基于小波的图像压缩编码
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摘要
伴随着数字通信、计算机网络及多媒体技术的飞速发展,图像压缩编码成为解决多媒体通信问题的一个关键环节和信息技术中最为活跃的领域之一,而基于小波变换的图像压缩是图像压缩领域的一个重要分支。在这种背景下对小波图像压缩算法进行研究和改进无疑是一项重要任务和研究热点。
论文主要内容由以下几部分构成:首先介绍了课题的背景、意义和研究现状以及静止图像压缩的分类和评价标准;然后,从小波变换的原理出发,通过实验,对变换后图像小波系数的特点进行分析,为后面介绍和改进小波压缩编码方法做准备;接下来,介绍了阈值压缩方法并根据硬阈值和软阈值压缩方法的优点和缺点提出了一种改进的阈值压缩方案;然后,对基于零树结构的图像压缩编码进行了重点介绍和研究,主要分析了嵌入式零树小波编码(Embeded Zerotree Wavelets Encoding,EZW)和基于多级树集合分割编码(Set Partitioning in Hierarchical Trees,SPIHT)两种算法,并对SPIHT算法提出了两种改进方案:方案一,在SPIHT编码前采用纹理子带补偿的预处理。这种方案能够提高恢复图像的主观质量。方案二,首先引入“兄弟节点相关性”假设对原算法中“父子节点相关性”假设作补充;其次,对最低频子带系数分开处理,即将LL_n子带系数和LH_n, HL_n , HH_n子带系数区别对待。并对SPIHT算法的零树结构进行相应的修改。方案二改进后的SPIHT算法能够更有效的标识显著节点,进而提高了图像的压缩性能。两种方案通过仿真实验证实都是可行和有效的。
With the rapid development of digital communications, computer networks and multimedia technologies, image compression become a key of solution to multimedia communications and one of the most active areas in information technology. Image compression based on wavelet transform is an important branch of image compression. Against this background to study and improve the algorithms of image compression based on wavelet is no only an important task but also a research hot.
The thesis is main divided into the following sections: first give a brief introduction about background, significance, study of current conditions and evaluation criterion of still image compression; and then, from the principle of wavelet transform through experiments give the characteristics of wavelet coefficients as a prepare to describe and improve the wavelet compression coding method; then introduced the wavelet image compression based on threshold. And according to the advantages and disadvantages of both hard -threshold and compression soft-threshold proposed an improved the compression scheme based on threshold. Then the research focused on the image compression based on zero-tree structure. After having a main analysis both EZW (Embeded Zerotree Wavelets Encoding for short) and SPIHT (Set Partitioning in Hierarchical Trees for short), we proposed two programs to improve SPIHT algorithm. Program I, have a pretreatment when code texture subband .Such program can improve the subjective quality of the restoration of images. Program II, Give the introduction of assumption“relationship of brothers nodes”to add the“relationship of father and son nodes”assumption; Have a different treatment to the coefficients of lowest resolution subbands and we modified the zerotree of SPIHT algorithm. Program II improve the ability of identify the significant coefficients node, so it improved the SPIHT algorithm’s performance of image compression. Simulation confirmed that both program I and program II are feasible and effective.
论文主要内容由以下几部分构成:首先介绍了课题的背景、意义和研究现状以及静止图像压缩的分类和评价标准;然后,从小波变换的原理出发,通过实验,对变换后图像小波系数的特点进行分析,为后面介绍和改进小波压缩编码方法做准备;接下来,介绍了阈值压缩方法并根据硬阈值和软阈值压缩方法的优点和缺点提出了一种改进的阈值压缩方案;然后,对基于零树结构的图像压缩编码进行了重点介绍和研究,主要分析了嵌入式零树小波编码(Embeded Zerotree Wavelets Encoding,EZW)和基于多级树集合分割编码(Set Partitioning in Hierarchical Trees,SPIHT)两种算法,并对SPIHT算法提出了两种改进方案:方案一,在SPIHT编码前采用纹理子带补偿的预处理。这种方案能够提高恢复图像的主观质量。方案二,首先引入“兄弟节点相关性”假设对原算法中“父子节点相关性”假设作补充;其次,对最低频子带系数分开处理,即将LL_n子带系数和LH_n, HL_n , HH_n子带系数区别对待。并对SPIHT算法的零树结构进行相应的修改。方案二改进后的SPIHT算法能够更有效的标识显著节点,进而提高了图像的压缩性能。两种方案通过仿真实验证实都是可行和有效的。
With the rapid development of digital communications, computer networks and multimedia technologies, image compression become a key of solution to multimedia communications and one of the most active areas in information technology. Image compression based on wavelet transform is an important branch of image compression. Against this background to study and improve the algorithms of image compression based on wavelet is no only an important task but also a research hot.
The thesis is main divided into the following sections: first give a brief introduction about background, significance, study of current conditions and evaluation criterion of still image compression; and then, from the principle of wavelet transform through experiments give the characteristics of wavelet coefficients as a prepare to describe and improve the wavelet compression coding method; then introduced the wavelet image compression based on threshold. And according to the advantages and disadvantages of both hard -threshold and compression soft-threshold proposed an improved the compression scheme based on threshold. Then the research focused on the image compression based on zero-tree structure. After having a main analysis both EZW (Embeded Zerotree Wavelets Encoding for short) and SPIHT (Set Partitioning in Hierarchical Trees for short), we proposed two programs to improve SPIHT algorithm. Program I, have a pretreatment when code texture subband .Such program can improve the subjective quality of the restoration of images. Program II, Give the introduction of assumption“relationship of brothers nodes”to add the“relationship of father and son nodes”assumption; Have a different treatment to the coefficients of lowest resolution subbands and we modified the zerotree of SPIHT algorithm. Program II improve the ability of identify the significant coefficients node, so it improved the SPIHT algorithm’s performance of image compression. Simulation confirmed that both program I and program II are feasible and effective.
引文
[1]沈兰荪,卓力,田栋等,视频编码与低频率传输,北京:电子工业出版社,2001
[2]林立宇,Contourlet变换--影像处理应用,北京:科学出版社,2008
[3]陈武凡,小波分析及其在图像处理中的应用,北京:科学出版社,2002
[4]程正兴,小波分析算法与应用,西安:西安交通大学出版社,1998
[5]张砺佳,基于小波变换的图像压缩编码研究:[硕士学位论文],西安:中科院西安光学精密机械研究所,2007.
[6] M. Unser. Approximation power of biorthogonal wavelet expansions. IEEE Trans. on Signal Processing. 1996.4(4):519-527.
[7]边威,小波基的选取与构造方法讨论:[硕士学位论文] ,长春:东北师范大学, 2007.
[8] J. Villasensoe. Wavelets filter evaluation for image compression. IEEE Trans.On image processing.1995.4 (8):1053-1060
[9] Olivier Egger. Weili. Subband coding of image using asymmetrical filter banks.IEEE Transom image processing.1995.4 (4):478-485
[10] W.A.Pearhnan, A. Islam, N.Nagaraj, and A.Said, "Efficient, low-complexity image coding with a set-partitioning embedded block coder", IEEE Trazas. on Circuits and Systems for Video Technology vo1.14, no.l 1, pp.1219-1235, Nov, 2004
[11] I. Daubechies. Ten lectures on wavelets. CBMS-NSF Lecture notes nr. 61.SIAM.1992
[12] O.Rioul. P. Duhamel. Fast algorithms for discrete and continuous wavelet transform. IEEE Trans. Information theory. 1992.38(2):569-585
[13] Sweldens W. The lifting scheme: a custom-design construction of biorthogonal wavelet. Appl. Comput. Harmon. Anal 1996.3(2):186-200
[14] Sweldens W. The lifting scheme: a construction of second generation wavelets [technical report].USA: university of south Carolina.1995
[15] Honeying Meng.Zhihua Wang. Fast spatial combinative lifting algorithm of wavelet transform using 9/7 filter for image block compression. Electronics letters.2000.36 (21)
[16] J. Reichel. M. Menegaz. Integer wavelet transforms for embedded lossy to lossless image compression .IEEE Trans. Image Processing .2001.10(3):383-392
[17] Shaprio J M. Embedded image coding using zerotree of wavelet coefficients.IEEE Trans .onsignal processing .1993.41(12):3445-3463
[18] A. Said .W. A. Pearlman. A new. Fast. And efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuit and systems for video technology. 1996.6(3): 243-250
[19] Gopinath R.A, Lang M, Guo H, Odegard J.E. Wavelet-based post-processing of low bit rate transform coded images. Image Processing,1994.Proceedings.ICIP-94.,IEEE International Conference,2:913-917
[20]宋好好,对改进SPECK和LZC图像压缩编码算法的研究:[硕士论文],济南:山东大学,2004
[21]康世华,基于小波和神经网络的图像编码算法研究: [硕士论文],兰州:兰州大学,2006
[22]邹玮刚,基于小波变换的快速分形图像编码压缩方法的研究: [硕士论文],南昌:南昌大学,2006
[23]刘元宁,虹膜及指纹图像基于整数小波变换的形态学零树压缩编码;吉林:吉林大学,2004
[24]沈兰荪,图像编码与异步传输,北京:人民邮电出版社,1998
[25]姜秀华,数字电视广播原理与应用,北京:人民邮电出版社,2007
[26]刘文耀,小波图像编码与专用VLSI设计,北京:电子工业出版社,2006.
[27] Barnsley M F.Sloan A D.A better way to conpress image. Magazine,1988,13(1):215-223
[28]丁艺芳,基于小波变换和视觉系统的图像质量综合评价新算法: [全国优秀博士论文],上海:上海大学,2001
[29]张砺佳,基于小波变换的图像压缩编码研究:[硕士学位论文],西安:中科院西安光学精密机械研究所,2007
[30]刘文耀,小波图像编码与专用VLSI设计,北京:电子工业出版社,2006.
[31]柯丽,基于小波变换的空间遥感图像实时压缩方法研究:[博士论文] ,长春:中国科学院长春光学精密机械与物理研究所,2005
[32] Mallat S G.Multifrequency Channel Decomposition of Images and Wavelet Models [J].IEEE Trans. On Acoustics, Speech and Signal Processing, 1989, 37 (12):20912110.
[33] Mallat S G.A Theory for Multiresolution Signal Decomposition: the Wavelet Representation [J].IEEE Trans. On Pattern Recognition and Machine Intelligence, 1989, 11(7): 674693.
[34]张砺佳.基于小波变换的图像压缩编码研究:[硕士学位论文],西安:中科院西安光学精密机械研究所,200,
[35]梁晓鸣,基于小波变换的图像压缩技术研究:[硕士学位论文],哈尔滨:哈尔滨理工大学2008.3
[36] Lewis,A.S.and Knowles, G. Image Compression Using the 2-D Wavelet Transform, IEEETrans.IP,vol. 1, no. 2, April 1992, pp. 244-250
[37]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(自然科学版) , 2000 , 7 (40)
[38]刘利章,基于小波变换的图像压缩编码研究:[博士学位论文],西安:西北工业大学,2006
[39] Shaorong Chang,Carin L. A modified SPIHT algorithm for image coding with a joint MSE and classification distortion measure Image Processing, IEEE Transactions onVolume 15, Issue , March 2006 :713– 725
[40]邱自华,一种改进的SPIHT图像编码方法计算机与数字工程,2007,35(4):121-125
[41]韩明臣,SPIHT嵌入式编码算法在图像压缩中的应用,电子科技2007,(7)
[42]解得钦,基于提升结构的小波图像压缩编码研究与设计:[硕士学位论文],吉林:吉林大学,2007
[43]张砺佳.基于小波变换的图像压缩编码研究:[硕士学位论文],西安:中科院西安光学精密机械研究所,2007.
[44]林立宇,Contourlet变换--影像处理应用,北京:科学出版社,2008
[45]刘文耀,小波图像编码与专用VLSI设计,北京:电子工业出版社,2006.
[46] Yushin Cho, Pearlman W.A. Hierarchical Dynamic Range Coding of Wavelet Subbands for Fast and Efficient Image Decompression ,Image Processing, IEEE Transactions on2007:2005– 2015
[47]柯丽,基于小波变换的空间遥感图像实时压缩方法研究:[博士论文] ,长春:中国科学院长春光学精密机械与物理研究所,2005
[48]梁晓鸣,基于小波变换的图像压缩技术研究:[硕士学位论文],哈尔滨:哈尔滨理工大学2008.3
[49]丁艺芳,基于小波变换和视觉系统的图像质量综合评价新算法:[全国优秀博士论文],上海:上海大学,2001
[50]李文娟,基于视觉特性的彩色图像质量评价研究:[硕士学位论文],吉林:吉林大学,2006
[51] KHAN E, GHANBARI M. Efficient SPIHT Based Embedded Color Image Coding [J].IEEE Electronics Letters 2001,(37): 951-952.
[52] Taubman D. "High performance scalable image compression with EBCOT" IEEE Trans. Image Processing, 2000, 9(7):1158-1170.
[53]马平,数字图像处理和压缩,北京;电子工业出版社,2007
[2]林立宇,Contourlet变换--影像处理应用,北京:科学出版社,2008
[3]陈武凡,小波分析及其在图像处理中的应用,北京:科学出版社,2002
[4]程正兴,小波分析算法与应用,西安:西安交通大学出版社,1998
[5]张砺佳,基于小波变换的图像压缩编码研究:[硕士学位论文],西安:中科院西安光学精密机械研究所,2007.
[6] M. Unser. Approximation power of biorthogonal wavelet expansions. IEEE Trans. on Signal Processing. 1996.4(4):519-527.
[7]边威,小波基的选取与构造方法讨论:[硕士学位论文] ,长春:东北师范大学, 2007.
[8] J. Villasensoe. Wavelets filter evaluation for image compression. IEEE Trans.On image processing.1995.4 (8):1053-1060
[9] Olivier Egger. Weili. Subband coding of image using asymmetrical filter banks.IEEE Transom image processing.1995.4 (4):478-485
[10] W.A.Pearhnan, A. Islam, N.Nagaraj, and A.Said, "Efficient, low-complexity image coding with a set-partitioning embedded block coder", IEEE Trazas. on Circuits and Systems for Video Technology vo1.14, no.l 1, pp.1219-1235, Nov, 2004
[11] I. Daubechies. Ten lectures on wavelets. CBMS-NSF Lecture notes nr. 61.SIAM.1992
[12] O.Rioul. P. Duhamel. Fast algorithms for discrete and continuous wavelet transform. IEEE Trans. Information theory. 1992.38(2):569-585
[13] Sweldens W. The lifting scheme: a custom-design construction of biorthogonal wavelet. Appl. Comput. Harmon. Anal 1996.3(2):186-200
[14] Sweldens W. The lifting scheme: a construction of second generation wavelets [technical report].USA: university of south Carolina.1995
[15] Honeying Meng.Zhihua Wang. Fast spatial combinative lifting algorithm of wavelet transform using 9/7 filter for image block compression. Electronics letters.2000.36 (21)
[16] J. Reichel. M. Menegaz. Integer wavelet transforms for embedded lossy to lossless image compression .IEEE Trans. Image Processing .2001.10(3):383-392
[17] Shaprio J M. Embedded image coding using zerotree of wavelet coefficients.IEEE Trans .onsignal processing .1993.41(12):3445-3463
[18] A. Said .W. A. Pearlman. A new. Fast. And efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuit and systems for video technology. 1996.6(3): 243-250
[19] Gopinath R.A, Lang M, Guo H, Odegard J.E. Wavelet-based post-processing of low bit rate transform coded images. Image Processing,1994.Proceedings.ICIP-94.,IEEE International Conference,2:913-917
[20]宋好好,对改进SPECK和LZC图像压缩编码算法的研究:[硕士论文],济南:山东大学,2004
[21]康世华,基于小波和神经网络的图像编码算法研究: [硕士论文],兰州:兰州大学,2006
[22]邹玮刚,基于小波变换的快速分形图像编码压缩方法的研究: [硕士论文],南昌:南昌大学,2006
[23]刘元宁,虹膜及指纹图像基于整数小波变换的形态学零树压缩编码;吉林:吉林大学,2004
[24]沈兰荪,图像编码与异步传输,北京:人民邮电出版社,1998
[25]姜秀华,数字电视广播原理与应用,北京:人民邮电出版社,2007
[26]刘文耀,小波图像编码与专用VLSI设计,北京:电子工业出版社,2006.
[27] Barnsley M F.Sloan A D.A better way to conpress image. Magazine,1988,13(1):215-223
[28]丁艺芳,基于小波变换和视觉系统的图像质量综合评价新算法: [全国优秀博士论文],上海:上海大学,2001
[29]张砺佳,基于小波变换的图像压缩编码研究:[硕士学位论文],西安:中科院西安光学精密机械研究所,2007
[30]刘文耀,小波图像编码与专用VLSI设计,北京:电子工业出版社,2006.
[31]柯丽,基于小波变换的空间遥感图像实时压缩方法研究:[博士论文] ,长春:中国科学院长春光学精密机械与物理研究所,2005
[32] Mallat S G.Multifrequency Channel Decomposition of Images and Wavelet Models [J].IEEE Trans. On Acoustics, Speech and Signal Processing, 1989, 37 (12):20912110.
[33] Mallat S G.A Theory for Multiresolution Signal Decomposition: the Wavelet Representation [J].IEEE Trans. On Pattern Recognition and Machine Intelligence, 1989, 11(7): 674693.
[34]张砺佳.基于小波变换的图像压缩编码研究:[硕士学位论文],西安:中科院西安光学精密机械研究所,200,
[35]梁晓鸣,基于小波变换的图像压缩技术研究:[硕士学位论文],哈尔滨:哈尔滨理工大学2008.3
[36] Lewis,A.S.and Knowles, G. Image Compression Using the 2-D Wavelet Transform, IEEETrans.IP,vol. 1, no. 2, April 1992, pp. 244-250
[37]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(自然科学版) , 2000 , 7 (40)
[38]刘利章,基于小波变换的图像压缩编码研究:[博士学位论文],西安:西北工业大学,2006
[39] Shaorong Chang,Carin L. A modified SPIHT algorithm for image coding with a joint MSE and classification distortion measure Image Processing, IEEE Transactions onVolume 15, Issue , March 2006 :713– 725
[40]邱自华,一种改进的SPIHT图像编码方法计算机与数字工程,2007,35(4):121-125
[41]韩明臣,SPIHT嵌入式编码算法在图像压缩中的应用,电子科技2007,(7)
[42]解得钦,基于提升结构的小波图像压缩编码研究与设计:[硕士学位论文],吉林:吉林大学,2007
[43]张砺佳.基于小波变换的图像压缩编码研究:[硕士学位论文],西安:中科院西安光学精密机械研究所,2007.
[44]林立宇,Contourlet变换--影像处理应用,北京:科学出版社,2008
[45]刘文耀,小波图像编码与专用VLSI设计,北京:电子工业出版社,2006.
[46] Yushin Cho, Pearlman W.A. Hierarchical Dynamic Range Coding of Wavelet Subbands for Fast and Efficient Image Decompression ,Image Processing, IEEE Transactions on2007:2005– 2015
[47]柯丽,基于小波变换的空间遥感图像实时压缩方法研究:[博士论文] ,长春:中国科学院长春光学精密机械与物理研究所,2005
[48]梁晓鸣,基于小波变换的图像压缩技术研究:[硕士学位论文],哈尔滨:哈尔滨理工大学2008.3
[49]丁艺芳,基于小波变换和视觉系统的图像质量综合评价新算法:[全国优秀博士论文],上海:上海大学,2001
[50]李文娟,基于视觉特性的彩色图像质量评价研究:[硕士学位论文],吉林:吉林大学,2006
[51] KHAN E, GHANBARI M. Efficient SPIHT Based Embedded Color Image Coding [J].IEEE Electronics Letters 2001,(37): 951-952.
[52] Taubman D. "High performance scalable image compression with EBCOT" IEEE Trans. Image Processing, 2000, 9(7):1158-1170.
[53]马平,数字图像处理和压缩,北京;电子工业出版社,2007