platform combines efficient video encoding and streaming with low power consumption. Video encoding standards namely, H.264/AVC and particularly HEVC result in sharply reduced bitrates but, because of their complexity, present a challenge to IoT devices. Although, HEVC provides more than 40% bit rate savings [36], compared to H.264/AVC it is still more complex and, thus, is not suitable for these low-resourced devices with low-processing capabilities. A content-sensitive compression technique introduced in Reference [37], measures similarity between consecutive frames, depending upon three factors, that is: luminance, contrast and structure. The frames, which are similar or have small differences, are removed and only those frames are sent which show prominent change. There is another data compression approach [38] for sensor data of different types. Based on an importance of information score, sensor data is extracted and then, using Chebyshev polynomials, the extracted information is compressed. This approach has shown reasonable compression gain. The approach has also been adapted for quasi-periodic and dynamic signals [38]. A video compression technique for mobile devices is reported in Reference [39]. This technique was introduced to enable mobile devices to process and transfer videos. It uses successive frame differences, rate distortion and bit allocation to reduce compression complexity. However, the technique does not handle inter-frame distortion, which results in low PSNR (reduced quality).  
As for commonly used and well-known video compression standards, there are two known families namely Motion Photographic Experts Group (MPEG) and H.26x [30]. Initially, video compression techniques like MPEG-2 were based on image compression techniques, among which JPEG is the main image compression standard used [33; 40]. Other image compression techniques are outlined in Reference [41]. For compressing videos, compression of individual frames can be carried out, as indeed occurs with Motion-JPEG (M-JPEG), which allows very rapid compression of Ultra High-Resolution video. However, as a general solution to video compression, an M-JPEG encoder [42] fails to exploit temporal redundancies, which represent around 80% of the redundancy in videos [30].