Scalable Image Coding based on Epitomes

Abstract : In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome. An epitome can be seen as a factorized representation of an image. Focusing on spatial scalability, the enhancement layer of the proposed scheme contains only the epitome of the input image. The pixels of the enhancement layer not contained in the epitome are then restored using two approaches inspired from local learning-based super-resolution methods. In the first method, a locally linear embedding model is learned on base layer patches and then applied to the corresponding epitome patches to reconstruct the enhancement layer. The second approach learns linear mappings between pairs of co-located base layer and epitome patches. Experiments have shown that significant improvement of the rate-distortion performances can be achieved compared to an SHVC reference.
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IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2017, 26 (8), pp.3624-3635. 〈10.1109/TIP.2017.2702396〉
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https://hal.inria.fr/hal-01591504
Contributeur : Martin Alain <>
Soumis le : jeudi 21 septembre 2017 - 14:45:16
Dernière modification le : jeudi 13 décembre 2018 - 14:32:01

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Martin Alain, Christine Guillemot, Dominique Thoreau, Philippe Guillotel. Scalable Image Coding based on Epitomes. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2017, 26 (8), pp.3624-3635. 〈10.1109/TIP.2017.2702396〉. 〈hal-01591504〉

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