A Co-Prediction-Based Compression Scheme for Correlated Images

Deep learning has achieved a preliminary success in image compression due to the ability to learn the nonlinear spaces with compact features that training samples belong to. Unfortunately, it is not straightforward for the network based image compression methods to code multiple highly related image...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2020-08, Vol.22 (8), p.1917-1928
Main Authors: Yin, Wenbin, Shi, Yunhui, Zuo, Wangmeng, Fan, Xiaopeng
Format: Article
Language:English
Subjects:
Autoencoder
Binary codes
Coders
Convolutional codes
correlated images
Correlation
Decoding
Image coding
Image compression
Image reconstruction
Machine learning
multi-stream networks
Network management systems
Optimization
Permutations
rate distortion optimization
Transform coding
Transforms
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Summary:Deep learning has achieved a preliminary success in image compression due to the ability to learn the nonlinear spaces with compact features that training samples belong to. Unfortunately, it is not straightforward for the network based image compression methods to code multiple highly related images. In this paper, we propose a co-prediction based image compression (CPIC) which uses the multi-stream autoencoders to collaboratively code the multiple highly correlated images by enforcing the co-reference constraint on the multi-stream features. Patch samples fed into the multi-stream autoencoder, are generated through corresponding patch matching under permutation, which helps the autoencoder to learn the relationship among corresponding patches from the correlated images. Each stream network consists of encoder, decoder, importance map network and binarizer. In order to guide the allocation of local bit rate of the binary features, the important map network is employed to guarantee the compactness of learned features. A proxy function is used to make the binary operation for the code layer of the autoencoder differentiable. Finally, the network optimization is formulated as a rate distortion optimization. Experimental results prove that the proposed compression method outperforms JPEG2000 up to 1.5 dB in terms of PSNR.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2019.2949393