基于小波变换的嵌入式零树图像压缩编码研究
|
摘要
数字图像压缩编码技术是各种数字图像应用的关键技术。在过去几十年之中,数字图像压缩编码技术取得了很大进步,由于小波变换在时频域的良好性能,小波变换编码成为当前编码的新的和重要的发展方向。随着数字图像压缩编码技术的广泛应用,人们对于压缩系统的要求越来越高,实时性作为一个重要的指标,正受到越来越多的关注。
小波变换具有良好的局部特性和空间—频率特性,并且具有描述非平稳图像信号的能力和适应人眼视觉特性的良好性能,故在较高压缩比的图像编码领域中得到广泛研究和应用。基于小波变换的算法是国际标准JPEG 2000的核心技术之一。
嵌入式零树小波编码(EZW)算法是小波图像编码研究的一个里程碑,对其研究具有较高的理论意义和实用价值。本文阐述了小波变换和图像编码的基础理论,简单介绍了多分辨率分析技术和Mallat算法,详细分析了小波变换的嵌入式零树图像编码算法的原理;在此基础上提出了一种改进的嵌入式小波零树图像编码方案,并通过仿真实验对比改进算法与原EZW算法,无论是在客观数据还是主观视觉上都有所改善,从而验证了该算法的有效性。
Digital image compression is the key technology of different kinds of applications in digital image processing. In the past years, great processes have been made in digital image compression area. Digital image compression based on wavelet transform is becoming one of the most important trends of development in this field because of wavelet transform’s excellent performance both in time and frequency domain. With the development of technology, more and more algorithms emerge and their capability of real-time processing is attracting more focus than ever before.
The wavelet transform have a better partial and space-frequency characteristics,an ability of describing the non-stationary image signal and a good function of adapting to the eye sense of visual characteristic. So it is extensively researched and applied in higher compression ratio of image coding field. The core of International Standard JPEG 2000 is based on the wavelet transform.
EZW algorithm is the landmark in the research of wavelet image coding. It has been researched that EZW needs further improvement. In the thesis, after having introduced the theories about image coding and wavelet transform, then, the multiresolution analysis technology and the Mallat algorithm are introduced. And then the embedded wavelet zero-tree image coding (EZW) algorithm is discussed and analysed in detail. Based on the clear analysis, an improved algorithm is proposed in this thesis.The original and improved EZW algorithms simulated and their performances were compared. The improvement was proved to be efficient through the comparison between the data and the vision.
小波变换具有良好的局部特性和空间—频率特性,并且具有描述非平稳图像信号的能力和适应人眼视觉特性的良好性能,故在较高压缩比的图像编码领域中得到广泛研究和应用。基于小波变换的算法是国际标准JPEG 2000的核心技术之一。
嵌入式零树小波编码(EZW)算法是小波图像编码研究的一个里程碑,对其研究具有较高的理论意义和实用价值。本文阐述了小波变换和图像编码的基础理论,简单介绍了多分辨率分析技术和Mallat算法,详细分析了小波变换的嵌入式零树图像编码算法的原理;在此基础上提出了一种改进的嵌入式小波零树图像编码方案,并通过仿真实验对比改进算法与原EZW算法,无论是在客观数据还是主观视觉上都有所改善,从而验证了该算法的有效性。
Digital image compression is the key technology of different kinds of applications in digital image processing. In the past years, great processes have been made in digital image compression area. Digital image compression based on wavelet transform is becoming one of the most important trends of development in this field because of wavelet transform’s excellent performance both in time and frequency domain. With the development of technology, more and more algorithms emerge and their capability of real-time processing is attracting more focus than ever before.
The wavelet transform have a better partial and space-frequency characteristics,an ability of describing the non-stationary image signal and a good function of adapting to the eye sense of visual characteristic. So it is extensively researched and applied in higher compression ratio of image coding field. The core of International Standard JPEG 2000 is based on the wavelet transform.
EZW algorithm is the landmark in the research of wavelet image coding. It has been researched that EZW needs further improvement. In the thesis, after having introduced the theories about image coding and wavelet transform, then, the multiresolution analysis technology and the Mallat algorithm are introduced. And then the embedded wavelet zero-tree image coding (EZW) algorithm is discussed and analysed in detail. Based on the clear analysis, an improved algorithm is proposed in this thesis.The original and improved EZW algorithms simulated and their performances were compared. The improvement was proved to be efficient through the comparison between the data and the vision.
引文
[1] 梁瑛.JPEG2000 静止图像核心压缩算法.北京广播学院学报,2003,10(3):76-82
[2] 李建平,张万萍,陈廷槐. 小波分析的一些有前景的应用领域. 重庆大学学报, 1999.22(1):121~125
[3] Official JPEG2000 Homepage: http://wwwjpeg.org/jpeg2000/index.html
[4] (日)小野定康,铃木纯司. JPEG2000 技术. 科学出版社,2004
[5] (美)David S.Taubman,Michael WMarcellin.JPEG2000 图像压缩基础、标准和实践。电子工业出版社,2004
[6] (日)田村秀行计算机图像处理.北京:科学出版社,?004
[7] 姚敏等. 数字图像处理. 机械工业出版社, 2006. 137~224
[8] 沈兰荪. 图像编码与异步传输. 人民邮电出版社, 2001.13~121
[9] 崔屹. 数字图像处理技术与应用.北京:电子工业出版社,1997
[10] 孙延奎. 小波分析及其应用. 机械工业出版社, 2005,3. 89~152
[11] 张旭东,卢国栋等. 图像编码基础和小波压缩技术-原理、算法和标准, 清华大学出版社, 2004,3. 165~210,235~312
[12] 成礼智,王红霞等. 小波的理论与应用科学出版社, 2004,9. 75~94
[13] (法)Stephane Mallat 著,杨力华等译. 信号处理的小波导引. 机械出版社, 2002
[14] 姜丹. 信息论与编码. 中国科学技术大学出版社,2004.204560
[15] Wallace G K.The JPEG:still picture compression standard. Communi. Of the ACM,1991, 34(4):3044
[16] 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 上海大学出版社
[17] 刘海林.静止图像压缩研究一 JPEG2000 标准的分析与应用,成都理工大学,2002
[18] 孙廷奎.小波分析及其应用.北京:机械工业出版社,2005
[19] 程正兴.小波分析算法与应用.西安:西安交通大学出版社,1998
[20] 徐长法,李国宽. 实用小波方法(第二版). 华中科技大学出版社,2004. 43~184
[21] 李弼程,罗建书.小波分析及其应用.北京:电子工业出版社,2003
[22] 李建平,唐远炎.小波分析方法的应用.重庆:重庆大学出版社,1999
[23] Mallat S. Atheory for multiresolution signal decompositionathe wavelet representation. IEEE Trans. On PAMI, 1989,11(7):674~693
[24] 于景侠.基于小波变换的静止图像编解码算法研究与实现.成都电子科技大学,2003
[25] (美)Jaideva C. Goswami Andrew K. Chan 小波分析 — 理论、算法及其应用. 国防工业出版社. 2007
[26] 康静.多小波及其在图像压缩中的应用. 西北大学,2004
[27] Mallat S.G. A theory for multiresolution signal decomposition: the wavelet representation [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, July 1989,11(7): 674-693.
[28] 崔锦泰著,程正兴译.小波分析导论.西安交通大学出版社,1995.77
[29] 杨福生.小波变换的工程分析与应用.科学出版社,1999
[30] 彭玉华.小波变换与工程应用.科学出版社,2000
[31] 李建平,唐远炎.小波分析方法的应用.重庆:重庆大学出版社,1999
[32] 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):742745
[33] 王青海,莫玉龙. JPEG2000 压缩标准中小波基的选择. 计算机工程,2004,30 (23):138140
[34] Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. On Image Processing, 1992,1(2):244~250
[35] 周志成. 基于小波零树的图像压缩算法的研究与改进:[学位论文]. 2003, 6
[36] 飞思科技产品研发中心. 小波分析理论与 MATLAB7.0 实现. 电子工业出版社,2005,3:271361
[37] 胡昌华.张军波.夏军.张伟. 基于 MATLAB 的系统分析与设计—小波分析. 西安电子科技大学出版社. 1999
[38] 郑治真.沈萍.杨选辉.万玉莉. 小波变换及其 MATLAB 工具的应用. 地震出版社 2001
[39] Karlsson G Extension of finite length sigals for subband coding. Signal Processing. 1989, (17):161~168
[40] 毛立强. 嵌入式零树小波编码方法研究微机发展, 2004,14(7):109111
[41] Andrew JP,Ogunbora PO,Paoloni FJ. Coding Gain and Spatial Localisation Properties of Discrete Wavelet Transform Filters for Image coding. IEEE Proc-vin, Image Signal process. 1995,142(3):133-140
[42] Xu Mengxia. The Fundamental of Image Coding. Beijing: People's Posts& Telecommunications Publishing House, 1984
[43] S.Arivazhagan,D.Gnanadurai,J.R.Antony Vance,K.M.Sarojini,L.Ganesan. Evaluation of Zero Tree Wavelet Coders. IEEE Computers and Communications. 2003
[44] Witten I H, Neal R M, Cleary J G Arthmtic coding for data compression. Communication of the ACM, 1987, 30(6):520540
[45] ISO/IEC 15444-1: Information technology-JPEG2000 image coding system-Partl:Core coding system, 2000
[46] 乔世杰. 小波图象编码中的对称边界延拓法. 中国图象图形学报, 2000,5(9):725729
[47] 付文秀,都来浩等. EZW 图像编码算法的改进与仿真实现.吉林大学学报,2004,22 (2):9397
[48] 王向阳,杨红颖. 基于多阈值与嵌入零树小波的图像压缩算法. 通信学报, 2001,22 (12):8893
[49] 王祥林,吴国威等. 一种基于零树的多码率小波图象编码方法. 电子学报, 1997,25 (4):4851
[50] 黄佳庆,翁默颖. 一种基于子波变换改进型零树量化编码算法. 华东师范大学学报, 1999,9:4751
[51] Amir Saidetai1 A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees. IEEE Trans on Circuits & Systems for Video Technology. 1996,1,6 (6)
[52] Habibollah Danyali,Alfred Mertins. Highly scalable image compression based on SPIHT for network applications. IEEE ICIP. 2002,6
[53] 王家文,王浩等. MATLAB7.0 编程基础. 机械工业出版社,2005,7
[2] 李建平,张万萍,陈廷槐. 小波分析的一些有前景的应用领域. 重庆大学学报, 1999.22(1):121~125
[3] Official JPEG2000 Homepage: http://wwwjpeg.org/jpeg2000/index.html
[4] (日)小野定康,铃木纯司. JPEG2000 技术. 科学出版社,2004
[5] (美)David S.Taubman,Michael WMarcellin.JPEG2000 图像压缩基础、标准和实践。电子工业出版社,2004
[6] (日)田村秀行计算机图像处理.北京:科学出版社,?004
[7] 姚敏等. 数字图像处理. 机械工业出版社, 2006. 137~224
[8] 沈兰荪. 图像编码与异步传输. 人民邮电出版社, 2001.13~121
[9] 崔屹. 数字图像处理技术与应用.北京:电子工业出版社,1997
[10] 孙延奎. 小波分析及其应用. 机械工业出版社, 2005,3. 89~152
[11] 张旭东,卢国栋等. 图像编码基础和小波压缩技术-原理、算法和标准, 清华大学出版社, 2004,3. 165~210,235~312
[12] 成礼智,王红霞等. 小波的理论与应用科学出版社, 2004,9. 75~94
[13] (法)Stephane Mallat 著,杨力华等译. 信号处理的小波导引. 机械出版社, 2002
[14] 姜丹. 信息论与编码. 中国科学技术大学出版社,2004.204560
[15] Wallace G K.The JPEG:still picture compression standard. Communi. Of the ACM,1991, 34(4):3044
[16] 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 上海大学出版社
[17] 刘海林.静止图像压缩研究一 JPEG2000 标准的分析与应用,成都理工大学,2002
[18] 孙廷奎.小波分析及其应用.北京:机械工业出版社,2005
[19] 程正兴.小波分析算法与应用.西安:西安交通大学出版社,1998
[20] 徐长法,李国宽. 实用小波方法(第二版). 华中科技大学出版社,2004. 43~184
[21] 李弼程,罗建书.小波分析及其应用.北京:电子工业出版社,2003
[22] 李建平,唐远炎.小波分析方法的应用.重庆:重庆大学出版社,1999
[23] Mallat S. Atheory for multiresolution signal decompositionathe wavelet representation. IEEE Trans. On PAMI, 1989,11(7):674~693
[24] 于景侠.基于小波变换的静止图像编解码算法研究与实现.成都电子科技大学,2003
[25] (美)Jaideva C. Goswami Andrew K. Chan 小波分析 — 理论、算法及其应用. 国防工业出版社. 2007
[26] 康静.多小波及其在图像压缩中的应用. 西北大学,2004
[27] Mallat S.G. A theory for multiresolution signal decomposition: the wavelet representation [J]. IEEE Trans. Pattern Analysis and Machine Intelligence, July 1989,11(7): 674-693.
[28] 崔锦泰著,程正兴译.小波分析导论.西安交通大学出版社,1995.77
[29] 杨福生.小波变换的工程分析与应用.科学出版社,1999
[30] 彭玉华.小波变换与工程应用.科学出版社,2000
[31] 李建平,唐远炎.小波分析方法的应用.重庆:重庆大学出版社,1999
[32] 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):742745
[33] 王青海,莫玉龙. JPEG2000 压缩标准中小波基的选择. 计算机工程,2004,30 (23):138140
[34] Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. On Image Processing, 1992,1(2):244~250
[35] 周志成. 基于小波零树的图像压缩算法的研究与改进:[学位论文]. 2003, 6
[36] 飞思科技产品研发中心. 小波分析理论与 MATLAB7.0 实现. 电子工业出版社,2005,3:271361
[37] 胡昌华.张军波.夏军.张伟. 基于 MATLAB 的系统分析与设计—小波分析. 西安电子科技大学出版社. 1999
[38] 郑治真.沈萍.杨选辉.万玉莉. 小波变换及其 MATLAB 工具的应用. 地震出版社 2001
[39] Karlsson G Extension of finite length sigals for subband coding. Signal Processing. 1989, (17):161~168
[40] 毛立强. 嵌入式零树小波编码方法研究微机发展, 2004,14(7):109111
[41] Andrew JP,Ogunbora PO,Paoloni FJ. Coding Gain and Spatial Localisation Properties of Discrete Wavelet Transform Filters for Image coding. IEEE Proc-vin, Image Signal process. 1995,142(3):133-140
[42] Xu Mengxia. The Fundamental of Image Coding. Beijing: People's Posts& Telecommunications Publishing House, 1984
[43] S.Arivazhagan,D.Gnanadurai,J.R.Antony Vance,K.M.Sarojini,L.Ganesan. Evaluation of Zero Tree Wavelet Coders. IEEE Computers and Communications. 2003
[44] Witten I H, Neal R M, Cleary J G Arthmtic coding for data compression. Communication of the ACM, 1987, 30(6):520540
[45] ISO/IEC 15444-1: Information technology-JPEG2000 image coding system-Partl:Core coding system, 2000
[46] 乔世杰. 小波图象编码中的对称边界延拓法. 中国图象图形学报, 2000,5(9):725729
[47] 付文秀,都来浩等. EZW 图像编码算法的改进与仿真实现.吉林大学学报,2004,22 (2):9397
[48] 王向阳,杨红颖. 基于多阈值与嵌入零树小波的图像压缩算法. 通信学报, 2001,22 (12):8893
[49] 王祥林,吴国威等. 一种基于零树的多码率小波图象编码方法. 电子学报, 1997,25 (4):4851
[50] 黄佳庆,翁默颖. 一种基于子波变换改进型零树量化编码算法. 华东师范大学学报, 1999,9:4751
[51] Amir Saidetai1 A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees. IEEE Trans on Circuits & Systems for Video Technology. 1996,1,6 (6)
[52] Habibollah Danyali,Alfred Mertins. Highly scalable image compression based on SPIHT for network applications. IEEE ICIP. 2002,6
[53] 王家文,王浩等. MATLAB7.0 编程基础. 机械工业出版社,2005,7