嵌入式小波图像编码算法及应用研究
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摘要
随着Internet的迅速发展,图像压缩在多媒体信息的存储和传输中起着至关重要的作用。传统的基于DCT的编码方案在高压缩比条件下不可避免地会出现方块效应和飞蚊噪声,严重影响主观质量。小波变换因其特有的与人眼视觉相符的多分辨率分析能力以及方向选择能力而被广泛应用于图像压缩领域。JPEG2000标准的颁布,说明小波变换已成功取代DCT成为新一代编码算法的主要变换工具。嵌入式小波图像编码方法具有分辨率可分级、质量可分级以及较强的抗误码性能等优良特性,成为图像编码算法研究的主要方向。但目前的研究方法尚未能完全利用小波系数的所有统计特性,如何更好地利用小波变换系数的各种相关性以有效组织小波系数,并将小波变换技术与其它技术相结合以进一步提高压缩性能,是小波图像编码算法研究的主要问题。
本文对现有的小波图像编码算法进行了比较详细的分析,主要从以下四方面对嵌入式小波图像编码算法及应用进行研究。
(1)对基于分形的小波图像编码算法进行了深入研究。针对分形编码匹配搜索时间开销大的问题,提出了一种基于分块的分形搜索树结构,有效地减小了定义域池的搜索范围,并提高了匹配精度。对值域块则依据不同子带小波系数的重要性采取自适应的划分方式,然后寻找一组压缩仿射变换,使其构成的IFS逼近给定的吸引子,最后对获得的分形参数进行熵编码。该算法重构图像质量有所提高,特别在中低码率下PSNR提高明显。
(2)由于分形编码具有编解码不平衡的问题,进一步从SVM的角度对小波图像编码算法进行了研究。在小波变换基础上,构建了一种适合SVM回归的树结构,并对树中显著性小波系数的分布规律进行了分析,提出了一种线性阈值选取方法,使得参与回归的小波系数分布趋于均匀,以利于回归。在此基础上提出依据阈值进行误差参数动态选取的方法,进一步提高了回归拟合精度。最后对支持向量及其权重进行熵编码。该算法实现了嵌入式特性,与现有算法相比压缩比有了较大提高。
(3)虽然小波变换与分形和SVM结合取得了较好的编码效果,但小波变换在表示二维图像时并不是最优或最稀疏的函数表示方法。为此对基于Contourlet变换的图像编码算法进行了研究。将小波变换与Contourlet结合获取非冗余特性的Contourlet变换,然后对分解后的子带进行基于HVS的视觉加权处理。针对Contourlet方向分解没有考虑子带内系数分布特性的问题,研究了熵随子带方向分解数的变化情况,进而提出了基于熵的方向分解优化算法,增强了子带内部的局部相关性。在此基础上采用SPECK算法对经过方向优化分解的子带进行编码。该算法不仅峰值信噪比得到提高,而且对局部纹理失真较小。
(4)对基于小波包的EBCOT岩心图像压缩进行了研究。基于岩心图像纹理特征突出的特点,采用小波包分解方式以增强对高频细节的表示能力。针对率失真方法计算量较大以及熵不能完全表征图像纹理信息的问题,提出了利用分形维数确定最优小波包基的方法,分解方式与子带方向特性比较吻合。在此基础上采用EBCOT算法对岩心图像进行压缩,并提出了通道并行扫描方法以提高EBCOT算法的执行速度。实验结果表明,算法的压缩比和速度均高于JPEG2000。
With the rapid development of the Internet, image compression plays a vital role in the storage and transmission of multimedia information. Conventional DCT-based coding scheme will inevitably produce block artifacts and mosquito noise in the case of high compression ratio, which results in a seriously harming to the subjective quality of the reconstructed image. For the capability of multi-resolution analysis consistent with human visual system and the direction selectivity, wavelet transform has been widely used in the field of image compression. The promulgating of JPEG2000 indicates that wavelet transform has successfully replaced DCT and become a major transform tool of the new generation of image coding algorithm. Embedded wavelet image coding, taking the advantage of resolution scalable, quality scalable and strong error-resilient performance, has been an important research direction of image coding. But the current methods are still not able to take full use of all the statistical characteristics of wavelet coefficients, how to make better use of the variety of relevance of wavelet transform coefficients in order to effectively organize the wavelet coefficients, especially to combine wavelet transform with other techniques to further enhance the compression performance, are the main topics in the research of wavelet image coding.
In this dissertation, the popular wavelet image coding algorithms are analyzed in detail, then embedded wavelet image coding algorithms are studied mainly from four aspect as follows.
(1)The fractal based wavelet image coding algorithm was studied deeply. Aimed at the problem of too much time overhead for matching search of fractal image coding, a block based fractal searching tree structure was proposed, which reduced the searching range of domain pool effectively and improved the match precision. A self-adaptive way was adopted to partition range blocks according to the importance of wavelet coefficients in different sub-bands. Then seek for a group of compression affine transforms to make the IFS approximate to the given attractors, and at last carry out entropy coding to the fractal parameters obtained. The quality of reconstructed image is better than that of the existing algorithms, especially the PSNR increases significantly in medium and low bit-rate.
(2)Considering the unbalance problem of fractal in coding and decoding, the wavelet image coding algorithm was studied further from the perspective of SVM. Basing on the wavelet transform, a tree structure suitable for SVM regression was constructed, and the distribution of significant coefficients in the tree was analyzed. Then a linear threshold selection method was proposed to make the distribution of coefficients involved in the regression tend to be uniform in order to facilitate the regression process. On that basis, a dynamic error parameter selection method was put forward according to the thresholds and the precision of the regression fitting was improved further. At last the support vectors and their weights are entropy coded. The algorithm is provided with embedded feature, and the compression ratio improved significantly.
(3)Although better coding effect was achieved by combining with fractal and SVM, the wavelet transform is not the best or the most sparse in two dimensional image representation. So Contourlet transform based image coding algorithm was studied. Combine the wavelet transform with Contourlet to obtain non-redundant Contourlet transform, then perform visual weighting based on HVS to each sub-band. For that the direction decomposition of Contourlet do not consider the distribution characteristics of coefficients inside each sub-band, the changing situation of entropy with the direction number of sub-band decomposition was studied. Then an entropy based optimization algorithm of direction decomposition was presented and the local relevance inside each sub-band was enhanced. The SPECK algorithm was adopted to encode each sub-band decomposed through the optimized direction decomposition algorithm. Not only the PSNR of the reconstructed image is improved, but also the distortion for local texture is much less.
(4)Core image compression using wavelet packet based EBCOT algorithm was studied. Considering that the texture feature is predominant in core images, wavelet packet decomposition was adopted to enhance the representation ability to high-frequency details. Due to the large amount of calculation of the rate-distortion method and the incapacity in representing texture information effectively of entropy method, a fractal dimension based optimal wavelet packet base selection method was proposed, in which the decomposition pattern is consistent with the direction feature of each sub-band. On this basis, use EBCOT algorithm to execute compression to core image, and a parallel pass scanning method was proposed to enhance the speed of EBCOT. The experimental results demonstrate that the compression ratio and the speed are both higher than JPEG2000.
本文对现有的小波图像编码算法进行了比较详细的分析,主要从以下四方面对嵌入式小波图像编码算法及应用进行研究。
(1)对基于分形的小波图像编码算法进行了深入研究。针对分形编码匹配搜索时间开销大的问题,提出了一种基于分块的分形搜索树结构,有效地减小了定义域池的搜索范围,并提高了匹配精度。对值域块则依据不同子带小波系数的重要性采取自适应的划分方式,然后寻找一组压缩仿射变换,使其构成的IFS逼近给定的吸引子,最后对获得的分形参数进行熵编码。该算法重构图像质量有所提高,特别在中低码率下PSNR提高明显。
(2)由于分形编码具有编解码不平衡的问题,进一步从SVM的角度对小波图像编码算法进行了研究。在小波变换基础上,构建了一种适合SVM回归的树结构,并对树中显著性小波系数的分布规律进行了分析,提出了一种线性阈值选取方法,使得参与回归的小波系数分布趋于均匀,以利于回归。在此基础上提出依据阈值进行误差参数动态选取的方法,进一步提高了回归拟合精度。最后对支持向量及其权重进行熵编码。该算法实现了嵌入式特性,与现有算法相比压缩比有了较大提高。
(3)虽然小波变换与分形和SVM结合取得了较好的编码效果,但小波变换在表示二维图像时并不是最优或最稀疏的函数表示方法。为此对基于Contourlet变换的图像编码算法进行了研究。将小波变换与Contourlet结合获取非冗余特性的Contourlet变换,然后对分解后的子带进行基于HVS的视觉加权处理。针对Contourlet方向分解没有考虑子带内系数分布特性的问题,研究了熵随子带方向分解数的变化情况,进而提出了基于熵的方向分解优化算法,增强了子带内部的局部相关性。在此基础上采用SPECK算法对经过方向优化分解的子带进行编码。该算法不仅峰值信噪比得到提高,而且对局部纹理失真较小。
(4)对基于小波包的EBCOT岩心图像压缩进行了研究。基于岩心图像纹理特征突出的特点,采用小波包分解方式以增强对高频细节的表示能力。针对率失真方法计算量较大以及熵不能完全表征图像纹理信息的问题,提出了利用分形维数确定最优小波包基的方法,分解方式与子带方向特性比较吻合。在此基础上采用EBCOT算法对岩心图像进行压缩,并提出了通道并行扫描方法以提高EBCOT算法的执行速度。实验结果表明,算法的压缩比和速度均高于JPEG2000。
With the rapid development of the Internet, image compression plays a vital role in the storage and transmission of multimedia information. Conventional DCT-based coding scheme will inevitably produce block artifacts and mosquito noise in the case of high compression ratio, which results in a seriously harming to the subjective quality of the reconstructed image. For the capability of multi-resolution analysis consistent with human visual system and the direction selectivity, wavelet transform has been widely used in the field of image compression. The promulgating of JPEG2000 indicates that wavelet transform has successfully replaced DCT and become a major transform tool of the new generation of image coding algorithm. Embedded wavelet image coding, taking the advantage of resolution scalable, quality scalable and strong error-resilient performance, has been an important research direction of image coding. But the current methods are still not able to take full use of all the statistical characteristics of wavelet coefficients, how to make better use of the variety of relevance of wavelet transform coefficients in order to effectively organize the wavelet coefficients, especially to combine wavelet transform with other techniques to further enhance the compression performance, are the main topics in the research of wavelet image coding.
In this dissertation, the popular wavelet image coding algorithms are analyzed in detail, then embedded wavelet image coding algorithms are studied mainly from four aspect as follows.
(1)The fractal based wavelet image coding algorithm was studied deeply. Aimed at the problem of too much time overhead for matching search of fractal image coding, a block based fractal searching tree structure was proposed, which reduced the searching range of domain pool effectively and improved the match precision. A self-adaptive way was adopted to partition range blocks according to the importance of wavelet coefficients in different sub-bands. Then seek for a group of compression affine transforms to make the IFS approximate to the given attractors, and at last carry out entropy coding to the fractal parameters obtained. The quality of reconstructed image is better than that of the existing algorithms, especially the PSNR increases significantly in medium and low bit-rate.
(2)Considering the unbalance problem of fractal in coding and decoding, the wavelet image coding algorithm was studied further from the perspective of SVM. Basing on the wavelet transform, a tree structure suitable for SVM regression was constructed, and the distribution of significant coefficients in the tree was analyzed. Then a linear threshold selection method was proposed to make the distribution of coefficients involved in the regression tend to be uniform in order to facilitate the regression process. On that basis, a dynamic error parameter selection method was put forward according to the thresholds and the precision of the regression fitting was improved further. At last the support vectors and their weights are entropy coded. The algorithm is provided with embedded feature, and the compression ratio improved significantly.
(3)Although better coding effect was achieved by combining with fractal and SVM, the wavelet transform is not the best or the most sparse in two dimensional image representation. So Contourlet transform based image coding algorithm was studied. Combine the wavelet transform with Contourlet to obtain non-redundant Contourlet transform, then perform visual weighting based on HVS to each sub-band. For that the direction decomposition of Contourlet do not consider the distribution characteristics of coefficients inside each sub-band, the changing situation of entropy with the direction number of sub-band decomposition was studied. Then an entropy based optimization algorithm of direction decomposition was presented and the local relevance inside each sub-band was enhanced. The SPECK algorithm was adopted to encode each sub-band decomposed through the optimized direction decomposition algorithm. Not only the PSNR of the reconstructed image is improved, but also the distortion for local texture is much less.
(4)Core image compression using wavelet packet based EBCOT algorithm was studied. Considering that the texture feature is predominant in core images, wavelet packet decomposition was adopted to enhance the representation ability to high-frequency details. Due to the large amount of calculation of the rate-distortion method and the incapacity in representing texture information effectively of entropy method, a fractal dimension based optimal wavelet packet base selection method was proposed, in which the decomposition pattern is consistent with the direction feature of each sub-band. On this basis, use EBCOT algorithm to execute compression to core image, and a parallel pass scanning method was proposed to enhance the speed of EBCOT. The experimental results demonstrate that the compression ratio and the speed are both higher than JPEG2000.
引文
[1] Shapiro J M. Embedded image coding using zerotree of wavelet coefficients. IEEE Transactions on Signal Processing, 1993, 41(12): 3445-3462
[2] Said A, Pearlman W A. A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, 1996, 6(3): 243-250
[3] Taubman D. EBCOT: Embedded block coding with optimized truncation. N1020R, ISO/IEC JTC1/SC29/WG1, 1998
[4] Woods J W, Oneil S D. Subband coding of images. IEEE Trans. Acoust Speech and Signal Proc, 1986, 34(5): 1278-1288
[5] Mallat S. Multiresolution approximation and wavelet orthonormal bases of L2 (R). Trans Amer Math Soc, 1989, 315: 69-87
[6] Taubman D S, Marcellin M W. JPEG2000 Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, 2002: 231-235
[7] Andra K, Chakrabarti C, Acharya T. A High-performance JPEG2000 Architecture. IEEE Transactions on Circuits and Systems for Video Technology, 2003, 13(3): 209-218
[8] Islam A, Pearlman W A. An embedded and efficient low-complexity hierarchical image coder. In: Proceedings of SPIE International Conference on Visual Communication and Image Processing. San Jose, 1999.294-305
[9] Pearlman W A, Islam A, Nagaraj N, et al. Efficient, low-complexity image coding with a set-partitioning embedded block coder. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(11): 1219-1235
[10] Zandi A, Allen J, Schwartz E, et al. CREW: Compression with reversible embedded wavelets. In: Proceedings of IEEE Data Compression Conference. Snowbird, USA, 1995: 212-221
[11] Tian J, Wells R O. Embedded image coding using wavelet difference reduction. Kluwer Academic Publishers: wavelet and Video compression, 1998: 289-301
[12] Fowler J E. Shape-adaptive coding using binary set splitting with k-d trees. In: Proceedings of the International Conference on Image Processing. Singapore, 2004.1301-1304
[13] Linde Y, Buzo A, Gray R M. An algorithm for vector quantizer design. IEEE Transactions on Communications, 1980, 28(1): 84-95
[14] Sampson D G., et al. Wavelet vector quantization scheme for image sequence coding at 64kbit/s. IEE Electronics Letters, 1995, 31(2):92-93
[15] Dre C, Branis G, Goutis C. Image coding using vector quantization of wavelet coefficients. Microprocessing and Microprogramming, 1994, 40: 927-930
[16] Bradley J N, Stockham T G, Mathews V J. An optimal design procedure for intraband vector quantized subband coding. IEEE Trans. Communications, 1995, 43(2):523-533
[17] Da Silva E A B, Sampson D G., Ghanbari M. A successive approximation vector quantizer for wavelet transform image coding. IEEE Trans. on Image Processing, 1996, 5(2): 299-310
[18] Hub Y, Hwang J J, Rao K R. Block wavelet transform coding of images using classified vector quantization. IEEEE Trans. on CSVT, 1995, 5(1): 63-67
[19] Mohsenian N, Nasrabadi N M. Edge-based subband VQ techniques for images and video. IEEE Tans. Circuit Syst. VideoTechnol, 1994, 4(l): 53-67
[20] Liu L, Wang W. A wavelet image coding algorithm based on human visual system characteristics. In: Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition. 2008: 113-117
[21] Zhang X, Guo J, Zou K, et al. Improved SPIHT algorithm based on associative memory neural network and human visual system. In: Proceedings of the International Conference on Intelligent Computation Technology and Automation. 2008: 200-203
[22] Guo L, Meng Y. An HVS-based wavelet image coder. In: Proceedings of the IASTED International Conference on Signal and Image Processing. 2006: 216-221
[23] Wang A, Zhang Y, Gu Y. SAR image compression using HVS model. In: proceedings of the CIE International Conference on Radar. 2006: 1-4
[24] Sreelekha G, Sathidevi P S. A New Wavelet Based Compression Scheme Replicating Human Visual System Processing. In: proceedings of the International Conference on Computational Intelligence and Multimedia Applications. 2007: 41-46
[25]梁亚玲,杨春玲,余英林.基于人眼视觉特性的ROI编码.计算机应用, 2005, 25(7): 1598-1601
[26]胡海平,莫玉龙.基于人的视觉特征的双正交小波基的构造.光学学报, 2001,21(7): 845-849
[27]郑伟,崔跃利,王芳.基于小波变换的图像压缩编码研究综述.通信技术, 2008, 41(02): 83-85
[28] Rinaldo R, Calvagno G. Image coding by block prediction of multiresolution subimages. IEEE Transactions on Image Processing, 1995, 4(7): 909-920
[29] Davis G M. Self-quantization of wavelet subtrees: a wavelet based theory of fractal image compression. In: Proceedings of Data Compression Conference. 1995: 232-241
[30] Davis G M. A wavelet-based analysis of fractal image compression. IEEE Transactions on Image Processing, 1998, 7(2): 141-154
[31] Po L M, Zhang Y, Cheung K W, et al. Efficient subtree splitting algorithm for wavelet-based fractal image coding. In: Proceedings of IEEE International Symposium on Circuits and Systems. Monterey, USA, 1998: 106-109
[32] Jiang S, Shuang K, Sun L W. Fractal Image Coding Based on Oriented Wavelet Sub-tree. In: Proceedings of the International Symposium on Computer Science and Computational Technology. 2008: 273-276
[33]王端芳,姜威.结合小波零树的分形图像编码.系统工程与电子技术, 2005, 27(6): 1120-1122
[34] Zhao X, Zhai L. Wavelet-fractal based compression of ophthalmic image. In: Proceedings of SPIE - The International Society for Optical Engineering. 2006, 6027: 1-8
[35] Iano Y, da Silva F S, Cruz A L M. A fast and efficient hybrid fractal-wavelet image coder. IEEE Transactions on Image Processing, 2006, 15(1): 98-105
[36] Fisher Y. Fractal Image Compression: Theory and Application. New York: Springer-Verlag, 1995
[37] Abdelwahab A A, Elmogazy H A. Wavelet-based adaptive embedded fractal image coding. In: Proceedings of the International Conference on Computer Engineering and Systems. 2006: 202-207
[38] Jiang M, Jiang Z. A New Searchless Fractal Image Encoding Method Based on Wavelet Decomposition. In: Proceedings of the World Congress on Intelligent Control and Automation. Dalian, China, 2006: 9583-9586
[39] Zhang L, Xi L. Hybrid image compression using fractal-wavelet prediction. WSEAS Transactions on Systems, 2007, 6(3): 556-561
[40] Coifman R R, Meyer Y. Orthonormal Wave Packet Bases. New Haven: Department of Mathematics, Yale University, 1990
[41] Coifman R R, Meyer Y, Win K M V. Wavelets and their applications. Boston: Jones and Bartlett, 1992
[42] Mallat S.信号处理中的小波导引.杨力华,戴道清等译. 2版.北京:机械工业出版社, 2003
[43] Coifman R R, Wickerhauser M V. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory, 1992, 38(2): 713-718
[44] Ramchandran K, Vetterli M. Best wavelet packet bases in a rate-distortion sense. IEEE Transactions on Image Processing, 1994, 2(2): 160-175
[45] Cumming I G, Wang J. Polarimetric SAR data compression using wavelet packets in a block coding scheme. In: Proceedings of International Geoscience and Remote Sensing Symposium. Toronto, Canada, 2002: 1126-1128
[46] Liu Y, Ngan K N. Embedded wavelet packet object-based image coding based on context classification and quadtree ordering. Signal Processing: Image Communication, 2006, 21(2): 143-155
[47]陈东明,朱志良,高晓兴等.一种最优小波包基搜索算法.哈尔滨工业大学学报, 2009, 41(1): 200-203
[48] Xiong Z, Ramchandran K, Orchard M T. Wavelet packets coding using space-frequency quantization. IEEE Transactions on Image Processing, 1998, 7(6): 892-898
[49] Sembiring J, Nakabayashi M, Soemintapoera K, et al. Image Compression Using Zerotrees of Wavelet Packets in Rate-Distortion Sense. In: Proceedings of the Asia-Pacific Conference on Communications. 2003: 822-824
[50] Sprljan N, Grgic S, Grgic M. Modified SPIHT Algorithm for Wavelet Packet Image Coding. Real-Time Imaging, 2005, 11(5-6): 378-388
[51] Lu T, Wen K, Chang P. Block Reordering Wavelet Packet SPIHT Image Coding. In: Proceedings of IEEE Pacific Rlm conference on multimedia. Beijing, China, 2001: 442-449
[52] ?ktem R, ?ktem L, Egiazarian K. A Wavelet Packet Transform based Image Coding Algorithm. In: Proceedings of the Nordic Signal Processing Symposium. Kolmarden, Sweden, 2000: 77-80
[53] Rajpoot N M, Wilson R G, Meyer F G, et al. Adaptive wavelet packet basis selection for zerotree image coding. IEEE Transactions on Image Processing, 2003, 12(12): 1460-1472
[54]杨永明,许超.一个基于小波包分解的率失真最优化块分割图像编码算法.中国科学(E辑:信息科学), 2008, 38(8): 1204-1219
[55] Daubechies I. Ten lectures on wavelets. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM), 1992
[56] Goodman T N T, Lee S L. Wavelets of Multiplicity r. Transactions of American Mathematical Society, 1994, 342(1): 307-324
[57] Geronimo J S, Hardin D P, Massopust P R. Fractal functions and wavelet expansions based on several scaling functions. Journal of Approximation Theory, 1994, 78 (3): 373-401
[58] Martin M B, Bell A E. New Image Compression Techniques Using Multiwavelets and Multiwavelet Packets. IEEE Transactions on Image Processing, 2001, 10(4): 500-510
[59] Ragupathy U S, Tamilarasi A. A Novel Method of Image Compression Using Multiwavelets and Set Partitioning Algorithm. Modern Applied Science, 2009, 3(2): 134-143
[60] He X C, Yu S S, Zhou J L. Context- and HVS-based multiwavelet image coding using SPIHT framework. Circuits, systems, and signal processing, 2005, 24(2): 117-134
[61]同武勤,凌永顺,黄超超等.数学形态学和小波变换的红外图像处理方法.光学精密工程, 2007, 15(1): 138-144
[62] Lebrun J, Vetterli M. High order Balanced multiwavelets: theory, factorization and design. IEEE Transactions on Signal Processing, 2001, 49(9): 1918-1929
[63] Wang J, Kang S, Liu Y, et al. Image Compression Based on Balanced Orthogonal Multiwavelet and SOFM. In: Proceedings of the Chinese Control Conference. Harbin, Heilongjiang. 2006: 1387-1390
[64]范文兵,秦瑞林,丁国忠.平衡多小波滤波器及其在图像编码中的应用.计算机工程, 2007, 33(6): 187-190
[65]赵秀影,商玉凤,翟林培等.基于EBCOT的平衡多小波航空图像压缩编码.光学精密工程, 2007, 15(11): 1796-1801
[66] Pan H, Siu W C, Law N F. A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT. Signal Processing: Image Communication, 2008, 23(3): 146-161
[67] Cagnazzo M, Poggi G, Verdoliva L. Costs and Advantages of Shape - Adaptive Wavelet Transform for Region-Based Image Coding. IEEE International Conference on Image Processing, 2005, 3: 197-200
[68] Lee W S, Kassim A A. Signal and Image Approximation Using Interval Wavelet Transform. IEEE Trans. Image Processing, 2007, 16(1): 46-56
[69] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693
[70] Mallat S. Multi frequency channel decompositions of images and wavelet models. IEEE Transactions on Acoustic Speech and Signal Processing, 1989, 37(12): 2091-2110
[71] Meyer F G, Averbuch A, Strombern J O. Fast adaptive wavelet packet image compression. IEEE Trans Image Processing. 2000, 9(6): 792-800
[72] Voukelatos S P, Soraghan J J. Very low bit-rate color video coding using adaptive subband vector quantization with dynamic bit allocation. IEEE Transactions on Circuits and systems for video Technology, 1997, 7(2): 424-428
[73] Soongsathitanon S. Wavelet-based image compression using a new partial search LBG algorithm. WSEAS Transactions on Communications, 2007, 6(5): 715-721
[74] Chitwong S, Cheevasuvit F, Sinthuvanichsaid J. Image coding using adaptive vector quantization of wavelet coefficients. Proceedings of SPIE - The International Society for Optical Engineering, 2001, 4391: 191-196
[75] Esakkirajan S, Malmurugan N, Veerakumar T, et al. Image compression using adaptive wavelet packet and multistage vector quantization. IEEE Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems, 2008: 1-4
[76] Voinson T, Guillemot L, Moureaux J M. Image compression using lattice vector quantization with code book shape adapted thresholding. IEEE International Conference on Image Processing, 2002, 2: 641-644
[77] Xu L, Kou B, Zhao Y. New algorithm of classified vector quantization based on wavelet transform for image coding. Proceedings of SPIE-The International Society for Optical Engineering, 2001, 4551: 183-188
[78] Barnsley M F, Elton J H, Hardin D P. Recurrent iterated function systems. Constructive Approximation, 1989, 5(1): 3-31
[79] Jacquin A E. Fractal image coding: A review. Proc. of the IEEE, 1993, 81(10): 1451-1465
[80] Fisher Y. Fractal image compression. Fractals, 1994, 2(3): 321-329
[81] Jin L, Kuo C C J. Image Compression with a Hybrid wavelet-Fractal Coder. IEEE Transactions on Image Processing, 1999, 8(6): 868-874
[82] Kim T, Van Dyck R E, Miller D J. Hybrid fractal zerotree wavelet image coding. Signal Processing: Image Communication, 2002, 17(4): 347-360
[83] Mandelbrot B B. Fractals: Form, Chance, and Dimension. 2nd edition. W.H. Freeman and Co Ltd, 1977
[84] Falconer K J.曾文曲等译. Fractal Geometry: Mathematical Foundations and Applications. John Wiley and Sons,东北工学院出版社, 1991
[85]文志英.分形几何的数学基础.上海:上海科技教育出版社,2000
[86] Wornell G. Signal Processing with Fractals. Prentice Hall, 1995
[87]曾文曲,文有为,孙炜.分形小波与图像压缩.沈阳:东北大学出版社, 2002, 1-156
[88]陈守吉,张立明.分形与图像压缩.上海:上海科技教育出版社, 1998
[89]庄军,李弼程.一种基于灰度共生矩阵的文本图像识别方法.计算机工程, 2006, 32(3): 214-216
[90] Robinson J, Kecman V. Combining support vector machine learning with the discrete cosine transform in image compression. IEEE Transactions on Neural Networks, 2003, 14(4): 950-958
[91] Ernesto S, Adolf G, Danny M. Support Vector Machines and quad-trees applied to image compression. Proceedings of the 6th NORSIG 2004: 105-108
[92] Ahmed R. Wavelet-Based Image Compression Using Support Vector Machine Learning and Encoding Techniques. Proceedings of the Eighth International Conference on Computer Graphics and Imaging, Hawaii, USA , 2005: 162-166
[93] Chang C C, Liao C T. An image coding scheme using SMVQ and support vector machines. Neurocomputing, 2006, 69(16-18): 2327-2335
[94] Jiao R, Li Y, Wang Q, et al. SVM regression and its application to image compression. Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC2005, 2005: 747-756
[95] Sweldens W. The lifting scheme, a construction of second generation Wavelets. Journal of Appl. And Comput Harmonic Analysis, 1997, 29(02): 511-546
[96] Kim K I, Jung K, Kim J H. Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1631-1639
[97] Hao P Y, Chiang J H. A fuzzy model of support vector regression. IEEE International Conference on Fuzzy Systems, 2003, 1: 738-742
[98] Cristianini N,Shawe-Taylor J.支持向量机导论(英文版).北京:机械工业出版社, 2005
[99]邓乃扬,田英杰.数据挖掘中的新方法:支持向量机.上海:科学技术出版社, 2004
[100]汪雪林,韩华,彭思龙.基于小波域局部高斯模型的图像复原.软件学报, 2004, 15(3): 433–450
[101]李元诚,焦润海,李波.一种基于支持向量机的小波图像压缩方法.北京航空航天大学学报, 2006, 32(5): 598-602
[102]张立宝,王珂.一种基于整数小波变换的图像编码算法.软件学报, 2003, 14(8): 1433-1438
[103]史耀媛,王晨,宋恒.基于支持向量机的图像预测编码算法.光电子技术, 2006, 26(4): 272-275
[104]赵楠楠,孙红星,徐新和.基于小波变换和SVM的图像压缩仿真研究.系统仿真学报, 2006, 18(11): 3034-3037
[105] Chen J M, Li L, Nie L Y. Wavelet Image Compression by Using Hybrid Kernel SVM. Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, China, 2008: 3056-3060
[106] Zhang X G, Gao Ding, Zhang X G, et al. Robust Wavelet Support Vector Machine for Regression Estimation. International Journal of Information Technology, 2005, 11(9): 35-45
[107] She Q, Su H, Dong L, et al. Support Vector Machine with Adaptive Parameters in Image Coding. International Journal of Innovative Computing, Information and Control, 2008, 4(2): 359-367
[108] Candes E J. Ridgelets: Theory and Application. Ph.D. Thesis. Department of Statistics, Stanford University, 1998
[109] Candès E J, Donoho D L. Curvelets, USA: department of statistics, Stanfod University, 1999
[110] Pennec E L, Mallat S. Image Compression with geometrical wavelets. Proceedings of ICIP 2000, Vancouver, Canada, 2000: 661-664
[111] Do M N, Vetterli M. Contourlets: A Directional Multiresolution Image Representation. Rochester: Proceedings of IEEE International Conference on Image Processing, 2002: 357-360
[112] Eslami R, Radha H. On low bit-rate coding using the Contourlet Transform. Signals, Systems and Computers, 2003, 2: 1524-1528
[113] Eslami R, Radha H. Wavelet-based contourlet transform and its application to image coding. Proceedings of IEEE International Conference on Image Processing, Singapore, Oct. 2004
[114] Chappelier V, Guillemot C, Marinkovic S. Image Coding with Iterated Contourlet and Wavelet Transforms. IEEE International Conference on Image Processing, 2004: 3157-3160
[115] Eslami R, Radha H. Wavelet-based contourlet coding using an SPIHT-like algorithm. Proceedings of International Conference on Information Sciences and Systems, Princeton, 2004: 784-788
[116]肖羽,王相海.基于Contourlet的中低码率图像质量可分级编码算法.计算机研究与发展. 2008, 45(6): 1020-1028
[117]宋蓓蓓,许录平,孙文方.一种基于小波的Contourlet变换的图像压缩算法.西安交通大学学报, 2007, 41(4): 94-97
[118] Burt P J, Adelson E H. The Laplacian Pyramid as a Compact Image Code. IEEE Transactions on Communications, 1983, 31(4): 532-540
[119]林立宇,张友焱,孙涛等. Contourlet变换——影像处理应用.北京:科学出版社, 2008
[120] Bamberger R H, Smith M J T. A Filter Bank for the Directional Decomposition of Images: Theory and Design. IEEE Trans. Signal Processing, 1992, 40(4): 882-893
[121] Do M N, Vetterli M. Pyramidal directional filter banks and curvelets. IEEE International Conference on Image Processing, Thessaloniki, Greece, 2001: 158-161
[122]易文娟,郁梅,蒋刚毅. Contourlet:一种有效的方向多尺度变换分析方法.计算机应用研究, 2006, 23(9): 18-22
[123] Do M N, Vetterli M. Contourlets: A Directional Multiresolution Image Representation. Rochester: Proc. of IEEE International Conference on Image Processing (ICIP), 2002: 357-360
[124] Lee I, Kim J, et al. Wavelet transform image coding using human visual system. IEEE Asia-Pacific Conference on Circuits and Systems, 1994: 619-623
[125] Miloslavski M, Ho Y S. Zerotree wavelet image coding based on the human visual system model. IEEE Asia-Pacific Conference on Circuits and Systems, 1998: 57-60
[126] Kim J Y, Kim L S, et al. An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Transactions on Circuits and Video Technology, 2001, 11(4): 475-484
[127]金炜.一种新颖的非冗余图像多尺度几何分析.电子与信息学报. 2006, 28(11): 2116-2120
[128] Duncan D Y, Do M N. Directional multiscale modeling of images using the contourlet transform. In: IEEE Workshop on Statistical Signal Processing, 2003: 262-265
[129] Sung T Y, Hsin H C. An efficient rearrangement of wavelet packet coefficients for embedded image coding based on SPIHT algorithm. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2007, E90-A(9): 2014-2020
[130] Dobrescu R, Mocanu S. Using fractal dimension as texture discriminator for context base image retrieval. Proceedings of 2007 International Workshop on Systems, Signals and Image Processing, and 6th EURASIP Conference Focused on Speech and Image Processing, Multimedia Communications and Services, 2007: 82-85
[131] Harrouni S. New method for estimating the fractal dimension of discrete temporal signals. IEEE International Symposium on Industrial Electronics, 2008: 2497-2502
[132] Taubman D. High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, 2000, 9(7): 1158-1170
[133] Lian C J, Chen K F, Chen H H, et al. Analysis and architecture design of block-coding engine for EBCOT in JPEG2000. IEEE Transactions on Circuits and Systems for Video Technology, 2003, 13(3): 219-230
[134] Chiang J S, Lin Y S, Hsieh C Y. Efficient pass-parallel architecture for EBCOT in JPEG2000. Proceedings of the 2002 IEEE International Symposium on Circuits and Systems, Scottsdale, 2002, 1: 773-776
[135] Xiong C Y, Tian J W, Liu J. Efficient word-level sequential coding scheme of bit plane coding for EBCOT in JPEG2000. Proceedings of the International Symposium on Consumer Electronics, 2005: 468-472
[2] Said A, Pearlman W A. A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, 1996, 6(3): 243-250
[3] Taubman D. EBCOT: Embedded block coding with optimized truncation. N1020R, ISO/IEC JTC1/SC29/WG1, 1998
[4] Woods J W, Oneil S D. Subband coding of images. IEEE Trans. Acoust Speech and Signal Proc, 1986, 34(5): 1278-1288
[5] Mallat S. Multiresolution approximation and wavelet orthonormal bases of L2 (R). Trans Amer Math Soc, 1989, 315: 69-87
[6] Taubman D S, Marcellin M W. JPEG2000 Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, 2002: 231-235
[7] Andra K, Chakrabarti C, Acharya T. A High-performance JPEG2000 Architecture. IEEE Transactions on Circuits and Systems for Video Technology, 2003, 13(3): 209-218
[8] Islam A, Pearlman W A. An embedded and efficient low-complexity hierarchical image coder. In: Proceedings of SPIE International Conference on Visual Communication and Image Processing. San Jose, 1999.294-305
[9] Pearlman W A, Islam A, Nagaraj N, et al. Efficient, low-complexity image coding with a set-partitioning embedded block coder. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(11): 1219-1235
[10] Zandi A, Allen J, Schwartz E, et al. CREW: Compression with reversible embedded wavelets. In: Proceedings of IEEE Data Compression Conference. Snowbird, USA, 1995: 212-221
[11] Tian J, Wells R O. Embedded image coding using wavelet difference reduction. Kluwer Academic Publishers: wavelet and Video compression, 1998: 289-301
[12] Fowler J E. Shape-adaptive coding using binary set splitting with k-d trees. In: Proceedings of the International Conference on Image Processing. Singapore, 2004.1301-1304
[13] Linde Y, Buzo A, Gray R M. An algorithm for vector quantizer design. IEEE Transactions on Communications, 1980, 28(1): 84-95
[14] Sampson D G., et al. Wavelet vector quantization scheme for image sequence coding at 64kbit/s. IEE Electronics Letters, 1995, 31(2):92-93
[15] Dre C, Branis G, Goutis C. Image coding using vector quantization of wavelet coefficients. Microprocessing and Microprogramming, 1994, 40: 927-930
[16] Bradley J N, Stockham T G, Mathews V J. An optimal design procedure for intraband vector quantized subband coding. IEEE Trans. Communications, 1995, 43(2):523-533
[17] Da Silva E A B, Sampson D G., Ghanbari M. A successive approximation vector quantizer for wavelet transform image coding. IEEE Trans. on Image Processing, 1996, 5(2): 299-310
[18] Hub Y, Hwang J J, Rao K R. Block wavelet transform coding of images using classified vector quantization. IEEEE Trans. on CSVT, 1995, 5(1): 63-67
[19] Mohsenian N, Nasrabadi N M. Edge-based subband VQ techniques for images and video. IEEE Tans. Circuit Syst. VideoTechnol, 1994, 4(l): 53-67
[20] Liu L, Wang W. A wavelet image coding algorithm based on human visual system characteristics. In: Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition. 2008: 113-117
[21] Zhang X, Guo J, Zou K, et al. Improved SPIHT algorithm based on associative memory neural network and human visual system. In: Proceedings of the International Conference on Intelligent Computation Technology and Automation. 2008: 200-203
[22] Guo L, Meng Y. An HVS-based wavelet image coder. In: Proceedings of the IASTED International Conference on Signal and Image Processing. 2006: 216-221
[23] Wang A, Zhang Y, Gu Y. SAR image compression using HVS model. In: proceedings of the CIE International Conference on Radar. 2006: 1-4
[24] Sreelekha G, Sathidevi P S. A New Wavelet Based Compression Scheme Replicating Human Visual System Processing. In: proceedings of the International Conference on Computational Intelligence and Multimedia Applications. 2007: 41-46
[25]梁亚玲,杨春玲,余英林.基于人眼视觉特性的ROI编码.计算机应用, 2005, 25(7): 1598-1601
[26]胡海平,莫玉龙.基于人的视觉特征的双正交小波基的构造.光学学报, 2001,21(7): 845-849
[27]郑伟,崔跃利,王芳.基于小波变换的图像压缩编码研究综述.通信技术, 2008, 41(02): 83-85
[28] Rinaldo R, Calvagno G. Image coding by block prediction of multiresolution subimages. IEEE Transactions on Image Processing, 1995, 4(7): 909-920
[29] Davis G M. Self-quantization of wavelet subtrees: a wavelet based theory of fractal image compression. In: Proceedings of Data Compression Conference. 1995: 232-241
[30] Davis G M. A wavelet-based analysis of fractal image compression. IEEE Transactions on Image Processing, 1998, 7(2): 141-154
[31] Po L M, Zhang Y, Cheung K W, et al. Efficient subtree splitting algorithm for wavelet-based fractal image coding. In: Proceedings of IEEE International Symposium on Circuits and Systems. Monterey, USA, 1998: 106-109
[32] Jiang S, Shuang K, Sun L W. Fractal Image Coding Based on Oriented Wavelet Sub-tree. In: Proceedings of the International Symposium on Computer Science and Computational Technology. 2008: 273-276
[33]王端芳,姜威.结合小波零树的分形图像编码.系统工程与电子技术, 2005, 27(6): 1120-1122
[34] Zhao X, Zhai L. Wavelet-fractal based compression of ophthalmic image. In: Proceedings of SPIE - The International Society for Optical Engineering. 2006, 6027: 1-8
[35] Iano Y, da Silva F S, Cruz A L M. A fast and efficient hybrid fractal-wavelet image coder. IEEE Transactions on Image Processing, 2006, 15(1): 98-105
[36] Fisher Y. Fractal Image Compression: Theory and Application. New York: Springer-Verlag, 1995
[37] Abdelwahab A A, Elmogazy H A. Wavelet-based adaptive embedded fractal image coding. In: Proceedings of the International Conference on Computer Engineering and Systems. 2006: 202-207
[38] Jiang M, Jiang Z. A New Searchless Fractal Image Encoding Method Based on Wavelet Decomposition. In: Proceedings of the World Congress on Intelligent Control and Automation. Dalian, China, 2006: 9583-9586
[39] Zhang L, Xi L. Hybrid image compression using fractal-wavelet prediction. WSEAS Transactions on Systems, 2007, 6(3): 556-561
[40] Coifman R R, Meyer Y. Orthonormal Wave Packet Bases. New Haven: Department of Mathematics, Yale University, 1990
[41] Coifman R R, Meyer Y, Win K M V. Wavelets and their applications. Boston: Jones and Bartlett, 1992
[42] Mallat S.信号处理中的小波导引.杨力华,戴道清等译. 2版.北京:机械工业出版社, 2003
[43] Coifman R R, Wickerhauser M V. Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory, 1992, 38(2): 713-718
[44] Ramchandran K, Vetterli M. Best wavelet packet bases in a rate-distortion sense. IEEE Transactions on Image Processing, 1994, 2(2): 160-175
[45] Cumming I G, Wang J. Polarimetric SAR data compression using wavelet packets in a block coding scheme. In: Proceedings of International Geoscience and Remote Sensing Symposium. Toronto, Canada, 2002: 1126-1128
[46] Liu Y, Ngan K N. Embedded wavelet packet object-based image coding based on context classification and quadtree ordering. Signal Processing: Image Communication, 2006, 21(2): 143-155
[47]陈东明,朱志良,高晓兴等.一种最优小波包基搜索算法.哈尔滨工业大学学报, 2009, 41(1): 200-203
[48] Xiong Z, Ramchandran K, Orchard M T. Wavelet packets coding using space-frequency quantization. IEEE Transactions on Image Processing, 1998, 7(6): 892-898
[49] Sembiring J, Nakabayashi M, Soemintapoera K, et al. Image Compression Using Zerotrees of Wavelet Packets in Rate-Distortion Sense. In: Proceedings of the Asia-Pacific Conference on Communications. 2003: 822-824
[50] Sprljan N, Grgic S, Grgic M. Modified SPIHT Algorithm for Wavelet Packet Image Coding. Real-Time Imaging, 2005, 11(5-6): 378-388
[51] Lu T, Wen K, Chang P. Block Reordering Wavelet Packet SPIHT Image Coding. In: Proceedings of IEEE Pacific Rlm conference on multimedia. Beijing, China, 2001: 442-449
[52] ?ktem R, ?ktem L, Egiazarian K. A Wavelet Packet Transform based Image Coding Algorithm. In: Proceedings of the Nordic Signal Processing Symposium. Kolmarden, Sweden, 2000: 77-80
[53] Rajpoot N M, Wilson R G, Meyer F G, et al. Adaptive wavelet packet basis selection for zerotree image coding. IEEE Transactions on Image Processing, 2003, 12(12): 1460-1472
[54]杨永明,许超.一个基于小波包分解的率失真最优化块分割图像编码算法.中国科学(E辑:信息科学), 2008, 38(8): 1204-1219
[55] Daubechies I. Ten lectures on wavelets. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM), 1992
[56] Goodman T N T, Lee S L. Wavelets of Multiplicity r. Transactions of American Mathematical Society, 1994, 342(1): 307-324
[57] Geronimo J S, Hardin D P, Massopust P R. Fractal functions and wavelet expansions based on several scaling functions. Journal of Approximation Theory, 1994, 78 (3): 373-401
[58] Martin M B, Bell A E. New Image Compression Techniques Using Multiwavelets and Multiwavelet Packets. IEEE Transactions on Image Processing, 2001, 10(4): 500-510
[59] Ragupathy U S, Tamilarasi A. A Novel Method of Image Compression Using Multiwavelets and Set Partitioning Algorithm. Modern Applied Science, 2009, 3(2): 134-143
[60] He X C, Yu S S, Zhou J L. Context- and HVS-based multiwavelet image coding using SPIHT framework. Circuits, systems, and signal processing, 2005, 24(2): 117-134
[61]同武勤,凌永顺,黄超超等.数学形态学和小波变换的红外图像处理方法.光学精密工程, 2007, 15(1): 138-144
[62] Lebrun J, Vetterli M. High order Balanced multiwavelets: theory, factorization and design. IEEE Transactions on Signal Processing, 2001, 49(9): 1918-1929
[63] Wang J, Kang S, Liu Y, et al. Image Compression Based on Balanced Orthogonal Multiwavelet and SOFM. In: Proceedings of the Chinese Control Conference. Harbin, Heilongjiang. 2006: 1387-1390
[64]范文兵,秦瑞林,丁国忠.平衡多小波滤波器及其在图像编码中的应用.计算机工程, 2007, 33(6): 187-190
[65]赵秀影,商玉凤,翟林培等.基于EBCOT的平衡多小波航空图像压缩编码.光学精密工程, 2007, 15(11): 1796-1801
[66] Pan H, Siu W C, Law N F. A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT. Signal Processing: Image Communication, 2008, 23(3): 146-161
[67] Cagnazzo M, Poggi G, Verdoliva L. Costs and Advantages of Shape - Adaptive Wavelet Transform for Region-Based Image Coding. IEEE International Conference on Image Processing, 2005, 3: 197-200
[68] Lee W S, Kassim A A. Signal and Image Approximation Using Interval Wavelet Transform. IEEE Trans. Image Processing, 2007, 16(1): 46-56
[69] Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693
[70] Mallat S. Multi frequency channel decompositions of images and wavelet models. IEEE Transactions on Acoustic Speech and Signal Processing, 1989, 37(12): 2091-2110
[71] Meyer F G, Averbuch A, Strombern J O. Fast adaptive wavelet packet image compression. IEEE Trans Image Processing. 2000, 9(6): 792-800
[72] Voukelatos S P, Soraghan J J. Very low bit-rate color video coding using adaptive subband vector quantization with dynamic bit allocation. IEEE Transactions on Circuits and systems for video Technology, 1997, 7(2): 424-428
[73] Soongsathitanon S. Wavelet-based image compression using a new partial search LBG algorithm. WSEAS Transactions on Communications, 2007, 6(5): 715-721
[74] Chitwong S, Cheevasuvit F, Sinthuvanichsaid J. Image coding using adaptive vector quantization of wavelet coefficients. Proceedings of SPIE - The International Society for Optical Engineering, 2001, 4391: 191-196
[75] Esakkirajan S, Malmurugan N, Veerakumar T, et al. Image compression using adaptive wavelet packet and multistage vector quantization. IEEE Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems, 2008: 1-4
[76] Voinson T, Guillemot L, Moureaux J M. Image compression using lattice vector quantization with code book shape adapted thresholding. IEEE International Conference on Image Processing, 2002, 2: 641-644
[77] Xu L, Kou B, Zhao Y. New algorithm of classified vector quantization based on wavelet transform for image coding. Proceedings of SPIE-The International Society for Optical Engineering, 2001, 4551: 183-188
[78] Barnsley M F, Elton J H, Hardin D P. Recurrent iterated function systems. Constructive Approximation, 1989, 5(1): 3-31
[79] Jacquin A E. Fractal image coding: A review. Proc. of the IEEE, 1993, 81(10): 1451-1465
[80] Fisher Y. Fractal image compression. Fractals, 1994, 2(3): 321-329
[81] Jin L, Kuo C C J. Image Compression with a Hybrid wavelet-Fractal Coder. IEEE Transactions on Image Processing, 1999, 8(6): 868-874
[82] Kim T, Van Dyck R E, Miller D J. Hybrid fractal zerotree wavelet image coding. Signal Processing: Image Communication, 2002, 17(4): 347-360
[83] Mandelbrot B B. Fractals: Form, Chance, and Dimension. 2nd edition. W.H. Freeman and Co Ltd, 1977
[84] Falconer K J.曾文曲等译. Fractal Geometry: Mathematical Foundations and Applications. John Wiley and Sons,东北工学院出版社, 1991
[85]文志英.分形几何的数学基础.上海:上海科技教育出版社,2000
[86] Wornell G. Signal Processing with Fractals. Prentice Hall, 1995
[87]曾文曲,文有为,孙炜.分形小波与图像压缩.沈阳:东北大学出版社, 2002, 1-156
[88]陈守吉,张立明.分形与图像压缩.上海:上海科技教育出版社, 1998
[89]庄军,李弼程.一种基于灰度共生矩阵的文本图像识别方法.计算机工程, 2006, 32(3): 214-216
[90] Robinson J, Kecman V. Combining support vector machine learning with the discrete cosine transform in image compression. IEEE Transactions on Neural Networks, 2003, 14(4): 950-958
[91] Ernesto S, Adolf G, Danny M. Support Vector Machines and quad-trees applied to image compression. Proceedings of the 6th NORSIG 2004: 105-108
[92] Ahmed R. Wavelet-Based Image Compression Using Support Vector Machine Learning and Encoding Techniques. Proceedings of the Eighth International Conference on Computer Graphics and Imaging, Hawaii, USA , 2005: 162-166
[93] Chang C C, Liao C T. An image coding scheme using SMVQ and support vector machines. Neurocomputing, 2006, 69(16-18): 2327-2335
[94] Jiao R, Li Y, Wang Q, et al. SVM regression and its application to image compression. Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC2005, 2005: 747-756
[95] Sweldens W. The lifting scheme, a construction of second generation Wavelets. Journal of Appl. And Comput Harmonic Analysis, 1997, 29(02): 511-546
[96] Kim K I, Jung K, Kim J H. Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1631-1639
[97] Hao P Y, Chiang J H. A fuzzy model of support vector regression. IEEE International Conference on Fuzzy Systems, 2003, 1: 738-742
[98] Cristianini N,Shawe-Taylor J.支持向量机导论(英文版).北京:机械工业出版社, 2005
[99]邓乃扬,田英杰.数据挖掘中的新方法:支持向量机.上海:科学技术出版社, 2004
[100]汪雪林,韩华,彭思龙.基于小波域局部高斯模型的图像复原.软件学报, 2004, 15(3): 433–450
[101]李元诚,焦润海,李波.一种基于支持向量机的小波图像压缩方法.北京航空航天大学学报, 2006, 32(5): 598-602
[102]张立宝,王珂.一种基于整数小波变换的图像编码算法.软件学报, 2003, 14(8): 1433-1438
[103]史耀媛,王晨,宋恒.基于支持向量机的图像预测编码算法.光电子技术, 2006, 26(4): 272-275
[104]赵楠楠,孙红星,徐新和.基于小波变换和SVM的图像压缩仿真研究.系统仿真学报, 2006, 18(11): 3034-3037
[105] Chen J M, Li L, Nie L Y. Wavelet Image Compression by Using Hybrid Kernel SVM. Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, China, 2008: 3056-3060
[106] Zhang X G, Gao Ding, Zhang X G, et al. Robust Wavelet Support Vector Machine for Regression Estimation. International Journal of Information Technology, 2005, 11(9): 35-45
[107] She Q, Su H, Dong L, et al. Support Vector Machine with Adaptive Parameters in Image Coding. International Journal of Innovative Computing, Information and Control, 2008, 4(2): 359-367
[108] Candes E J. Ridgelets: Theory and Application. Ph.D. Thesis. Department of Statistics, Stanford University, 1998
[109] Candès E J, Donoho D L. Curvelets, USA: department of statistics, Stanfod University, 1999
[110] Pennec E L, Mallat S. Image Compression with geometrical wavelets. Proceedings of ICIP 2000, Vancouver, Canada, 2000: 661-664
[111] Do M N, Vetterli M. Contourlets: A Directional Multiresolution Image Representation. Rochester: Proceedings of IEEE International Conference on Image Processing, 2002: 357-360
[112] Eslami R, Radha H. On low bit-rate coding using the Contourlet Transform. Signals, Systems and Computers, 2003, 2: 1524-1528
[113] Eslami R, Radha H. Wavelet-based contourlet transform and its application to image coding. Proceedings of IEEE International Conference on Image Processing, Singapore, Oct. 2004
[114] Chappelier V, Guillemot C, Marinkovic S. Image Coding with Iterated Contourlet and Wavelet Transforms. IEEE International Conference on Image Processing, 2004: 3157-3160
[115] Eslami R, Radha H. Wavelet-based contourlet coding using an SPIHT-like algorithm. Proceedings of International Conference on Information Sciences and Systems, Princeton, 2004: 784-788
[116]肖羽,王相海.基于Contourlet的中低码率图像质量可分级编码算法.计算机研究与发展. 2008, 45(6): 1020-1028
[117]宋蓓蓓,许录平,孙文方.一种基于小波的Contourlet变换的图像压缩算法.西安交通大学学报, 2007, 41(4): 94-97
[118] Burt P J, Adelson E H. The Laplacian Pyramid as a Compact Image Code. IEEE Transactions on Communications, 1983, 31(4): 532-540
[119]林立宇,张友焱,孙涛等. Contourlet变换——影像处理应用.北京:科学出版社, 2008
[120] Bamberger R H, Smith M J T. A Filter Bank for the Directional Decomposition of Images: Theory and Design. IEEE Trans. Signal Processing, 1992, 40(4): 882-893
[121] Do M N, Vetterli M. Pyramidal directional filter banks and curvelets. IEEE International Conference on Image Processing, Thessaloniki, Greece, 2001: 158-161
[122]易文娟,郁梅,蒋刚毅. Contourlet:一种有效的方向多尺度变换分析方法.计算机应用研究, 2006, 23(9): 18-22
[123] Do M N, Vetterli M. Contourlets: A Directional Multiresolution Image Representation. Rochester: Proc. of IEEE International Conference on Image Processing (ICIP), 2002: 357-360
[124] Lee I, Kim J, et al. Wavelet transform image coding using human visual system. IEEE Asia-Pacific Conference on Circuits and Systems, 1994: 619-623
[125] Miloslavski M, Ho Y S. Zerotree wavelet image coding based on the human visual system model. IEEE Asia-Pacific Conference on Circuits and Systems, 1998: 57-60
[126] Kim J Y, Kim L S, et al. An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Transactions on Circuits and Video Technology, 2001, 11(4): 475-484
[127]金炜.一种新颖的非冗余图像多尺度几何分析.电子与信息学报. 2006, 28(11): 2116-2120
[128] Duncan D Y, Do M N. Directional multiscale modeling of images using the contourlet transform. In: IEEE Workshop on Statistical Signal Processing, 2003: 262-265
[129] Sung T Y, Hsin H C. An efficient rearrangement of wavelet packet coefficients for embedded image coding based on SPIHT algorithm. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2007, E90-A(9): 2014-2020
[130] Dobrescu R, Mocanu S. Using fractal dimension as texture discriminator for context base image retrieval. Proceedings of 2007 International Workshop on Systems, Signals and Image Processing, and 6th EURASIP Conference Focused on Speech and Image Processing, Multimedia Communications and Services, 2007: 82-85
[131] Harrouni S. New method for estimating the fractal dimension of discrete temporal signals. IEEE International Symposium on Industrial Electronics, 2008: 2497-2502
[132] Taubman D. High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, 2000, 9(7): 1158-1170
[133] Lian C J, Chen K F, Chen H H, et al. Analysis and architecture design of block-coding engine for EBCOT in JPEG2000. IEEE Transactions on Circuits and Systems for Video Technology, 2003, 13(3): 219-230
[134] Chiang J S, Lin Y S, Hsieh C Y. Efficient pass-parallel architecture for EBCOT in JPEG2000. Proceedings of the 2002 IEEE International Symposium on Circuits and Systems, Scottsdale, 2002, 1: 773-776
[135] Xiong C Y, Tian J W, Liu J. Efficient word-level sequential coding scheme of bit plane coding for EBCOT in JPEG2000. Proceedings of the International Symposium on Consumer Electronics, 2005: 468-472