Fusion-based multiview distributed video coding
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Distributed coding of highly correlated image sequences with motion-compensated temporal wavelets
EURASIP Journal on Applied Signal Processing
Symmetric distributed coding of stereo omnidirectional images
Image Communication
Side information estimation and new symmetric schemes for multi-view distributed video coding
Journal of Visual Communication and Image Representation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Conditions for recovery of sparse signals correlated by local transforms
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Low-rate and flexible image coding with redundant representations
IEEE Transactions on Image Processing
Geometry-Based Distributed Scene Representation With Omnidirectional Vision Sensors
IEEE Transactions on Image Processing
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This paper addresses the problem of distributed image coding in camera neworks. The correlation between multiple images of a scene captured from different viewpoints can be effiiciently modeled by local geometric transforms of prominent images features. Such features can be efficiently represented by sparse approximation algorithms using geometric dictionaries of various waveforms, called atoms. When the dictionaries are built on geometrical transformations of some generating functions, the features in different images can be paired with simple local geometrical transforms, such as scaling, rotation or translations. The construction of the dictionary however represents a trade-off between approximation performance that generally improves with the size of the dictionary, and cost for coding the atoms indexes. We propose a learning algorithm for the construction of dictionaries adapted to stereo omnidirectional images. The algorithm is based on a maximum likelihood solution that results in atoms adapted to both image approximation and stereo matching. We then use the learned dictionary in a Wyner-Ziv multi-view image coder built on a geometrical correlation model. The experimental results show that the learned dictionary improves the rate-distortion performance of the Wyner-Ziv coder at low bit rates compared to a baseline parametric dictionary.