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分區(qū)域收斂的快速碼書訓(xùn)練算法

龐朝陽,  孫世新,  潘曄,  龔海瑩

龐朝陽, 孫世新, 潘曄, 龔?，? 分區(qū)域收斂的快速碼書訓(xùn)練算法[J]. 電子與信息學(xué)報(bào), 2002, 24(9): 1282-1286.
引用本文: 龐朝陽, 孫世新, 潘曄, 龔?，? 分區(qū)域收斂的快速碼書訓(xùn)練算法[J]. 電子與信息學(xué)報(bào), 2002, 24(9): 1282-1286.
Pang Chaoyang, Sun Shixin, Pan Ye, Gong Haiying. A fast codebook training algorithm using local clustering[J]. Journal of Electronics & Information Technology, 2002, 24(9): 1282-1286.
Citation: Pang Chaoyang, Sun Shixin, Pan Ye, Gong Haiying. A fast codebook training algorithm using local clustering[J]. Journal of Electronics & Information Technology, 2002, 24(9): 1282-1286.

分區(qū)域收斂的快速碼書訓(xùn)練算法

A fast codebook training algorithm using local clustering

  • 摘要: 該文提出分區(qū)域收斂的快速碼書訓(xùn)練算法LC,它與LBC算法相比,結(jié)構(gòu)簡單、速度快,用典型的測試圖像Lena和Barbara做實(shí)驗(yàn),表明LC算法峰值信噪比只比LBG算法少2％左右,但運(yùn)行速度東LBG的4.61-13.6倍。在比特率為0.375 bpp條件下,LC算法與LBG算法的重建圖像質(zhì)量無明顯差別。
    Abstract: A fast codebook training algorithm using local clustering, named the LC algorithm, is presented in this paper. Comparing with the LBG algorithm, it is simple and fast. The experiment using typical test, images Lena and Barbara shows that the algorithm is 14.61 ~ 13.6 times faster than the LBG algorithm, with its PSNR being reduced by 2% approximately from the LBG algorithm only. The reconstructed images between the LC and the LBG algorithms have no obvious differences at the 0.375 bit/pixel condition.
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  • Y. Linde, A. Buzo, R. M. Gray, An algorithm for vector quantization design, IEEE Trans. onCommum, 1980, COM-28(1), 84-95.[2]高文著,多媒體數(shù)據(jù)壓縮技術(shù),北京,電子工業(yè)出版社,1994年4月第一版,78-111.[3]P. Franti, T. Kaukoranta et al., Fast and memory efficient implementation of the exact PNN,IEEE Trans. on IP, 2000, 9(5), 773-777.[4]Chin-Chen Chang, Yu-Chen Hu, A fast LBG codebook training algorithm for vector quantization,IEEE Trans.on Consumer Electronics, 1998, 44(4), 1201-1208.[5]S. Pan, F. R. McInnes, M. A. Jack, Fast clustering algorithm for vector quantization, PatternRecognition, 1996, 29(3), 511-518.[6]T. Kaukoranta, P. Franti, O. Nevalainnem, Vector quantization by lazy pairwise nearest neighbormethod, Opt. Eng, 1999, 28(11), 1862-1868.[7]V. Ramasubramanian, K. Paliwal, Fast K-dimensional tree algorithms for nearest neighbor searchwith application to vector quantization, IEEE Trans. on SP, 1992, 40(3), 518-531.[8]C.K. Chan, L. M. Po, A Complexity reduction technique for image vector quantization, IEEETrans. on IP, 1992, 1(3), 312-321.[9]A. Buzo, A. H. Gray, R. M. Gray, J. D. Markel, Speech coding based upon vector quantization,IEEE Trans. on, ASSP, 1980, 28(5), 562-574.[10]張基宏,何振亞,一種指數(shù)型模糊學(xué)習(xí)矢量量化圖像編碼算法,通信學(xué)報(bào),1998,19(10),1-6.[11]D. Comaniciu, Model-based training set synthesis for vector quantization, Proc. of the IASTEDInternational Conferrence on Signal and Image Processing, Nassau, Bahamas, October, 18-21,1999, 261-265.[12]D. Comaniciu, Trainng set synthesis for entropy-constrained transform vector quantization, Proc.IEEE ICASSP, Atlanda, 1996, 4, 2036-2039.[13]C-M. Huang, R. W. Harris, A comparison of several vector quantization codebook generationapproaches, IEEE, Trans. on IP, 1993, 2(1), 108-112.[14]沈蘭蓀著,圖像編碼與異步傳輸,北京,人民郵電出版社,1998年5月第1版,1998年5月第1次印刷,26-27.[15]M.R. Garey, D. S. Johnson, H. S. Witsenhausen, The complexity of the generalized Loyd-Maxproblem, IEEE Trans. on IT, 1982, 28(2), 255-256.[16]J. Shanbehzadeh, P. O. Ogunbona, On the computational complexity of the LBG and PNNalgorithms, IEEE Trans. on IP, 1997, 6(4), 614-616.[17]Bernd Fritzke, The LBG-U Method for Vector Quantization-An Improvement over LBG Iinspired from Neural Networks, Klawer Academic Publisher, 1997, 1-10.
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  • 收稿日期:  2001-02-11
  • 修回日期:  2001-10-10
  • 刊出日期:  2002-09-19

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