基于小波变换的红外图像压缩技术
|
摘要
静态图像和运动图像通常都因为其数据量大而难以在实际应用中很好的使用，巨大的数据流严重的消耗了计算机有限的带宽和资源。所以，只有发展通用的、效率较高的视频压缩标准，才能缓解图像数据与有限的计算机资源之间的矛盾。
小波变换作为一种新颖的数学分析工具，因为它可以提供一个比傅立叶分析更为合理的子带—多分辨率分析／时—频表示框架，所以近年来在信号与图像分析、地震勘探、计算机视觉、语音识别与合成、信号奇异性检测与谱估计等众多领域中得到了广泛而有效的应用，其中最为引人注目的是在图像编码领域的进展。本文在介绍小波变换理论的基础上，探讨了图像压缩系统的组成部分及其应用，并且详细讨论了在运用小波变换进行图像压缩过程中，小波滤波器的种类以及如何选取合适的小波滤波器的问题，最后，结合实际工作的需要，研究了在红外图像处理时应用小波变换进行图像压缩的特点及压缩效果。
Still image and digital video data rates are very large. Data rates of this magnitude would consume a lot of the bandwidth ,storage and computing resources in the typical personal computer. For this reason ,image and video compression standards have been developed to eliminate picture redundancy ,allowing information to be transmitted and stored in a compact and efficient manner.
Wavelet transform is a new type of mathematic tools. In time-frequency property,wavelet transform is better than Fourier Transform. Recently many researches such as signal and image processing ,computer visualization and speech recognition/synthesis use wavelet transform ,especially in image coding research. This article first talk about some theories of wavelet,then give some rules about the wavelet selection . Finally it show the fact that wavelet transform perform good result in infrared image.
小波变换作为一种新颖的数学分析工具，因为它可以提供一个比傅立叶分析更为合理的子带—多分辨率分析／时—频表示框架，所以近年来在信号与图像分析、地震勘探、计算机视觉、语音识别与合成、信号奇异性检测与谱估计等众多领域中得到了广泛而有效的应用，其中最为引人注目的是在图像编码领域的进展。本文在介绍小波变换理论的基础上，探讨了图像压缩系统的组成部分及其应用，并且详细讨论了在运用小波变换进行图像压缩过程中，小波滤波器的种类以及如何选取合适的小波滤波器的问题，最后，结合实际工作的需要，研究了在红外图像处理时应用小波变换进行图像压缩的特点及压缩效果。
Still image and digital video data rates are very large. Data rates of this magnitude would consume a lot of the bandwidth ,storage and computing resources in the typical personal computer. For this reason ,image and video compression standards have been developed to eliminate picture redundancy ,allowing information to be transmitted and stored in a compact and efficient manner.
Wavelet transform is a new type of mathematic tools. In time-frequency property,wavelet transform is better than Fourier Transform. Recently many researches such as signal and image processing ,computer visualization and speech recognition/synthesis use wavelet transform ,especially in image coding research. This article first talk about some theories of wavelet,then give some rules about the wavelet selection . Finally it show the fact that wavelet transform perform good result in infrared image.
引文
[1] I. Daubechies.Orithonormal Basis of Compression and compactly supported wavelets.Pure Applied Math.1988,41:909～996.
[2] M. Vetterli and C.herley.Wavelets and filter banks:Theory and design.IEEE Trans.Signal Proc.1992,40:2207～2232.
[3] J. D. Villasenor, B. Belzer, and J. Liao. Wavelet filter evaluation for image compression.IEEE Trans. on Image Processing.1995,4(8): 1053～1060.
[4] O. Rioul.On the choice of Wavelet filters for still image compression.IEEE Data Compression Conference'93.1993,3:550～553.
[5] J. Liu,V.R.Algazi,and R. R. Estes. A comparative study of wavelet image coders.Optical Engineering.1996 35(9).
[6] R. Calderbank,I.Daubechies, W. Sweldens, and B. L.Yeo. Lossless image compression using integer to integer wavelet transforms. IEEE International Conference on Image Processing.1997,1(1):596～599.
[7] I. Daubechies. The wavelet transform, time-frequency localization and signal analysis.IEEE Trans on Information Theory. 1990, 36(5):961～1005.
[8] 汤焱，莫玉龙．第二代小波变换应用于图像的无损压缩编码．中国图象图形学报．2000，5(8)：699～702．
[9] 胡春玲．图象编码时小波基的选择．中国图象图形学报．1998，3(9)：742～745．
[10] A.H.Tewfik, D. Sinha, and P. Jorgensen. On the optimal choice of a wavelet for signal representation.IEEE Trans on information theory.1992,38(2): 747～765.
[11] G. K.Wallace. The JPEG still picture compression standard. Communications of the ACM.1991,34(4):30～45.
[12] R. Coifman, Y. Meyer, S. Quake and V. Wickerhauser. Entropy based algorithms for best basis selections. IEEE Trans. on information theory.1992,38(2):713～718.
[13] A. Kaama and J. Parkkinen. Multiwavelets in Spectral Image Compression. Proceedings of 11'th SCIA.1999,June 7-11:327～334.
[14] 孙仲康等．数字图像处理及应用．国防工业出版社．1985．
[15] 董士海等．图像格式编程指南．清华大学出版社．1994．
[16] 徐孟侠．图像编码的进展．通信学报．1993，14(2)：40～47．
[17] 吴乐南．数据压缩的原理与应用．电子工业出版社．1995．
[18] 王新成．多媒体实用技术(图像分册)．电子科技大学出版社．1995．
[19] 钟玉琢．多媒体计算机技术．清华大学出版社．1993．
[20] 何斌，马天予等．Visual C++数字图像处理．人民邮电出版社．2001．
[21] Virtex-II Platform FPGA Handbook. XILINX. 2000．
[22] Digital Video . Array Microsystems,Inc. 1997．
[23] M. G. Albanesi, I. de Lotto, and L. Carrioli. Image compression by the wavelet decomposition. IEEE Trans. Signal Processing. 1992, 3(3) .
[24] J. Shapiro. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Processing. 1993,41: 3443-3463,
[25] EDMUND REITER. Wavelet compression of medical imagery. Telemedicine Journal. 1996,2(2) : 131-136．
[2] M. Vetterli and C.herley.Wavelets and filter banks:Theory and design.IEEE Trans.Signal Proc.1992,40:2207～2232.
[3] J. D. Villasenor, B. Belzer, and J. Liao. Wavelet filter evaluation for image compression.IEEE Trans. on Image Processing.1995,4(8): 1053～1060.
[4] O. Rioul.On the choice of Wavelet filters for still image compression.IEEE Data Compression Conference'93.1993,3:550～553.
[5] J. Liu,V.R.Algazi,and R. R. Estes. A comparative study of wavelet image coders.Optical Engineering.1996 35(9).
[6] R. Calderbank,I.Daubechies, W. Sweldens, and B. L.Yeo. Lossless image compression using integer to integer wavelet transforms. IEEE International Conference on Image Processing.1997,1(1):596～599.
[7] I. Daubechies. The wavelet transform, time-frequency localization and signal analysis.IEEE Trans on Information Theory. 1990, 36(5):961～1005.
[8] 汤焱，莫玉龙．第二代小波变换应用于图像的无损压缩编码．中国图象图形学报．2000，5(8)：699～702．
[9] 胡春玲．图象编码时小波基的选择．中国图象图形学报．1998，3(9)：742～745．
[10] A.H.Tewfik, D. Sinha, and P. Jorgensen. On the optimal choice of a wavelet for signal representation.IEEE Trans on information theory.1992,38(2): 747～765.
[11] G. K.Wallace. The JPEG still picture compression standard. Communications of the ACM.1991,34(4):30～45.
[12] R. Coifman, Y. Meyer, S. Quake and V. Wickerhauser. Entropy based algorithms for best basis selections. IEEE Trans. on information theory.1992,38(2):713～718.
[13] A. Kaama and J. Parkkinen. Multiwavelets in Spectral Image Compression. Proceedings of 11'th SCIA.1999,June 7-11:327～334.
[14] 孙仲康等．数字图像处理及应用．国防工业出版社．1985．
[15] 董士海等．图像格式编程指南．清华大学出版社．1994．
[16] 徐孟侠．图像编码的进展．通信学报．1993，14(2)：40～47．
[17] 吴乐南．数据压缩的原理与应用．电子工业出版社．1995．
[18] 王新成．多媒体实用技术(图像分册)．电子科技大学出版社．1995．
[19] 钟玉琢．多媒体计算机技术．清华大学出版社．1993．
[20] 何斌，马天予等．Visual C++数字图像处理．人民邮电出版社．2001．
[21] Virtex-II Platform FPGA Handbook. XILINX. 2000．
[22] Digital Video . Array Microsystems,Inc. 1997．
[23] M. G. Albanesi, I. de Lotto, and L. Carrioli. Image compression by the wavelet decomposition. IEEE Trans. Signal Processing. 1992, 3(3) .
[24] J. Shapiro. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Processing. 1993,41: 3443-3463,
[25] EDMUND REITER. Wavelet compression of medical imagery. Telemedicine Journal. 1996,2(2) : 131-136．