Compression Artifacts Reduction for Depth Map by Deep Intensity Guidance

Pingping ZHANG, Xu WANG, Yun ZHANG, Lin MA, Jianmin JIANG, Sam KWONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)


In this paper, we propose a intensity guided CNN (IG-Net) model, which learns an end-to-end mapping between the intensity image and distorted depth map to the uncompressed depth map. To eliminate the undesired blocking artifacts such as discontinuities around object boundary, two branches are designed to extract the high-frequency information from intensity image and depth map, respectively. Multi-scale feature fusion and enhancement layers are introduced in the main branch to strength the edge information of the restored depth map. Performance evaluation on JPEG compression artifacts shows the effectiveness and superiority of our proposed model compared with state-of-the-art methods.
Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing : PCM 2017
EditorsBing ZENG, Qingming HUANG, Abdulmotaleb El SADDIK, Hongliang LI, Shuqiang JIANG, Xiaopeng FAN
PublisherSpringer, Cham
Number of pages10
ISBN (Electronic)9783319773803
ISBN (Print)9783319773797
Publication statusPublished - 2018
Externally publishedYes
Event18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
Duration: 28 Sept 201729 Sept 2017

Publication series

NameLecture Notes in Computer Science (including subseries Information Systems and Applications, incl. Internet/Web, and HCI)
PublisherSpringer, Cham
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th Pacific-Rim Conference on Multimedia, PCM 2017

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61501299, 61471348, 61672443 and 61620106008, in part by the Guangdong Nature Science Foundation under Grant 2016A030310058, in part by the Shenzhen Emerging Industries of the Strategic Basic Research Project under Grants JCYJ20150525092941043, JCYJ20160226191842793, in part by the Project 2016049 supported by SZU R/D Fund, and in part by the Tencent “Rhinoceros Birds”-Scientific Research Foundation for Young Teachers of Shenzhen University.


  • Compression artifacts
  • Convolutional neural network
  • JPEG compression


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