Title

Cuneuquant: A Cuda Implementation Of The Neuquant Image Quantization Algorithm

Keywords

CUDA; Image quantization; Kohonen neural network

Abstract

Color quantization is an often performed pre-step 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.

Publication Date

12-1-2012

Publication Title

Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012

Volume

1

Number of Pages

179-185

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84873316614 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84873316614

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