A multi-view image dataset is highly correlated and redundant. In this paper, we propose a multi-view image compression technique which exploits the inter-frame correlation in a dataset. A frame is divided into patches and sparse coding is applied to each patch, utilising an over-complete dictionary derived from a highly correlated region in the preceding frame and dictionary atoms are built as vector representation overlapping patches of selected region. The degree of sparsity of each patch can be controlled to achieve specified rate-distortion performance. The proposed technique does not require storage or transmission of dictionary atoms and all the frames in the dataset can be compressed by keeping preceding frame as reference. Performance of the proposed compression scheme is compared with JPEG2000 & Depth Layer based techniques and results reveal that proposed scheme outperforms it.
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