Dense correspondence based prediction for image set compression

Yabin Zhang, Weisi Lin, Jianfei Cai

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

6 Citations (Scopus)


In this paper, we propose a novel dense correspondence based prediction approach to reduce the inter-image redundancy for image set compression. Unlike previous methods, we manage to utilize the dense correspondence to predict and parameterize the inter-image relation and then reconstruct a new reference for the subsequent HEVC inter-prediction and encoding. Comparing to relevant state-of-the-art feature-based methods, our method is able to locally approximate the inter-image relation and thus more robust to complex local variations. Experimental results show that our proposed approach achieves better coding gains when the local variations are dominant.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
EditorsDoug Gray, Doug Cochran
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781467369978
Publication statusPublished - 2015
Externally publishedYes
EventIEEE International Conference on Acoustics, Speech and Signal Processing 2015 - Brisbane Convention & Exhibition Centre, Brisbane, Australia
Duration: 19 Apr 201524 Apr 2015 (Proceedings)


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing 2015
Abbreviated titleICASSP 2015
Internet address


  • Dense correspondence based prediction
  • HEVC
  • image set compression
  • reference reconstruction

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