CAR-DCGAN: A Deep Convolutional Generative Adversarial Network for Compression Artifact Removal in Video Surveillance Systems

Miloud Aqqa, Shishir Shah

Abstract

Video compression algorithms result in a degradation of frame quality due to their lossy approach to decrease the required bandwidth, thereby reducing the quality of video available for automatic video analysis. These artifacts may introduce undesired noise and complex structures, which remove textures and high-frequency details in video frames. Moreover, they may lead to decreased performance of some core applications in video surveillance systems such as object detectors. To remedy these quality distortions, it is required to restore high-quality videos from their low-quality counterparts without any changes to the existing compression pipelines through a complicated nonlinear 2D transformation. To this end, we devise a fully convolutional residual network for compression artifact removal (CAR-DCGAN) optimized in a patch-based generative adversarial approach (GAN). We show that our model is capable of restoring frames corrupted with complex and unknown distortions with more realistic details than existing methods. Furthermore, we show that CAR-DCGAN can be applied as a pre-processing step for the object detection task in video surveillance systems.

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Paper Citation


in Harvard Style

Aqqa M. and Shah S. (2021). CAR-DCGAN: A Deep Convolutional Generative Adversarial Network for Compression Artifact Removal in Video Surveillance Systems.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 455-464. DOI: 10.5220/0010312304550464


in Bibtex Style

@conference{visapp21,
author={Miloud Aqqa and Shishir Shah},
title={CAR-DCGAN: A Deep Convolutional Generative Adversarial Network for Compression Artifact Removal in Video Surveillance Systems},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={455-464},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010312304550464},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - CAR-DCGAN: A Deep Convolutional Generative Adversarial Network for Compression Artifact Removal in Video Surveillance Systems
SN - 978-989-758-488-6
AU - Aqqa M.
AU - Shah S.
PY - 2021
SP - 455
EP - 464
DO - 10.5220/0010312304550464