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CuNeuQuant: A CUDA Implementation of the NeuQuant Image Quantization Algorithm

David Bottisti, Liuva Mendez, Damian Dechev
Department of Computer Science, University of Central Florida, Orlando, FL, USA
The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV’12), 2012

@article{bottisti2012cuneuquant,

   title={CuNeuQuant: A CUDA Implementation of the NeuQuant Image Quantization Algorithm},

   author={Bottisti, D. and Mendez, L. and Dechev, D.},

   year={2012}

}

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Color quantization is an often performed prestep in many image processing and computer vision applications. Quantization is defined as the process of selecting a palette of representative colors P which can replace the original colors C in an image such that |P| << |C| and the perceptual distortion of the reduced color image is minimized. It is well known that the quantization process is an NP-complete problem and as such, many competing heuristic algorithms exist. One high-quality quantization algorithm is NeuQuant due to Dekker. In this paper, we describe a GPU based parallel implementation of the NeuQuant algorithm. Our GPU-based approach demonstrated a speedup by a factor of 5 or more in the performance evaluation we have performed. The details of the NeuQuant algorithm present unique difficulties to implementing a parallel version due to the sequential dependencies present when training the underlying neural network.
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