Abstract
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 language | English |
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Title of host publication | 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings |
Editors | Doug Gray, Doug Cochran |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1240-1244 |
Number of pages | 5 |
ISBN (Electronic) | 9781467369978 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |
Event | IEEE International Conference on Acoustics, Speech and Signal Processing 2015 - Brisbane Convention & Exhibition Centre, Brisbane, Australia Duration: 19 Apr 2015 → 24 Apr 2015 https://ieeexplore.ieee.org/xpl/conhome/7158221/proceeding (Proceedings) |
Conference
Conference | IEEE International Conference on Acoustics, Speech and Signal Processing 2015 |
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Abbreviated title | ICASSP 2015 |
Country/Territory | Australia |
City | Brisbane |
Period | 19/04/15 → 24/04/15 |
Internet address |
Keywords
- Dense correspondence based prediction
- HEVC
- image set compression
- reference reconstruction