Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Context Modeling based Depth Image Compression for Distributed Virtual Environment
CW '03 Proceedings of the 2003 International Conference on Cyberworlds
Depth map compression for real-time view-based rendering
Pattern Recognition Letters - Video computing
Image compression by linear splines over adaptive triangulations
Signal Processing
Compressive imaging of color images
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Content adaptive mesh representation of images using binary space partitions
IEEE Transactions on Image Processing
A multiscale framework for compressive sensing of video
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
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We propose in this paper a new scheme based on compressed sensing to compress a depth map. We first subsample the entity in the frequency domain to take advantage of its compressibility. We then derive a reconstruction scheme to recover the original map from the subsamples using a non-linear conjugate gradient minimization scheme. We preserve the discontinuities of the depth map at the edges and ensure its smoothness elsewhere by incorporating the Total Variation constraint in the minimization. The results we obtained on various test depth maps show that the proposed method leads to lower error rate at high compression ratio when compared to standard image compression techniques like JPEG and JPEG 2000.