An Efficient Image Interpolation Using Edge-error Based Sharpening

Panda, Jagyanseni; Meher, Sukadev

doi:10.1109/indicon49873.2020.9342212

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…FRUC is divided into two types including the motioncompensated FRUC (MC-FRUC) and non-MC-FRUC [13]. Non-MC-FRUC interpolates the absent frames by copying the previous frame or averaging the previous frame and the following frame, and it is suitable for low-speed videos.…”

Section: Introductionmentioning

confidence: 99%

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

Hao

Sun

et al. 2021

Security and Communication Networks

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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.

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Section: Introductionmentioning

confidence: 99%