Abstract
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.
Highlights
With the rapid development of the internet of things (IoT), more and more machines and autonomous devices are interconnected to produce various communication devices, such as smartphones, tablets, and set-top boxes
In order to select the best motion vectors (MVs) from the set of candidate MVs, bidirectional ME (BME) introduces the sum of bilateral absolute differences (SBAD) criterion. e SBAD of each candidate block is calculated, and the candidate block with the smallest SBAD value is located, and their relative displacement is computed from Bi,j as the best MV, i.e., vi,j arg minSBADBi,j, v, v
The performance of the proposed MC-frame rate up-conversion (FRUC) algorithm is evaluated by transmitting the YUV sequences with a CIF format in a simulation environment of IoT. ese sequences include Foreman, Akiyo, Bus, Football, Mobile, Stefan, Tennis, Flower, News, City, Coastguard, Mother & Daughter, and Soccer. e interpolated results by the proposed algorithm are compared with those that are generated by its two comparing algorithms proposed by Choi et al [20] and Romano and Elad [23]. e comparing algorithms keep
Summary
With the rapid development of the internet of things (IoT), more and more machines and autonomous devices are interconnected to produce various communication devices, such as smartphones, tablets, and set-top boxes. Frame rate up-conversion (FRUC) refers to a technique that increases the frame rate of the transmitted video by exploiting the temporospatial correlations of adjacent frames [6] It can improve the visual quality of the transmitted video, so some real-time applications use it to prevent the degradation of quality. We first extract the features of each video frame by the pretrained network; the extracted features are fused into the bidirectional matching to generate the MVs of the interpolated frame. E pretrained network cannot introduce excessive computations, and extracted features are so rich as to improve the accuracy of BME. In BME, the extracted features are combined with the video frame to perform a bidirectional match. Experiment results show that the extracted feature effectively improves BME accuracy and provide good objective and subjective interpolation qualities.
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