Special Section on Perceptually Driven Visual Information Analysis

Application of heterogeneous pulse coupled neural network in image quantization

[+] Author Affiliations
Yi Huang, Yide Ma, Shouliang Li, Kun Zhan

Lanzhou University, School of Information Science and Engineering, No. 222 Tianshui Road, Chengguan District, Lanzhou 730000, China

J. Electron. Imaging. 25(6), 061603 (Aug 08, 2016). doi:10.1117/1.JEI.25.6.061603
History: Received March 16, 2016; Accepted July 12, 2016
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Abstract.  On the basis of the different strengths of synaptic connections between actual neurons, this paper proposes a heterogeneous pulse coupled neural network (HPCNN) algorithm to perform quantization on images. HPCNNs are developed from traditional pulse coupled neural network (PCNN) models, which have different parameters corresponding to different image regions. This allows pixels of different gray levels to be classified broadly into two categories: background regional and object regional. Moreover, an HPCNN also satisfies human visual characteristics. The parameters of the HPCNN model are calculated automatically according to these categories, and quantized results will be optimal and more suitable for humans to observe. At the same time, the experimental results of natural images from the standard image library show the validity and efficiency of our proposed quantization method.

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Citation

Yi Huang ; Yide Ma ; Shouliang Li and Kun Zhan
"Application of heterogeneous pulse coupled neural network in image quantization", J. Electron. Imaging. 25(6), 061603 (Aug 08, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.6.061603


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