Regular Article: Computing Fourier Transforms and Convolutions on the 2-Sphere
Advances in Applied Mathematics
Catadioptric Projective Geometry
International Journal of Computer Vision
A posteriori quantization of progressive matching pursuit streams
IEEE Transactions on Signal Processing
Progressive Coding of 3-D Objects Based on Overcomplete Decompositions
IEEE Transactions on Circuits and Systems for Video Technology
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Omnidirectional images represent a special type of images that are captured by vision sensors with a 360-degree field of view. This work targets the compression of such images by taking into account their particular geometry. We first map omnidirectional images to spherical ones and then perform sparse image decomposition over a dictionary of geometric atoms on the 2D sphere. A coder based on Matching Pursuit and adaptive quantization is finally proposed for efficient compression of omnidirectional images. The experiments demonstrate that the proposed system outperforms JPEG2000 coding of unfolded images. Since most omnidirectional sensors can be parametrized with a spherical camera model, the proposed method is generic with respect to different sensor constructions.