CNN-Based Suppression of False Contour and Color Distortion in Bit-Depth Enhancement

It is a challenge to transmit and store the massive visual data generated in the Visual Internet of Things (VIoT), so the compression of the visual data is of great significance to VIoT. Compressing bit-depth of images is very cost-effective to reduce the large volume of visual data. However, compre...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-01, Vol.21 (2), p.416
Main Authors: Peng, Changmeng, Cai, Luting, Huang, Xiaoyang, Fu, Zhizhong, Xu, Jin, Li, Xiaofeng
Format: Article
Language:English
Subjects:
bit-depth enhancement
color distortion
convolution neural network
false contour
Visual Internet of Things
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Summary:It is a challenge to transmit and store the massive visual data generated in the Visual Internet of Things (VIoT), so the compression of the visual data is of great significance to VIoT. Compressing bit-depth of images is very cost-effective to reduce the large volume of visual data. However, compressing the bit-depth will introduce false contour, and color distortion would occur in the reconstructed image. False contour and color distortion suppression become critical issues of the bit-depth enhancement in VIoT. To solve these problems, a (BE-AUTO) is proposed in this paper. Based on the convolution-combined-with-deconvolution codec and global skip of BE-AUTO, this method can effectively suppress false contour and color distortion, thus achieving the state-of-the-art objective metric and visual quality in the reconstructed images, making it more suitable for bit-depth enhancement in VIoT.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21020416