Content-Adaptive Image Compressed Sensing Using Deep Learning

Liqun Zhong, Shuai Wan, Leyi Xie, Shun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper proposes a framework of content-adaptive image compressed sensing using deep learning, which analyzes the image content and adaptively allocates samples for different image patches accordingly. Experimental results demonstrate that the proposed framework outperforms the state-of-the-arts both in subjective and objective quality, especially at low sampling rates. For example, when the sampling rate is 0.1, 1-6 dB improvement in peak signal to noise ratio (PSNR) is observed. Moreover, the proposed work reconstructs images with more details and less image blocking effects, leading to apparent visual improvement.

Original languageEnglish
Title of host publication2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-61
Number of pages5
ISBN (Electronic)9789881476852
DOIs
StatePublished - 2 Jul 2018
Event10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
Duration: 12 Nov 201815 Nov 2018

Publication series

Name2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

Conference

Conference10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
Country/TerritoryUnited States
CityHonolulu
Period12/11/1815/11/18

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