Region-Based Prediction for Image Compression in the Cloud

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we...

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Bibliographic Details
Published in:IEEE transactions on image processing 2018-04, Vol.27 (4), p.1835-1846
Main Authors: Begaint, Jean, Thoreau, Dominique, Guillotel, Philippe, Guillemot, Christine
Format: Article
Language:English
Subjects:
Cloud computing
Computer Science
Correlation
Graph cut
high efficiency video coding (HEVC)
Image coding
Image segmentation
image terms
Lighting
local descriptors
Predictive models
Signal and Image Processing
Transform coding
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Summary:Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2017.2788192