Locally-Weighted Template-Matching Based Prediction for Cloud-Based Image Compression
2016
Thanks to the increasing number of images stored in the cloud, external image redundancies can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel cloud-based image coding scheme. Unlike current state-of-the-art systems, our method relies on a data dimensionality reduction technique. A global compensation is associated to a locally-weighted template matching compensation method to predict a reference frame, to be then differential-coded with classic video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared to current image coding solutions.
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