scholarly journals Machine-Type Video Communication Using Pretrained Network for Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ran Li ◽  
Peinan Hao ◽  
Fengyuan Sun ◽  
Yanling Li ◽  
Lei You
Keyword(s):  
Internet Of Things ◽  
Video Transmission ◽  
Frame Rate ◽  
Video Sequences ◽  
Video Communications ◽  
Machine Type ◽  
Video Frames ◽  
Communication Devices ◽  
Frame Rate Up Conversion ◽  
Original Frame

With the increasing demand for internet of things (IoT) applications, machine-type video communications have become an indispensable means of communication. It is changing the way we live and work. In machine-type video communications, the quality and delay of the video transmission should be guaranteed to satisfy the requirements of communication devices at the condition of limited resources. It is necessary to reduce the burden of transmitting video by losing frames at the video sender and then to increase the frame rate of transmitting video at the receiver. In this paper, based on the pretrained network, we proposed a frame rate up-conversion (FRUC) algorithm to guarantee low-latency video transmitting in machine-type video communications. At the IoT node, by periodically discarding the video frames, the video sequences are significantly compressed. At the IoT cloud, a pretrained network is used to extract the feature layers of the transmitted video frames, which is fused into the bidirectional matching to produce the motion vectors (MVs) of the losing frames, and according to the output MVs, the motion-compensated interpolation is implemented to recover the original frame rate of the video sequence. Experimental results show that the proposed FRUC algorithm effectively improve both objective and subjective qualities of the transmitted video sequences.

scholarly journals A Low-Complex Frame Rate Up-Conversion with Edge-Preserved Filtering

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 156 ◽  
Author(s):  
Ran Li ◽  
Wendan Ma ◽  
Yanling Li ◽  
Lei You
Keyword(s):  
Computational Complexity ◽  
Motion Vector ◽  
Frame Rate ◽  
Video Frame ◽  
Continuous Increase ◽  
Video Frames ◽  
Motion Vector Field ◽  
Frequency Components ◽  
Speed Up ◽  
Frame Rate Up Conversion

The improvement of resolution of digital video requires a continuous increase of computation invested into Frame Rate Up-Conversion (FRUC). In this paper, we combine the advantages of Edge-Preserved Filtering (EPF) and Bidirectional Motion Estimation (BME) in an attempt to reduce the computational complexity. The inaccuracy of BME results from the existing similar structures in the texture regions, which can be avoided by using EPF to remove the texture details of video frames. EPF filters out by the high-frequency components, so each video frame can be subsampled before BME, at the same time, with the least accuracy degradation. EPF also preserves the edges, which prevents the deformation of object in the process of subsampling. Besides, we use predictive search to reduce the redundant search points according to the local smoothness of Motion Vector Field (MVF) to speed up BME. The experimental results show that the proposed FRUC algorithm brings good objective and subjective qualities of the interpolated frames with a low computational complexity.

Research on Video Sequences Quality Based on Motion Intensity

2010 ◽  
Vol 20-23 ◽  
pp. 1476-1481
Author(s):  
Chun Ting Yang ◽  
Yang Liu ◽  
Jing Yu
Keyword(s):  
Communication Systems ◽  
Video Transmission ◽  
Quality Metrics ◽  
Video Sequences ◽  
Perceptual Quality ◽  
Quality Metric ◽  
Temporal Correlations ◽  
Video Frames ◽  
Wide Range ◽  
Transmission Quality

Perception video sequences quality metrics are of great potential benefit to the video industry, as they promise the means to evaluate the performance of acquisition, display, coding and communication systems. Many researchers have focused on developing digital video sequences quality metrics which produce results that accurately emulate subjective responses. However, to be widely applicable a metric must also work over a wide range of quality, and be useful for in-service quality monitoring. We have developed novel video distortion metrics for video sequences. The temporal correlations of video frames and the visual interest feature are considered in this method. Meanwhile the metrics are capable of capturing spatial distortions in video sequences. The metrics correlate well with subjective video transmission quality measures because perception distortions of human were took account of. Results are presented that demonstrate our perceptual quality metric performs better than existing methods.

scholarly journals Reinforcement learning based energy-efficient internet-of-things video transmission

2020 ◽  
Vol 1 (3) ◽  
pp. 258-270
Author(s):  
Yilin Xiao ◽  
Guohang Niu ◽  
Liang Xiao ◽  
Yuzhen Ding ◽  
Sicong Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Video Transmission

scholarly journals Research on Key Technology of Real-time Video Transmission Based on Mobile Terminal

1970 ◽  
Vol 1 (2) ◽  
Author(s):  
Cao Pengfei
Keyword(s):  
Video Coding ◽  
Real Time ◽  
Video Transmission ◽  
Loss Rate ◽  
Signal To Noise Ratio ◽  
Mobile Terminal ◽  
Frame Rate ◽  
Minimum Requirement ◽  
Encapsulation Method ◽  
Real Time Transmission

In order to solve the problems existing in real-time video transmission of mobile terminals, this paper proposes the encapsulation method which is suitable for H.263 and H.264 video coding, and re- duces the extra waste of real-time transmission proto- col packets and to improve the transmission efficien- cy of the video. Experimental results show that the peak signal to noise ratio (PSNR) in H.263 and H.264 video coding mode is above 30 dB at the lowest frame rate and resolution, and the minimum requirement of video transmission has been satisfied. Rate of 24 Hz, the two encoding PSNR are more than 40 dB, videotransmission quality ideal. In addition, the two packet loss rate of about10%maximum, themaximumdelay of 400 ms or less, have reached the requirements of real-time videotransmission.

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