A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The data compression book (2nd ed.)
The data compression book (2nd ed.)
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Near real-time motion segmentation using graph cuts
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Video coding by texture analysis and synthesis using graph cut
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
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The compression of natural images and their transmission over multi-hop wireless networks still presents many challenges for the researchers and industry. In this paper we present a new block-based rate-distortion optimization algorithm that can encode efficiently the coefficients of a critically sampled, non-orthogonal or even redundant transform. The basic idea is to construct a specialized graph such that its minimum cut minimizes the energy functional. We propose to apply this technique for rate-distortion Lagrangian optimization in block-based subband image coding. The method yields good compression results compared to the state-of-art JPEG2000 codec, as well as a general improvement in visual quality.