基于提升小波的嵌入式图像压缩
摘要
图像在信息获取中具有重要意义，但由于数据量大的特点，给传输和储存带来很大不便。传统的图像压缩方法主要从信息论的角度出发，没有考虑视觉特性，在压缩率和恢复图像的主观质量上不能满足人们的要求。本文主要研究如何利用图像小波变换后系数间的相关性实现小波系数的分级编码和传输，从而实现图像的渐近传输技术。
首先，由于小波分析由于具有多分辨或多尺度特性，在图像处理方面得到广泛应用。提升技术是构造小波和实现小波变换的一种新方法，并可以实现从整数到整数的变换，因此可以成为图像无损压缩的新手段。
其次，由于不同用户对同一图像的质量要求不同，因此有必要在编解码时，使图像的恢复能够根据用户要求，做到从有损到无损的过渡，即图像的渐近编码。
本文主要利用提升算法实现图像的整数小波变换，然后对几种经典的嵌入式图像编码算法进行了研究和比较，在对经过小波变换的图像系数进行统计分析后，提出一种对EZW(Embedded Zerotree Wavelets)算法进行改进的嵌入式图像压缩编码算法。该算法旨在提高图像无损压缩的压缩率，并可用于在进行图像有损压缩时提高恢复图像的主观质量。经过仿真表明，该算法在压缩率和恢复图像的主观质量上优于EZW算法。
Image is important for us to obtain information. But it is difficult to save and transmit because of abundant data. Tradition image compression method mostly consider information theory instead of vision character. So it is not satisfy the people' s need on compression ratio and revert quality. This paper mainly study how to realize wavelet coefficient layered coding and transmission and image data progressive transmission using the correlation of the transformed image.
First, wavelet was widely used in image processing due to its multiresolution or multiscale analysis property. Lifting scheme is a new method for constructing wavelets and performing wavelet transform. At the same time, it can realize transforms that map integers to integers. So lifting scheme will become a new method in lossless image compression.
Secondly, image data should be transmitted from loss to lossless, named progressive transmission , because different people require different image quality.
This paper mainly realize integer wavelet transform using lifting scheme, then study and compare several embedded coding arithmetic. Base on analyzing the image coefficient, we propose an improved EZW(Embedded Zerotree Wavelets) arithmetic. Jt aimed at increasing compression ratio in lossless image compression and subjective quality in loss image compression. Experiment shows that this arithmetic is superior to EZW arithmetic in compression ratio and subjective quality.
首先，由于小波分析由于具有多分辨或多尺度特性，在图像处理方面得到广泛应用。提升技术是构造小波和实现小波变换的一种新方法，并可以实现从整数到整数的变换，因此可以成为图像无损压缩的新手段。
其次，由于不同用户对同一图像的质量要求不同，因此有必要在编解码时，使图像的恢复能够根据用户要求，做到从有损到无损的过渡，即图像的渐近编码。
本文主要利用提升算法实现图像的整数小波变换，然后对几种经典的嵌入式图像编码算法进行了研究和比较，在对经过小波变换的图像系数进行统计分析后，提出一种对EZW(Embedded Zerotree Wavelets)算法进行改进的嵌入式图像压缩编码算法。该算法旨在提高图像无损压缩的压缩率，并可用于在进行图像有损压缩时提高恢复图像的主观质量。经过仿真表明，该算法在压缩率和恢复图像的主观质量上优于EZW算法。
Image is important for us to obtain information. But it is difficult to save and transmit because of abundant data. Tradition image compression method mostly consider information theory instead of vision character. So it is not satisfy the people' s need on compression ratio and revert quality. This paper mainly study how to realize wavelet coefficient layered coding and transmission and image data progressive transmission using the correlation of the transformed image.
First, wavelet was widely used in image processing due to its multiresolution or multiscale analysis property. Lifting scheme is a new method for constructing wavelets and performing wavelet transform. At the same time, it can realize transforms that map integers to integers. So lifting scheme will become a new method in lossless image compression.
Secondly, image data should be transmitted from loss to lossless, named progressive transmission , because different people require different image quality.
This paper mainly realize integer wavelet transform using lifting scheme, then study and compare several embedded coding arithmetic. Base on analyzing the image coefficient, we propose an improved EZW(Embedded Zerotree Wavelets) arithmetic. Jt aimed at increasing compression ratio in lossless image compression and subjective quality in loss image compression. Experiment shows that this arithmetic is superior to EZW arithmetic in compression ratio and subjective quality.
引文
[1] 夏良正，数字图像处理，南京，东南大学出版社，1999．8
[2] 李在铭，数字图像处理压缩与识别技术，成都，电子科技大学出版社，2000．11
[3] Kenneth. R. Castleman. Digital image processing. Prentice-Hall. inc. 1996
[4] J W Wood, S D O' Nell. Subband coding of images [J]. IEEE Trans Acoust Speech, and Signal Proc, 1986, 34 (5)。
[5] S Mallat. A theory for multiresolution decomposition:the wavelet representation [J]. IEEE Trans Pattn Anal Mach Intell, 1989, 11(7).
[6] Sweldens W. The lifting scheme: A custom design construction of biothogonal wavelets. Journal of Appl and Comput Harmonic Analysis. 1996.3 (2).
[7] I. Daubechies, W. Swelends. Factoring wavelet transforms into lifting steps. [J] Fourier Anal Appl, 1996, 4(3).
[8] Shapiro.J.M. Embedded image coding using zerotree of wavelet codfficients. IEEE Transaction on Signal Processing. Vol. 41, NO. 1211993]. P. 3445-3462
[9] A. Said, W. A. Pearlman. A new, fast, and efficient image coder based on set partitioning in hierarchical Trees. IEEE Transactions on Crcuits and Systems for Video Tchnology, Vol. 6, No. 3, 1996 (6). P243-250
[10] A. Islam and W. A. Pearlman. An embedded and efficient low-complexity hierarchical image coder. Visual Communications and Image Processing, Proceedings of SPIE. Vol. 3653, P 294-305, Jan. 1999
[11] [美]崔锦泰 著，程正兴 译，小波分析导论，西安，西安交通大学出版社，1995
[12] 刘贵忠、邸双亮，小波分析及其应用，西安，西安电子科技大学出版社，1992
[13] 李世雄，小波变换及其应用，北京，高等教育出版社，1997
[14] 陈武凡，小波分析及其在图续处理中的应用，北京，科学出版社，2002
[15] Cohen, A. Daubechies, Feauveau. J.C, Biorthogonal bases of compactly supported wavelet. Commun. Pure Appl. Math. 1992.5, 45(5):485-560
[16] Chui. C.K. Wavelets: A tatorial in theory and Applications. Academic Press. San Diego. CA. 1992
[17] Vetterli. M and Herley. C. Wavelet and filter banks:Theroy and design. IEEE Trans. Acoust. Speech signal Process, 1992.40(9). 2207-2232
[18] A. R. Calderbank, Ingrid Daubechies, Wim Sweldens, And Boon-Lock Yeo. Wavelet transforms that map integers to integers. Applied and Computational Harmonic Analysis, vol. 5, no. 3, pp. 332-369, July 1998.
[19] Adams and Kossentini. F, Reversible integer-to integer wavelet
tranforms for image compression:performance evaluation and analysis. IEEE Trans. On Image Processing. 2000.6 9(6):1010-1023
[20] A.R. Calderbank, Ingrid Daubechies, Wim Sweldens and Boon-Lock Yeo. Lossless image compression using integer to integer wavelet transforms, Conf. On Image Proc. IEEE Press, 1997
[21] A. Said, W.A. Pearlman, Image compression using the spatial-orientation tree. IEEE Int. Symp. Circuits and Systems, Chicago, IL,May 1993.
[22] 刘伟、龙琼、刘光斌，基于SPIHT算法的图像压缩方法，西安，现代电子技术，2001．11
[23] 王卫国，基于小波均值块的渐进图像编码算法的研究，[硕士学位论文]，西安电子科技大学，2002．1
[24] 马社祥、刘贵忠，曾召华，基于多尺度均值和小波变换的Internet图像可分级压缩编码传输技术，中国图像图形学报，Vol．5，No．11，P942-947，2000．11
[25] Ronald A. DeVore, Bjorn Jawerth, and Bradley J. Lucier. Image Compression through wavelet transform coding. IEEE Transactions on Information Theory, Vol. 38, No. 2,1992.3
[26] 陈青，尹亮，廖原，王延平，一种整数—整数小波变换，武汉大学学报，Vol．44．NO．3．1998．6
[27] 田金文，柳斌，柳健，基于整数小波变换的无失真图像压缩技术，电子学报，Vol．28，No．4，2000．4
[28] 赵瑞珍，小波理论及其在图像、信号处理中的算法研究，[博士学位论文]，西安电子科技大学，2001．6
[24] 马社祥、刘贵忠，曾召华，基于多尺度均值和小波变换的Internet图像可分级压缩编码传输技术，中国图像图形学报，Vol．5，No．11，P942-947，2000．11
[29] 王琪、钟玉琢，一种结合量化的零树小波图像编码器，清华大学学报，Vol．40， No．7．2000
[30] 沈兰荪等，视频编码与低速率传输，北京，电子工业出版社，2001．12。
[31] Efstratiadis. S.N. Hierarchical partition priority wavelet image compression. IEEE Trans Image Processing, 1996.5(1), P1111-1123.
[2] 李在铭，数字图像处理压缩与识别技术，成都，电子科技大学出版社，2000．11
[3] Kenneth. R. Castleman. Digital image processing. Prentice-Hall. inc. 1996
[4] J W Wood, S D O' Nell. Subband coding of images [J]. IEEE Trans Acoust Speech, and Signal Proc, 1986, 34 (5)。
[5] S Mallat. A theory for multiresolution decomposition:the wavelet representation [J]. IEEE Trans Pattn Anal Mach Intell, 1989, 11(7).
[6] Sweldens W. The lifting scheme: A custom design construction of biothogonal wavelets. Journal of Appl and Comput Harmonic Analysis. 1996.3 (2).
[7] I. Daubechies, W. Swelends. Factoring wavelet transforms into lifting steps. [J] Fourier Anal Appl, 1996, 4(3).
[8] Shapiro.J.M. Embedded image coding using zerotree of wavelet codfficients. IEEE Transaction on Signal Processing. Vol. 41, NO. 1211993]. P. 3445-3462
[9] A. Said, W. A. Pearlman. A new, fast, and efficient image coder based on set partitioning in hierarchical Trees. IEEE Transactions on Crcuits and Systems for Video Tchnology, Vol. 6, No. 3, 1996 (6). P243-250
[10] A. Islam and W. A. Pearlman. An embedded and efficient low-complexity hierarchical image coder. Visual Communications and Image Processing, Proceedings of SPIE. Vol. 3653, P 294-305, Jan. 1999
[11] [美]崔锦泰 著，程正兴 译，小波分析导论，西安，西安交通大学出版社，1995
[12] 刘贵忠、邸双亮，小波分析及其应用，西安，西安电子科技大学出版社，1992
[13] 李世雄，小波变换及其应用，北京，高等教育出版社，1997
[14] 陈武凡，小波分析及其在图续处理中的应用，北京，科学出版社，2002
[15] Cohen, A. Daubechies, Feauveau. J.C, Biorthogonal bases of compactly supported wavelet. Commun. Pure Appl. Math. 1992.5, 45(5):485-560
[16] Chui. C.K. Wavelets: A tatorial in theory and Applications. Academic Press. San Diego. CA. 1992
[17] Vetterli. M and Herley. C. Wavelet and filter banks:Theroy and design. IEEE Trans. Acoust. Speech signal Process, 1992.40(9). 2207-2232
[18] A. R. Calderbank, Ingrid Daubechies, Wim Sweldens, And Boon-Lock Yeo. Wavelet transforms that map integers to integers. Applied and Computational Harmonic Analysis, vol. 5, no. 3, pp. 332-369, July 1998.
[19] Adams and Kossentini. F, Reversible integer-to integer wavelet
tranforms for image compression:performance evaluation and analysis. IEEE Trans. On Image Processing. 2000.6 9(6):1010-1023
[20] A.R. Calderbank, Ingrid Daubechies, Wim Sweldens and Boon-Lock Yeo. Lossless image compression using integer to integer wavelet transforms, Conf. On Image Proc. IEEE Press, 1997
[21] A. Said, W.A. Pearlman, Image compression using the spatial-orientation tree. IEEE Int. Symp. Circuits and Systems, Chicago, IL,May 1993.
[22] 刘伟、龙琼、刘光斌，基于SPIHT算法的图像压缩方法，西安，现代电子技术，2001．11
[23] 王卫国，基于小波均值块的渐进图像编码算法的研究，[硕士学位论文]，西安电子科技大学，2002．1
[24] 马社祥、刘贵忠，曾召华，基于多尺度均值和小波变换的Internet图像可分级压缩编码传输技术，中国图像图形学报，Vol．5，No．11，P942-947，2000．11
[25] Ronald A. DeVore, Bjorn Jawerth, and Bradley J. Lucier. Image Compression through wavelet transform coding. IEEE Transactions on Information Theory, Vol. 38, No. 2,1992.3
[26] 陈青，尹亮，廖原，王延平，一种整数—整数小波变换，武汉大学学报，Vol．44．NO．3．1998．6
[27] 田金文，柳斌，柳健，基于整数小波变换的无失真图像压缩技术，电子学报，Vol．28，No．4，2000．4
[28] 赵瑞珍，小波理论及其在图像、信号处理中的算法研究，[博士学位论文]，西安电子科技大学，2001．6
[24] 马社祥、刘贵忠，曾召华，基于多尺度均值和小波变换的Internet图像可分级压缩编码传输技术，中国图像图形学报，Vol．5，No．11，P942-947，2000．11
[29] 王琪、钟玉琢，一种结合量化的零树小波图像编码器，清华大学学报，Vol．40， No．7．2000
[30] 沈兰荪等，视频编码与低速率传输，北京，电子工业出版社，2001．12。
[31] Efstratiadis. S.N. Hierarchical partition priority wavelet image compression. IEEE Trans Image Processing, 1996.5(1), P1111-1123.