Dense correspondence based prediction for image set compression

Yabin Zhang, Weisi Lin, Jianfei Cai

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.

External IDs:dblp:conf/icassp/ZhangLC15