On spatial quantization of color images. Puzicha, J., Held, M., Ketterer, J., Buhmann, J., & Fellner, D. Image Processing, IEEE Transactions on, 9(4):666 -682, 4, 2000. doi abstract bibtex Image quantization and digital halftoning, two fundamental image processing problems, are generally performed sequentially and, in most cases, independent of each other. Color reduction with a pixel-wise defined distortion measure and the halftoning process with its local averaging neighborhood typically optimize different quality criteria or, frequently, follow a heuristic approach without reference to any quantitative quality measure. In this paper, we propose a new model to simultaneously quantize and halftone color images. The method is based on a rigorous cost-function approach which optimizes a quality criterion derived from a simplified model of human perception. It incorporates spatial and contextual information into the quantization and thus overcomes the artificial separation of quantization and halftoning. Optimization is performed by an efficient multiscale procedure which substantially alleviates the computational burden. The quality criterion and the optimization algorithms are evaluated on a representative set of artificial and real-world images showing a significant image quality improvement compared to standard color reduction approaches. Applying the developed cost function, we also suggest a new distortion measure for evaluating the overall quality of color reduction schemes
@article{841942,
Author = {Puzicha, J. and Held, M. and Ketterer, J. and Buhmann, J.M. and Fellner, D.W.},
Date-Added = {2012-08-20 14:08:07 +0000},
Date-Modified = {2012-08-20 17:40:00 +0000},
Doi = {10.1109/83.841942},
Issn = {1057-7149},
Journal = {Image Processing, IEEE Transactions on},
Keywords = {artificial images;artificial separation;color images;color reduction;color reduction schemes;cost function;digital halftoning;fundamental image processing problems;halftoning process;heuristic approach;image quantization;local averaging neighborhood;multiscale procedure;optimization;pixel-wise defined distortion measure;quantitative quality measure;real-world images;spatial quantization;image colour analysis;optimisation;},
Month = {4},
Number = {4},
Pages = {666 -682},
Title = {On spatial quantization of color images},
Volume = {9},
Year = {2000},
Abstract = {Image quantization and digital halftoning, two fundamental image processing problems, are generally performed sequentially and, in most cases, independent of each other. Color reduction with a pixel-wise defined distortion measure and the halftoning process with its local averaging neighborhood typically optimize different quality criteria or, frequently, follow a heuristic approach without reference to any quantitative quality measure. In this paper, we propose a new model to simultaneously quantize and halftone color images. The method is based on a rigorous cost-function approach which optimizes a quality criterion derived from a simplified model of human perception. It incorporates spatial and contextual information into the quantization and thus overcomes the artificial separation of quantization and halftoning. Optimization is performed by an efficient multiscale procedure which substantially alleviates the computational burden. The quality criterion and the optimization algorithms are evaluated on a representative set of artificial and real-world images showing a significant image quality improvement compared to standard color reduction approaches. Applying the developed cost function, we also suggest a new distortion measure for evaluating the overall quality of color reduction schemes},
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