Sparse stereo image coding with learned dictionaries

This paper proposes a framework for stereo image coding with effective representation of geometry in 3D scenes. We propose a joint sparse approximation framework for pairs of perspective images that are represented as linear expansions of atoms selected from a dictionary of geometric functions learned on a database of stereo perspective images. We then present a coding solution where atoms are selected iteratively as a trade-off between distortion and consistency of the geometry information. Experimental results on stereo images from the Middlebury database show that the new coder achieves better rate-distortion performance compared to the MPEG4-part10 scheme, at all rates. In addition to good rate-distortion performance, our flexible framework permits to build consistent image representations that capture the geometry of the scene. It certainly represents a promising solution towards the design of multi-view coding algorithms where the compressed stream inherently contains rich information about 3D geometry.


Published in:
IEEE International Conference on Image Processing (ICIP)
Presented at:
IEEE International Conference on Image Processing (ICIP), September, 2011
Year:
2011
Keywords:
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 Record created 2012-01-26, last modified 2018-09-13

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