Scalable Image Coding Based on Epitomes

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 pixel...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 26; no. 8; pp. 3624 - 3635
Main Authors: Alain, Martin, Guillemot, Christine, Thoreau, Dominique, Guillotel, Philippe
Format: Journal Article
Language:English
Published: United States IEEE 01.08.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Computer Science
Decoding
Distortion
Encoding
Epitome
Image coding
Image Processing
Image reconstruction
Image restoration
Learning
Patches (structures)
Pixels
scalable image coding
SHVC
Spatial resolution
super-resolution
Epitome
SHVC
scalable image coding
super-resolution
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary: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 the significant improvement of the rate-distortion performances can be achieved compared with the Scalable extension of HEVC (SHVC).
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2017.2702396