CNN paradigm based multilevel halftoning of digital images. Bakic, P., Vujovic, N., Brzakovic, D., Kostic, P., & Reljin, B. Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on, 44(1):50 -53, 1, 1997. doi abstract bibtex An algorithm for displaying gray level images using a small number of fixed quantization levels is proposed. The algorithm, called multilevel halftoning, is based on the Cellular Neural Networks (CNN) paradigm. It tracks the CNN transient outputs and selects the image which is subjectively perceived to be the best when reduced to the allowed number of gray levels. The selection criterion is based on the ldquo;visually compensated rdquo; mean square error that takes into account the specifics of the human visual system. The results of the proposed algorithm were validated in subjective quality experiments with human subjects
@article{559369,
Author = {Bakic, P.R. and Vujovic, N.S. and Brzakovic, D.P. and Kostic, P.D. and Reljin, B.D.},
Date-Added = {2012-08-20 14:23:59 +0000},
Date-Modified = {2012-08-20 17:22:21 +0000},
Doi = {10.1109/82.559369},
Issn = {1057-7130},
Journal = {Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on},
Keywords = {CNN;algorithm;cellular neural network;digital image;gray level;human visual system;multilevel halftoning;quantization;visually compensated mean square error;cellular neural nets;image processing;quantisation (signal);},
Month = {1},
Number = {1},
Pages = {50 -53},
Title = {CNN paradigm based multilevel halftoning of digital images},
Volume = {44},
Year = {1997},
Abstract = {An algorithm for displaying gray level images using a small number of fixed quantization levels is proposed. The algorithm, called multilevel halftoning, is based on the Cellular Neural Networks (CNN) paradigm. It tracks the CNN transient outputs and selects the image which is subjectively perceived to be the best when reduced to the allowed number of gray levels. The selection criterion is based on the ldquo;visually compensated rdquo; mean square error that takes into account the specifics of the human visual system. The results of the proposed algorithm were validated in subjective quality experiments with human subjects},
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