An O(KN lg N) algorithm for optimum K-level quantization on histograms of N points
CSC '89 Proceedings of the 17th conference on ACM Annual Computer Science Conference
Optimal quantization by matrix searching
Journal of Algorithms
Vector quantization and signal compression
Vector quantization and signal compression
Color quantization by dynamic programming and principal analysis
ACM Transactions on Graphics (TOG)
ACM Transactions on Graphics (TOG)
Image quantization using reaction-diffusion equations
SIAM Journal on Applied Mathematics
Complete ordering and multivariate mathematical morphology
ISMM '98 Proceedings of the fourth international symposium on Mathematical morphology and its applications to image and signal processing
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
Efficient graph-based energy minimization methods in computer vision
Efficient graph-based energy minimization methods in computer vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Convex Optimization
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence of the Lloyd Algorithm for Computing Centroidal Voronoi Tessellations
SIAM Journal on Numerical Analysis
Journal of Mathematical Imaging and Vision
Approximate Labeling via Graph Cuts Based on Linear Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Coding Method for Teleconferencing and Medical Image Sequences
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Computer Vision and Image Understanding
On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows
International Journal of Computer Vision
Convex Multi-class Image Labeling by Simplex-Constrained Total Variation
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
Deblurring Poissonian images by split Bregman techniques
Journal of Visual Communication and Image Representation
Fusion Moves for Markov Random Field Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast image recovery using variable splitting and constrained optimization
IEEE Transactions on Image Processing
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
New algorithms for convex cost tension problem with application to computer vision
Discrete Optimization
Segmentation of images with separating layers by fuzzy c-means and convex optimization
Journal of Visual Communication and Image Representation
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Quantization, defined as the act of attributing a finite number of levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to image analysis tasks, such as denoising and segmentation. In this paper, we investigate vector quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods.