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Deep Correlated Image Set Compression Based on Distributed Source Coding and Multi-Scale Fusion

Citation Author(s):
Jin Wang, Yunhui Shi, Yinsen Xing, Nam Ling, Baocai Yin
Submitted by:
Jin Wang
Last updated:
5 March 2022 - 11:04am
Document Type:
Presentation Slides
Document Year:
2022
Event:
Presenters:
Jin Wang
Paper Code:
107

Abstract

In this paper, we present a deep correlated image set compression scheme based on Distributed Source Coding(DSC) and multi-scale image fusion. As there exists strong correlation among images in a similar image set, we propose to utilize such correlation to generate side information at decoder side for each image in the set. Specifically, a reference structure of the image set is generated by building a minimum spanning tree according to the similarity between two images at encoder. With the reference structure, the side information of each image to be decoded can be generated based on the decoded reference image. And our network learns the correlation between an image and its side information in the training phase. Based on the principle of DSC, the side information can provide additional information such as rich details at decoder side. To make full use of the side information, the initially decoded image and the additional side information are fused at different scales. A decompressed image enhancement network is introduced to reduce the compression artifacts of the decoded images. Extensive experimental results compared with other mainstream methods validate the superior performance of our scheme in both terms of subjective and objective quality.

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