TY - GEN
T1 - A rapid method for image compression based on wavelet transform and SOFM neural network
AU - Xu, Hongke
AU - Yang, Weisong
AU - Fang, Jianwu
AU - Wen, Changbao
AU - Sun, Wei
PY - 2012
Y1 - 2012
N2 - The current self-organizing feature map (SOFM) neural network algorithm used for image compression, of which a large amount of network training time and the blocking effect in the reconstructed image existed in codebook design vector calculation. Based on the above issue, this paper proposed an improved SOFM. The new SOFM introduced normalized distance between the sum of input vectors and the sum of the codeword vectors as a constraint in the process of searching for the winning neuron, which can remove redundant Euclidean distance calculation in the competitive process. Furthermore, this paper has done image compression by combining wavelet transform with the improved SOFM (WT & improved SOFM). The method firstly conducted wavelet decomposition for the image, retained low-frequency sub-band, then put the high-frequency sub-band into improved SOFM network, and achieved the purpose of compression. Experimental results showed that this algorithm can greatly reduce the network training time and enhance the learning efficiency of neural network, while effectively improve the PSNR (increased 0.6dB) of reconstructed.
AB - The current self-organizing feature map (SOFM) neural network algorithm used for image compression, of which a large amount of network training time and the blocking effect in the reconstructed image existed in codebook design vector calculation. Based on the above issue, this paper proposed an improved SOFM. The new SOFM introduced normalized distance between the sum of input vectors and the sum of the codeword vectors as a constraint in the process of searching for the winning neuron, which can remove redundant Euclidean distance calculation in the competitive process. Furthermore, this paper has done image compression by combining wavelet transform with the improved SOFM (WT & improved SOFM). The method firstly conducted wavelet decomposition for the image, retained low-frequency sub-band, then put the high-frequency sub-band into improved SOFM network, and achieved the purpose of compression. Experimental results showed that this algorithm can greatly reduce the network training time and enhance the learning efficiency of neural network, while effectively improve the PSNR (increased 0.6dB) of reconstructed.
KW - Image compression
KW - Self-organizing feature map neural network
KW - Vector quantitative
KW - Wavelet transform
UR - https://www.scopus.com/pages/publications/82555205149
U2 - 10.4028/www.scientific.net/AMM.135-136.126
DO - 10.4028/www.scientific.net/AMM.135-136.126
M3 - 会议稿件
AN - SCOPUS:82555205149
SN - 9783037852903
T3 - Applied Mechanics and Materials
SP - 126
EP - 131
BT - Advances in Science and Engineering II
T2 - 2011 WASE Global Conference on Science Engineering, GCSE 2011
Y2 - 10 December 2011 through 11 December 2011
ER -