A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network

Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image recons...

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Vydáno v:IEEE transactions on multimedia Ročník 25; s. 4026 - 4040
Hlavní autoři: Wang, Chuntao, Zhang, Tianjian, Chen, Hao, Huang, Qiong, Ni, Jiangqun, Zhang, Xinpeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Cloud computing
Color
Color imagery
Constraints
Cryptography
Customization
Deep learning
Encrypted image compression
Encryption
Feature extraction
Image coding
Image compression
Image reconstruction
residual dense network
Sampling
spatial attention mechanism
super-resolution reconstruction
ISSN:1520-9210, 1941-0077
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Shrnutí:Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.
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ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2022.3171099