紀要論文

SpeedDeblur : A Framework to speed up CNN-based Deblurring for HEVC compressed video

eng

24368083

https://doi.org/10.15002/00025359

法政大学大学院理工学研究科

法政大学大学院紀要. 理工学研究科編

63

1

6

2022-03-24

Version of Record

This paper proposes a speedup convolutional neural network (CNN)-based deblurring framework (SpeedDeblur) for reconstructed blurry videos. First, we extract the coding information and the reconstructed video from the compressed data. Second, a CNN-based algorithm is used for deblurring the first reconstructed frame. Pixels of the deblurred frames are transferred to the subsequent frames guided by HEVC decoded data. The transferring process is simple and faster than applying a deblurring algorithm on all frames. However, passing pixels throughout a long video propagates accumulated errors and reduces the deblurring performance. To bridge this gap, we design an adaptive reset strategy for deciding which frame needs CNN-based deblur during the transfering process. Besides, a data generation strategy simulating blurry real-world factors such as camera shake and fast movement is proposed. Compared to frame-by-frame deblurring approaches, our framework can retain the same comparable results and boost the deblurring processing by up to 4.0× and 99.4× on GPU and CPU, respectively.

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