小波域图像与视频压缩算法及应用研究
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摘要
多媒体技术的发展，促进了数字图像在互联网、数字电视、可视电话、网络电影、远程监测、医疗影像、家用电器、公共安全等领域的广泛应用。图像的大数据量与有限容量的存储媒体和传输带宽的矛盾日益突出，图像压缩技术的研究成为科研人员的一项艰巨任务。随着二十年来在数学等领域诸多理论成果的取得，图像压缩技术取得了许多重大的突破，以传统的无损压缩和有损压缩方法为基础，推出了基于小波变换、分形方法、神经网络等新的图像压缩方法，在压缩比和图像质量上有了很大的提高。新的压缩方法也带来了计算复杂性问题，为实时应用带来了困难，算法的实时性研究和硬件实现也成为图像压缩的一个重要研究方向。
以ISO／IEC的JPEG(联合摄影专家组)和MPEG(活动图像编码专家组)及ITU-T的VCEG(视频编码专家组)为主，总结了全球范围内最新、最有效的研究成果，针对一些具体应用颁布了多种图像压缩国际标准，推荐了标准算法和数据格式，同时也为图像压缩领域的科技人员改进和研究新算法指名了方向。
本论文对图像压缩的一般方法进行了综述，分析了最新的国际标准，尤其对基于小波变换的图像压缩方法进行了研究，讨论了目前典型的小波域图像压缩方法，提出了几种小波变换的视频图像压缩方法并给出了仿真结果。论文还对分形和神经网络图像压缩方法进行了探讨。最后介绍了基于ADV611的实时视频图像压缩板的设计与实现方法。
第一章绪论对图像压缩的一般方法和发展方向进行了综述。
第二章分析讨论了MPEG-4、H.264和JPEG2000图像压缩国际标准。
第三章介绍了小波分析一般方法和其最新发展，包括多分辨率分析与Mallat算法、双正交小波、小波包和多小波分析方法，还探讨了提升小波——第二代小波的原理和实现方法。第四章讨论了几种国际上最具影响力的基于小波变换的图像压缩方法，包括嵌入式零树小波编码(EZW)图像压缩算法、多级树集合分裂算法(SPIHT)和优化截取的嵌入式块编码方法(EBCOT)。
第五章对小波域矢量量化方法进行了分析，对LBG和SOFM矢量分类搜索算法进行了对比，在此基础上探讨了一种新的小波域矢量量化图像压缩方法，在较小影响编码效果的前提下大大加快了算法速度；本章还探讨了一种基于运动补偿的视频图像压缩新方法，重点描述了图像块运动矢量的快速搜索算法；最后提出了一种新的小波域矢量编码的视频压缩方法，首次将运动补偿和矢量分类相结合，仿真结果表明具有很快的速度和良好的压缩效果。
第六章探讨了分形图像压缩编码原理，分析了简库恩全自动分形图像压缩编
重庆大学博士学位论文
码方法，并介绍了一种小波域分形图像压缩编码方法。
第七章介绍了基于ADV6n的实时视频图像压缩板设计方法。将ADV6n的
静态图像压缩与活动图像的帧相关技术相结合，采用DSP+FPGA的框架结构，实
现了视频图像的实时压缩。
第八章对论文工作进行了总结，对图像压缩技术发展和自己今后的努力方向
进行了展望。
With the development of multimedia technology, digital image is widely used in the fields of Internet, digital TV, visual telephone, network film, remote surveillance, medical photograph, household appliances, public security and so on. The contradiction is more conspicuous day by day between large number of data and the limited capacity of storage and band width. It is a hard task for the science and technology worker to do the research of image compression. Many significant achievements have been acquired as more and more theoretical results are obtained in the two decades. Based on the classics lossy and lossless image compression methods, various new algorithms are proposed to be applied to image compression, such as wavelet transform, fractal technology, artificial neural network(ANN). Higher compression ratio and better visual effect of image are achieved. Nevertheless, it is arduous for the new algorithms are applied to the real time fields on account of their complicated computation procedure. It is
urgent to accelerate the computation speed of image compression.
The standard organizations of JPEG(Joint Photographic Experts Group), MPEG(Moving Picture Experts Group) and VCEG(Video Coding Experts Group) have promulgated several international standards in accordance with various applications, in which the new efficient research achievements are adopted. The standards have recommended data styles and algorithms, meanwhile the endeavor direction of research workers is showed clearly.
The general way of image compression is described in this thesis. We have analyzed the recent international standards, especially the image compression methods based on wavelet transform. We have discussed three typical algorithms. Several new video compression themes based on wavelet transform are proposed and the simulation effects were presented in the thesis. Even more we have probed into the subjects of fractal theory and ANN technology. At last we have introduced the design approach of the real time video compression board based on the ADV611 chip.
The general way of image compression is described in the preface of chapter 1.
We discussed the image compression international standards of MPEG-4 H.264 and JPEG2000 in chapter 2.
In chapter 3, we introduced the theory of wavelet analysis and its recent progress, include multiresolution analysis, Mallat algorithms, biorthogonal wavelet, wavelet
packet, multi-wavelet analysis. The lifting scheme was discussed too. We analyzed "Embedded Zerotree Wavelet Encoding", "A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees" and "Embedded Block Coding with Optimized Truncation of the embedded bit streams " algorithms in chapter 4.
The vector quantization of wavelet coefficients is analyzed in chapter 5. The LBG algorithm is compared with SOFM vector classified searching method. A new vector classified searching method is proposed in this chapter that can accelerate searching speed with less influence to the visual quality. We presented a new video compression method based on MC (motion compensation) mainly the fast searching method of the motion vectors is described. In the end of this chapter we presented a new video compression algorithm of vector coding based on wavelet transform where the MC is combined with vector searching. Simulation shows high compression rate is achieved with less time-consumption.
In chapter 6, We probed into the principle of fractal theory. The fractal image compression algorithm of Jacqain AE is analyzed and a new fractal image compression algorithm based on wavelet transform is presented.
In chapter 7, we introduced the design approach of the real time video compression board based on the ADV611 chip. The static image compression technique of AD V611 being conjoined with the interrelationship between neighboring frames of video pictures, the DSP+FPGA hardware structure being adopted, the real time compression of video comes into reality.
We made a summary of this thesis in the last chapter, and then the endeavor d
以ISO／IEC的JPEG(联合摄影专家组)和MPEG(活动图像编码专家组)及ITU-T的VCEG(视频编码专家组)为主，总结了全球范围内最新、最有效的研究成果，针对一些具体应用颁布了多种图像压缩国际标准，推荐了标准算法和数据格式，同时也为图像压缩领域的科技人员改进和研究新算法指名了方向。
本论文对图像压缩的一般方法进行了综述，分析了最新的国际标准，尤其对基于小波变换的图像压缩方法进行了研究，讨论了目前典型的小波域图像压缩方法，提出了几种小波变换的视频图像压缩方法并给出了仿真结果。论文还对分形和神经网络图像压缩方法进行了探讨。最后介绍了基于ADV611的实时视频图像压缩板的设计与实现方法。
第一章绪论对图像压缩的一般方法和发展方向进行了综述。
第二章分析讨论了MPEG-4、H.264和JPEG2000图像压缩国际标准。
第三章介绍了小波分析一般方法和其最新发展，包括多分辨率分析与Mallat算法、双正交小波、小波包和多小波分析方法，还探讨了提升小波——第二代小波的原理和实现方法。第四章讨论了几种国际上最具影响力的基于小波变换的图像压缩方法，包括嵌入式零树小波编码(EZW)图像压缩算法、多级树集合分裂算法(SPIHT)和优化截取的嵌入式块编码方法(EBCOT)。
第五章对小波域矢量量化方法进行了分析，对LBG和SOFM矢量分类搜索算法进行了对比，在此基础上探讨了一种新的小波域矢量量化图像压缩方法，在较小影响编码效果的前提下大大加快了算法速度；本章还探讨了一种基于运动补偿的视频图像压缩新方法，重点描述了图像块运动矢量的快速搜索算法；最后提出了一种新的小波域矢量编码的视频压缩方法，首次将运动补偿和矢量分类相结合，仿真结果表明具有很快的速度和良好的压缩效果。
第六章探讨了分形图像压缩编码原理，分析了简库恩全自动分形图像压缩编
重庆大学博士学位论文
码方法，并介绍了一种小波域分形图像压缩编码方法。
第七章介绍了基于ADV6n的实时视频图像压缩板设计方法。将ADV6n的
静态图像压缩与活动图像的帧相关技术相结合，采用DSP+FPGA的框架结构，实
现了视频图像的实时压缩。
第八章对论文工作进行了总结，对图像压缩技术发展和自己今后的努力方向
进行了展望。
With the development of multimedia technology, digital image is widely used in the fields of Internet, digital TV, visual telephone, network film, remote surveillance, medical photograph, household appliances, public security and so on. The contradiction is more conspicuous day by day between large number of data and the limited capacity of storage and band width. It is a hard task for the science and technology worker to do the research of image compression. Many significant achievements have been acquired as more and more theoretical results are obtained in the two decades. Based on the classics lossy and lossless image compression methods, various new algorithms are proposed to be applied to image compression, such as wavelet transform, fractal technology, artificial neural network(ANN). Higher compression ratio and better visual effect of image are achieved. Nevertheless, it is arduous for the new algorithms are applied to the real time fields on account of their complicated computation procedure. It is
urgent to accelerate the computation speed of image compression.
The standard organizations of JPEG(Joint Photographic Experts Group), MPEG(Moving Picture Experts Group) and VCEG(Video Coding Experts Group) have promulgated several international standards in accordance with various applications, in which the new efficient research achievements are adopted. The standards have recommended data styles and algorithms, meanwhile the endeavor direction of research workers is showed clearly.
The general way of image compression is described in this thesis. We have analyzed the recent international standards, especially the image compression methods based on wavelet transform. We have discussed three typical algorithms. Several new video compression themes based on wavelet transform are proposed and the simulation effects were presented in the thesis. Even more we have probed into the subjects of fractal theory and ANN technology. At last we have introduced the design approach of the real time video compression board based on the ADV611 chip.
The general way of image compression is described in the preface of chapter 1.
We discussed the image compression international standards of MPEG-4 H.264 and JPEG2000 in chapter 2.
In chapter 3, we introduced the theory of wavelet analysis and its recent progress, include multiresolution analysis, Mallat algorithms, biorthogonal wavelet, wavelet
packet, multi-wavelet analysis. The lifting scheme was discussed too. We analyzed "Embedded Zerotree Wavelet Encoding", "A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees" and "Embedded Block Coding with Optimized Truncation of the embedded bit streams " algorithms in chapter 4.
The vector quantization of wavelet coefficients is analyzed in chapter 5. The LBG algorithm is compared with SOFM vector classified searching method. A new vector classified searching method is proposed in this chapter that can accelerate searching speed with less influence to the visual quality. We presented a new video compression method based on MC (motion compensation) mainly the fast searching method of the motion vectors is described. In the end of this chapter we presented a new video compression algorithm of vector coding based on wavelet transform where the MC is combined with vector searching. Simulation shows high compression rate is achieved with less time-consumption.
In chapter 6, We probed into the principle of fractal theory. The fractal image compression algorithm of Jacqain AE is analyzed and a new fractal image compression algorithm based on wavelet transform is presented.
In chapter 7, we introduced the design approach of the real time video compression board based on the ADV611 chip. The static image compression technique of AD V611 being conjoined with the interrelationship between neighboring frames of video pictures, the DSP+FPGA hardware structure being adopted, the real time compression of video comes into reality.
We made a summary of this thesis in the last chapter, and then the endeavor d
引文
[1] D.Taubman, "High Performance Scalable Image Compression With EBCOT", Proc. IEEE Int. Conference Image Processing, Vol. Ⅲ, pp. 343-348, Kobe, Japan, October 1999.
[2] D.Taubman, "High Performance Scalable Image Compression With EBCOT", IEEE Trans. Image Processing, Vol.9, No.7, pp. 1158-1170, July 2000.
[3] ISO/IEC JTC1/SC29/WG1 N505 Call for contributions for JPEG 2000(JTC 1.29.14 15444) Image Coding System March 1997.
[4] ISO/IEC JTC1/SC29/WG1 N390R New work item: JPEG2000 image coding system, March 1997.
[5] Fisher Y. Fractal image compression: theory and application[M]. New York: Spring-Verlag, 1995.
[6] Davoine F, Bertin E, Chassery A. An adaptive partition for fractal image coding[J]. Fractals. 1997, 5: 243-256.
[7] Huaqiu Deng, Wenhua Weng, Li Li, Yingling Yu. An efficient fractal image compression method[A]. 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 1997, 5. 4204-4206.
[8] Wu X, Yao C. Image coding by adaptive tree-structured segmentation[M]. IEEE Computer Society Press, Los Alamitos, CA, 1991, 73-82.
[9] 秦烽，吴征，周峰，姚庆栋．分区IFS图像压缩编码[J]．通信学报，1997，18(5)：1-7．
[10] Davoine F, Svensson J, Chassery I M. A mixed triangular and quadrilateral partition for fractal image coding[A]. Proceedings ICIP-95 (IEEE International Conference on Image Processing)[C], Washington DC, USA, October, 1995, Ⅲ. 284-287.
[11] 王学军，陈贺新．基于方差不变特性的快速分形图像编码方法[J]．通信学报，2002，23(2)：88-91．
[12] 操红武，李波．基于自适应分块的快速分形图像压缩[J]．中国图像图形学报，1998，3(2)：91-95．
[13] Chong Sze Tong Fast Fractal Image Encoding Based on Adaptive Search[J]. IEEE Transactions on Image Processing, 2001, 10(9): 1269-1277.
[14] 陈刚，金以文．基于金字塔正交小波分解的快速分形图像编码[J]．电子学报，1998，26(8)：37-42．
[15] Saupe D. Accelerating fractal image compression by multi-dimen-sional nearest neighbor search[A]. Data Compression Conference, 1995. 222-231.
[16] 蔡芳，徐乃平，孙隆和．快速分形图像压缩算法[J]．西北工业大学学报，1999，17(3)：355-359．
[17] 张元亮，郑南宁，贾天旭．基于遗传算法的分形图像压缩[J]．信息与控制，1998，27(6)：469-474．
[18] 周艳，吴敏金．基于方向剖分的小波域分形图像压缩[J]．华东师范大学学报(自然科学版)，2002，(3)：20-26．
[19] 杜世培，杨木．分形图像压缩方法的改进与实现[J]．贵州工业大学学报，2000，29(4)：56-59．
[20] 印鉴，魏思兵，谭兆信．一种新的分形图像压缩方法[J]．计算机研究与发展，1999，36(4)：451-454．
[21] Mario Polvere, Michele Nappi. Speed-up in fractal image coding: comparison of methods[J]. IEEE Transactions on Image Compression 2000, 9(16): 1002-1009.
[22] Lee S J, Wu P Y, Sun K T. Fast image compression using neural networks[A]. Neural Networks Proceedings[C], IEEE World Congress on Computational Intelligence, 1998, 1. 613-618.
[23] Barahav Z, Malan D, Karnin E. Hierarchical interpretation of fractal image coding and its application to fast decoding[A]. Proceedings of the IEEE International Conference on Digital Signal Processing[C], Nicosia, Cyprus, July, 1993. 190-195.
[24] 张颖，余英林．一种基于去均值的加速收敛的分形压缩算法[J]．中国图像图形学报，1998，3(4)：285-289．
[25] 张亮．高速自适应分形图像压缩算法[A]．98大连——香港国际计算机会议．283-287．
[26] Jin Li, C-C Jay Kuo. Image compression with a hybrid wavelet-fractal coder[J]. IEEE Transactions on Image Processing, 1999, 8(6): 868-874.
[27] Yao Zhao, Baozong Yuan. A hybrid image compression scheme combining block-based fractal coding and DCT[J]. Signal Pro-cessing. Image Communication, 1996, (8): 73-78.
[28] 陈晓，朱耀庭，朱光喜．基于预测模型的分形图像压缩编码方法[J]．中国图像图形学报，1998，3(6)：471-475．
[29] Jeng J H, Truong T K, Sheu J R. Fast fractal image compression using the Hadamard transform[J]. IEE proceedings of Vision, Image Signal Processing 2000, 6: 571-574.
[30] 洪安祥，陈刚，吴炯峰等．基于分形编码的图像相似匹配研究[J]．电子学报，2002，30(5)：624-627．
[31] 张炜，高峻斌．图像压缩中小波与向量小波的选择[J]．华中理工大学学报，1999，27(7)：98-100．
[32] 谭南虹，孙光宇，等．一种基于小波——LBG的图像压缩研究[J]．贵州师范大学学报，1998，16(3)：38-41．
[33] 郑勇，周正华，朱维乐．基于数结构矢量分类的小波图像网格编码矢量量化[J]．通信学报，2001，22(9)：108-114．
[34] 魏海，沈兰荪．基于分类矢量量化的图像压缩和检索算法[J]．电子学报，2001，29(7)：933-936．
[35] 杨边，陆哲明，徐殿国等．基于临近像素的低复杂度预测矢量量化图像压缩编码算法[J]．电子学报，2003，31(5)：707-710．
[36] Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3445-3462.
[37] 王相海，张福炎．基于形态学膨胀操作的小波图像比率可分级编码研究[J]．计算机学报，2002，25(1)：80-86．
[38] Zhou Wang Embedded Foveation Image Coding[J]. IEEE Transactions on Image Processing, 2001, 10(10): 1397-1410.
[39] 王向阳，杨红颖．基于多阈值与嵌入零树小波的图像压缩方法[J]．通信学报，2001，22(12)：88-93．
[40] 周建鹏，杨义先．一种基于小波变换的低比特率混合图像编码方法[J]．电子学报，1999，27(2)：126-128．
[41] 杨春玲，余英林．基于三维小波变换嵌入式视频压缩算法的研究[J]．电子学报，2001，29(10)：1381-1383．
[42] 彭进业，俞卞章，张烨等．一种改进的图像自适应零树编码方法[J]．电子学报，2002，30(4)：591-593．
[43] 冯前进，陈武凡．基于内嵌块优化等级树(EBOHT)算法的图像编码[J]．计算机学报，2002，25(11)：1170-1174．
[44] 张宗平，刘贵忠，杨一文．嵌入分层聚类的小波零树图像编码[J]．计算机学报，2002，25(11)：1189-1194．
[45] Michael B.Martin and Amy E.Bell New Image Compression Technology Using Multiwavelets and Multiwavelet Packets[J]. IEEE Transactions on Image Processing, 2001, 10(4): 500-510.
[46] 王玲，宋国乡．多小波的预处理及其在图像压缩中的应用[J]．电子学报，2001，29(10)：1418-1420．
[47] 吴渝，王国胤，吴国新．基于三层提升方案的小波变换及图像压缩[J]．计算机应用，2002，22(5)：4-6．
[48] NTT——数论变换算法在图像压缩技术中的应用研究[J]．计算机学报，2000，23(8)：887-891．
[49] 白跃彬，卢华斌等．图像压缩技术及其进展[J]．微机发展，1996，4：24-27．
[50] Sambhunath Biswas. Hilbert scan and image compression[A]. IEEE International Conf on Pattern Recongition, 2000, vol 3: 207-210.
[51] mer Nezih Gerek, A Enis etin. Adaptive polyphase subband decomposition structures for image compression[J]. IEEE Transactions on Image Compressing. 2000, 9(10): 1649-1660.
[52] 黄贤武．数字图像处理与压缩编码技术[M]．成都：电子科技大学出版社，2000
[53] 李建平．小波分析方法的的应用[M]．重庆：重庆大学出版社，1999
[54] 沈兰荪．图像编码与异步传输[M]．北京：人民邮电出版社，1998
[55] 阮秋琦．数字图像处理学[M]．北京：电子工业出版社，2001年1月第1版
[56] 毛玉星．基于小波分析的活动图像压缩方法[J]．计算机应用，2002，22(2)：53-55
[57] John Goutsias, Henk JAM Heijmans.Nonlinear multiresolution signal Decomposition sechemes Part Ⅰ: Morphological pyramids[J].IEEE Trans.Image Processing, 2000, 9(11): 1862-1876。
[58] Zhang H, Blum R S.Acategorization of multi-scale decomposition based image fusion scheme With a performance study for a digital camera application[J]. Proc.IEEE, 1999, 87(8): 1315-1326.
[59] STEPHANE G. MALLAT A Theory for Multiresolution Signal Decomposition: The Wavelet Representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-692.
[60] 王国秋，袁卫卫．一般的9-7小波滤波器及其图像压缩性能研究[J]．电子学报 2001，29(1)：130-132
[61] 刘方敏，吴永辉，俞建新．JPEG2000图像压缩过程及原理概述[J]．计算机辅助设计与图形学学报 2002，14(10)：905-911，916
[62] 丁绪星，朱日宏，李建欣．JPEG2000及其新技术[J]．计算机应用 2003，23(2)：1-4
[63] Amir Said, William A. Pearlman, A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees[J]. IEEE Transactions on circuits and systems for video technology, 1996, 6(3): 243-250.
[64] Jacqain A.E.Fractal Image Coding: A Review[J].Proc. of IEEE, 1993, 81: 1451-1465
[65] 洪喜勇，陈贺新．基于小波变换的分形图像压缩编码方法[J]．系统工程与电子技术 2002，24(8)：113-116
[66] 赵耀，王红星，袁保宗．分形图像编码研究的进展[J]．电子学报 2000，28(4)：95-101，106
[67] 周宁，汤晓军，孙飞等．提升小波快速算法及其在JPEG2000中的应用[J]．中国有线电视 2002(18)：6-10
[68] I. Daubechies, Wim Sweldens. Factoring wavelet transforms into lifting steps. J. Fourier Anal., 1998, 4(3): 245-267
[69] Gabrel Fernandez, Senthil Periaswamy and Wim Sweldens. LIFTPACK: a software package for wavelet transforms using lifting, Proc. SPIE 2825, Wavelet Applications in Signal and Image Processing, 1996(4): 396-408
[70] 毛玉星，杨士中．小波域矢量编码的视频压缩方法[J]．重庆大学学报(自然科学版) 2003，26(3)：63-66
[71] Hyung tai Cha, Luis F Chaprarro.Adaptive Morphological Representation of signals: Polynomial and Wavelet methods[J].Multidimenstional systems and signal processing. 1997, 8(3): 249-271
[72] 周宁，汤晓军，孙飞等．多小波图象编码[J]．中国图象图形学报 2000，5(A)(4)：309-317
[73] 高西奇，甘露，邹采荣．多小波变换的理论及其在图像处理中的应用[J]．通信学报 1999，20(11)：55-60
[74] Dowine T R, Silverman B W. The discrete multiple wavelet transform and thresholding methods. IEEE Trans SP, 1998, 46 (9): 2558～256
[75] Bui T D, Chen G. Translation-invariant denoising using multiwavelets. IEEE Trans SP, 46 (12): 3414～3420
[76] Miller J.T, LiCC. Adaptive multiwavelet initialization. IEEE Trans SP, 1998, 46 (12): 3282～3292
[77] 毛玉星，杨士中．基于小波和阈值的矢量量化图像压缩方法[J]．计算机应用，2003，23(4)：38-39，42
[78] Cincotti G, LoiG, PappalardoD.Frequency decomposition and compounding of ultra sound medical images With Wavelet packets[J]. IEEE Trans. on Medical Imaging, 2001, 20(8): 764-771.
[79] 李国军，沈永增，计建炳等．一种基于小波包最优基算法的图像压缩方法[J]．浙江工业大学学报 2003，31(1)：48-52
[80] Kalker A A C, Shah I A. Ladder structures for multidimensional linear phase perfect reconstruction filter banks and wavelet Proc[J]. visual Communication Image Procession, 1992, (92): 12-20
[81] 高清维，明军，程玉林．图像小波包子带编码[J]．中国科学技术大学学报 2000，304)：455-459
[82] 李洋帆，鲁晓牧，冯驰．基于ADV601的数字图像压缩方案[J]．应用科技 2002，29(2)：1-2
[83] 李宏亮，刘贵忠，侯兴松等．一种新的二维非线性提升小波变换方法[J]．电子学报 2003，31(1)：21-24
[84] Roger W Brockett, Petros Maragos. Evolution equations for continuous scale morphological filtering[J].IEEE Trans.SignalProeessing, 1994, 42 (12): 3377-3385
[85] Amir Said, William A. Pearlman, Animage multiresolution representation for lossless and
lossy compression[J]. IEEE Trans. Image Processing, 1996, 5(9): 1303-1310.
[86] Strela V. Multiwavelets: theory and application. Ph D Thesis, 1996
[87] Xia X C, et al. Design of prefilters for discrete multiwavelet transforms. IEEE Trans SP, 1 996, 44: 25～35
[88] Miller JT, LiCC. Adaptive multiwavelet initialization. IEEE Trans SP, 1998, 46 (12): 3282～3292
[89] Xia T, Jiang Q T. Optimal multifilter banks: design, related symmetric extension transform and application to image compression, preprint, 1998.
[2] D.Taubman, "High Performance Scalable Image Compression With EBCOT", IEEE Trans. Image Processing, Vol.9, No.7, pp. 1158-1170, July 2000.
[3] ISO/IEC JTC1/SC29/WG1 N505 Call for contributions for JPEG 2000(JTC 1.29.14 15444) Image Coding System March 1997.
[4] ISO/IEC JTC1/SC29/WG1 N390R New work item: JPEG2000 image coding system, March 1997.
[5] Fisher Y. Fractal image compression: theory and application[M]. New York: Spring-Verlag, 1995.
[6] Davoine F, Bertin E, Chassery A. An adaptive partition for fractal image coding[J]. Fractals. 1997, 5: 243-256.
[7] Huaqiu Deng, Wenhua Weng, Li Li, Yingling Yu. An efficient fractal image compression method[A]. 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 1997, 5. 4204-4206.
[8] Wu X, Yao C. Image coding by adaptive tree-structured segmentation[M]. IEEE Computer Society Press, Los Alamitos, CA, 1991, 73-82.
[9] 秦烽，吴征，周峰，姚庆栋．分区IFS图像压缩编码[J]．通信学报，1997，18(5)：1-7．
[10] Davoine F, Svensson J, Chassery I M. A mixed triangular and quadrilateral partition for fractal image coding[A]. Proceedings ICIP-95 (IEEE International Conference on Image Processing)[C], Washington DC, USA, October, 1995, Ⅲ. 284-287.
[11] 王学军，陈贺新．基于方差不变特性的快速分形图像编码方法[J]．通信学报，2002，23(2)：88-91．
[12] 操红武，李波．基于自适应分块的快速分形图像压缩[J]．中国图像图形学报，1998，3(2)：91-95．
[13] Chong Sze Tong Fast Fractal Image Encoding Based on Adaptive Search[J]. IEEE Transactions on Image Processing, 2001, 10(9): 1269-1277.
[14] 陈刚，金以文．基于金字塔正交小波分解的快速分形图像编码[J]．电子学报，1998，26(8)：37-42．
[15] Saupe D. Accelerating fractal image compression by multi-dimen-sional nearest neighbor search[A]. Data Compression Conference, 1995. 222-231.
[16] 蔡芳，徐乃平，孙隆和．快速分形图像压缩算法[J]．西北工业大学学报，1999，17(3)：355-359．
[17] 张元亮，郑南宁，贾天旭．基于遗传算法的分形图像压缩[J]．信息与控制，1998，27(6)：469-474．
[18] 周艳，吴敏金．基于方向剖分的小波域分形图像压缩[J]．华东师范大学学报(自然科学版)，2002，(3)：20-26．
[19] 杜世培，杨木．分形图像压缩方法的改进与实现[J]．贵州工业大学学报，2000，29(4)：56-59．
[20] 印鉴，魏思兵，谭兆信．一种新的分形图像压缩方法[J]．计算机研究与发展，1999，36(4)：451-454．
[21] Mario Polvere, Michele Nappi. Speed-up in fractal image coding: comparison of methods[J]. IEEE Transactions on Image Compression 2000, 9(16): 1002-1009.
[22] Lee S J, Wu P Y, Sun K T. Fast image compression using neural networks[A]. Neural Networks Proceedings[C], IEEE World Congress on Computational Intelligence, 1998, 1. 613-618.
[23] Barahav Z, Malan D, Karnin E. Hierarchical interpretation of fractal image coding and its application to fast decoding[A]. Proceedings of the IEEE International Conference on Digital Signal Processing[C], Nicosia, Cyprus, July, 1993. 190-195.
[24] 张颖，余英林．一种基于去均值的加速收敛的分形压缩算法[J]．中国图像图形学报，1998，3(4)：285-289．
[25] 张亮．高速自适应分形图像压缩算法[A]．98大连——香港国际计算机会议．283-287．
[26] Jin Li, C-C Jay Kuo. Image compression with a hybrid wavelet-fractal coder[J]. IEEE Transactions on Image Processing, 1999, 8(6): 868-874.
[27] Yao Zhao, Baozong Yuan. A hybrid image compression scheme combining block-based fractal coding and DCT[J]. Signal Pro-cessing. Image Communication, 1996, (8): 73-78.
[28] 陈晓，朱耀庭，朱光喜．基于预测模型的分形图像压缩编码方法[J]．中国图像图形学报，1998，3(6)：471-475．
[29] Jeng J H, Truong T K, Sheu J R. Fast fractal image compression using the Hadamard transform[J]. IEE proceedings of Vision, Image Signal Processing 2000, 6: 571-574.
[30] 洪安祥，陈刚，吴炯峰等．基于分形编码的图像相似匹配研究[J]．电子学报，2002，30(5)：624-627．
[31] 张炜，高峻斌．图像压缩中小波与向量小波的选择[J]．华中理工大学学报，1999，27(7)：98-100．
[32] 谭南虹，孙光宇，等．一种基于小波——LBG的图像压缩研究[J]．贵州师范大学学报，1998，16(3)：38-41．
[33] 郑勇，周正华，朱维乐．基于数结构矢量分类的小波图像网格编码矢量量化[J]．通信学报，2001，22(9)：108-114．
[34] 魏海，沈兰荪．基于分类矢量量化的图像压缩和检索算法[J]．电子学报，2001，29(7)：933-936．
[35] 杨边，陆哲明，徐殿国等．基于临近像素的低复杂度预测矢量量化图像压缩编码算法[J]．电子学报，2003，31(5)：707-710．
[36] Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3445-3462.
[37] 王相海，张福炎．基于形态学膨胀操作的小波图像比率可分级编码研究[J]．计算机学报，2002，25(1)：80-86．
[38] Zhou Wang Embedded Foveation Image Coding[J]. IEEE Transactions on Image Processing, 2001, 10(10): 1397-1410.
[39] 王向阳，杨红颖．基于多阈值与嵌入零树小波的图像压缩方法[J]．通信学报，2001，22(12)：88-93．
[40] 周建鹏，杨义先．一种基于小波变换的低比特率混合图像编码方法[J]．电子学报，1999，27(2)：126-128．
[41] 杨春玲，余英林．基于三维小波变换嵌入式视频压缩算法的研究[J]．电子学报，2001，29(10)：1381-1383．
[42] 彭进业，俞卞章，张烨等．一种改进的图像自适应零树编码方法[J]．电子学报，2002，30(4)：591-593．
[43] 冯前进，陈武凡．基于内嵌块优化等级树(EBOHT)算法的图像编码[J]．计算机学报，2002，25(11)：1170-1174．
[44] 张宗平，刘贵忠，杨一文．嵌入分层聚类的小波零树图像编码[J]．计算机学报，2002，25(11)：1189-1194．
[45] Michael B.Martin and Amy E.Bell New Image Compression Technology Using Multiwavelets and Multiwavelet Packets[J]. IEEE Transactions on Image Processing, 2001, 10(4): 500-510.
[46] 王玲，宋国乡．多小波的预处理及其在图像压缩中的应用[J]．电子学报，2001，29(10)：1418-1420．
[47] 吴渝，王国胤，吴国新．基于三层提升方案的小波变换及图像压缩[J]．计算机应用，2002，22(5)：4-6．
[48] NTT——数论变换算法在图像压缩技术中的应用研究[J]．计算机学报，2000，23(8)：887-891．
[49] 白跃彬，卢华斌等．图像压缩技术及其进展[J]．微机发展，1996，4：24-27．
[50] Sambhunath Biswas. Hilbert scan and image compression[A]. IEEE International Conf on Pattern Recongition, 2000, vol 3: 207-210.
[51] mer Nezih Gerek, A Enis etin. Adaptive polyphase subband decomposition structures for image compression[J]. IEEE Transactions on Image Compressing. 2000, 9(10): 1649-1660.
[52] 黄贤武．数字图像处理与压缩编码技术[M]．成都：电子科技大学出版社，2000
[53] 李建平．小波分析方法的的应用[M]．重庆：重庆大学出版社，1999
[54] 沈兰荪．图像编码与异步传输[M]．北京：人民邮电出版社，1998
[55] 阮秋琦．数字图像处理学[M]．北京：电子工业出版社，2001年1月第1版
[56] 毛玉星．基于小波分析的活动图像压缩方法[J]．计算机应用，2002，22(2)：53-55
[57] John Goutsias, Henk JAM Heijmans.Nonlinear multiresolution signal Decomposition sechemes Part Ⅰ: Morphological pyramids[J].IEEE Trans.Image Processing, 2000, 9(11): 1862-1876。
[58] Zhang H, Blum R S.Acategorization of multi-scale decomposition based image fusion scheme With a performance study for a digital camera application[J]. Proc.IEEE, 1999, 87(8): 1315-1326.
[59] STEPHANE G. MALLAT A Theory for Multiresolution Signal Decomposition: The Wavelet Representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-692.
[60] 王国秋，袁卫卫．一般的9-7小波滤波器及其图像压缩性能研究[J]．电子学报 2001，29(1)：130-132
[61] 刘方敏，吴永辉，俞建新．JPEG2000图像压缩过程及原理概述[J]．计算机辅助设计与图形学学报 2002，14(10)：905-911，916
[62] 丁绪星，朱日宏，李建欣．JPEG2000及其新技术[J]．计算机应用 2003，23(2)：1-4
[63] Amir Said, William A. Pearlman, A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees[J]. IEEE Transactions on circuits and systems for video technology, 1996, 6(3): 243-250.
[64] Jacqain A.E.Fractal Image Coding: A Review[J].Proc. of IEEE, 1993, 81: 1451-1465
[65] 洪喜勇，陈贺新．基于小波变换的分形图像压缩编码方法[J]．系统工程与电子技术 2002，24(8)：113-116
[66] 赵耀，王红星，袁保宗．分形图像编码研究的进展[J]．电子学报 2000，28(4)：95-101，106
[67] 周宁，汤晓军，孙飞等．提升小波快速算法及其在JPEG2000中的应用[J]．中国有线电视 2002(18)：6-10
[68] I. Daubechies, Wim Sweldens. Factoring wavelet transforms into lifting steps. J. Fourier Anal., 1998, 4(3): 245-267
[69] Gabrel Fernandez, Senthil Periaswamy and Wim Sweldens. LIFTPACK: a software package for wavelet transforms using lifting, Proc. SPIE 2825, Wavelet Applications in Signal and Image Processing, 1996(4): 396-408
[70] 毛玉星，杨士中．小波域矢量编码的视频压缩方法[J]．重庆大学学报(自然科学版) 2003，26(3)：63-66
[71] Hyung tai Cha, Luis F Chaprarro.Adaptive Morphological Representation of signals: Polynomial and Wavelet methods[J].Multidimenstional systems and signal processing. 1997, 8(3): 249-271
[72] 周宁，汤晓军，孙飞等．多小波图象编码[J]．中国图象图形学报 2000，5(A)(4)：309-317
[73] 高西奇，甘露，邹采荣．多小波变换的理论及其在图像处理中的应用[J]．通信学报 1999，20(11)：55-60
[74] Dowine T R, Silverman B W. The discrete multiple wavelet transform and thresholding methods. IEEE Trans SP, 1998, 46 (9): 2558～256
[75] Bui T D, Chen G. Translation-invariant denoising using multiwavelets. IEEE Trans SP, 46 (12): 3414～3420
[76] Miller J.T, LiCC. Adaptive multiwavelet initialization. IEEE Trans SP, 1998, 46 (12): 3282～3292
[77] 毛玉星，杨士中．基于小波和阈值的矢量量化图像压缩方法[J]．计算机应用，2003，23(4)：38-39，42
[78] Cincotti G, LoiG, PappalardoD.Frequency decomposition and compounding of ultra sound medical images With Wavelet packets[J]. IEEE Trans. on Medical Imaging, 2001, 20(8): 764-771.
[79] 李国军，沈永增，计建炳等．一种基于小波包最优基算法的图像压缩方法[J]．浙江工业大学学报 2003，31(1)：48-52
[80] Kalker A A C, Shah I A. Ladder structures for multidimensional linear phase perfect reconstruction filter banks and wavelet Proc[J]. visual Communication Image Procession, 1992, (92): 12-20
[81] 高清维，明军，程玉林．图像小波包子带编码[J]．中国科学技术大学学报 2000，304)：455-459
[82] 李洋帆，鲁晓牧，冯驰．基于ADV601的数字图像压缩方案[J]．应用科技 2002，29(2)：1-2
[83] 李宏亮，刘贵忠，侯兴松等．一种新的二维非线性提升小波变换方法[J]．电子学报 2003，31(1)：21-24
[84] Roger W Brockett, Petros Maragos. Evolution equations for continuous scale morphological filtering[J].IEEE Trans.SignalProeessing, 1994, 42 (12): 3377-3385
[85] Amir Said, William A. Pearlman, Animage multiresolution representation for lossless and
lossy compression[J]. IEEE Trans. Image Processing, 1996, 5(9): 1303-1310.
[86] Strela V. Multiwavelets: theory and application. Ph D Thesis, 1996
[87] Xia X C, et al. Design of prefilters for discrete multiwavelet transforms. IEEE Trans SP, 1 996, 44: 25～35
[88] Miller JT, LiCC. Adaptive multiwavelet initialization. IEEE Trans SP, 1998, 46 (12): 3282～3292
[89] Xia T, Jiang Q T. Optimal multifilter banks: design, related symmetric extension transform and application to image compression, preprint, 1998.