自适应方向提升小波图像编码技术
摘要
随着现代信息、网络技术的不断发展,以及人们生活水平的提高,人们对图像质量的需求也越来越多,图像数据量便也相应持续增长,成为其继续发展的阻碍力量,图像压缩成为人们所关注的重点之一。
传统的图像压缩主要以DCT为核心,在其应用过程中发现,DCT可能导致图像的“方块效应”和“边缘效应”,这一缺陷严重限制了其在图像压缩领域的使用。DWT解决了这一应用缺陷,随之发展起来,继而成为近十年图像压缩领域最主要的变换方法之一,但在其发展和应用过程中发现,DWT变换不能实现整数小波变换,即变换后的小波系数不能实现精确重构。1996年,Swedens率先提出提升小波,提升小波保留了小波变换的优势,且克服了很多DWT的不足之处,成为现在的重点研究内容。近年来,研究者们针对图像不同区域、不同方向的空间几何特性提出了自适应提升小波,以实现针对这些空间几何特征,采用相对应的、最合适的方向进行提升。本文改进了一种基于插值法的自适应方向提升小波,该方法能够自适应选择最优的方向作为提升方向,而不再局限于水平和垂直方向,并且能够根据像素之间的局部特性,使用拉格朗日插值法进行预测。实验结果表明该方法比传统提升小波具有优越性。
DWT比DCT具有更多优良品质而在图像处理中得到广泛应用,通过小波变换能有效去除图像空间冗余,结合适应于小波变换系数特征的编码算法(如EZW、SPIHT),压缩效果更为突出。虽然经典的编码算法能有效对小波变换后的图像系数进行编码,但理论分析和实验结果仍表明该算法存在一些不足。本文通对SPIHT编码算法进行研究,在扫描过程中对输出比特数和扫描方法做出改进,如对D型集合扫描的输出比特进行优化、改变后续扫描系数的次数等。实验结果表明,改进的编码算法在同等的PSNR下,能够有效减少图像小波变换编码后码流的比特数,减少运行时间,提高效率。最后,通过实验论证分析得出,结合改进的自适应提升小波方法和改进的SPIHT编码算法,较传统方法效果更为显著。
With modern information technology and network continued to develop, people is demanding for image quality higher and higher. However, the amount of image data continued growth in the corresponding became an obstacle to forward. Therefore, image compression becomes one of the key issues. Core of the traditional image compression is mainly DCT, and DCT is one of the most mature compression technologies. DCT, however, led to the "box effect" and the "edge effect" seriously limits their use in image compression field.
DWT became one of the most important transformation method in one of the most important transformation method past decade. However, wavelet coefficients can not achieve accurate reconstruction because DWT can not achieve integer wavelet transform. In 1996, Swedens first proposed the lifting wavelet which not only retain the advantages of wavelet transform and overcome the many shortcomings. Lifting wavelet becomes the focus of research content now. Researchers in connection with different regions, different.directions and the geometric properties of space proposed adaptive lifting wavelet to achieve use of appropriate direction to lifting according geometrical features of these spaces. According to this view, this paper improves an interpolation method based on adaptive directional lifting wavelet construction. This approach can not only adaptively choose the best direction as lifting direction instead of horizontal and vertical direction, but also use the Lagrange's interpolation to predict pixels according to the local characteristics between pixels. Experimental result shows that the proposed method has advantages over the traditional lifting wavelet.
DWT has more superior quality than DCT to be widely used in image processing. Through the wavelet transform effectively removed the image spatial redundancy combine with adapted features of wavelet transform coefficients coding algorithms (such as EZW, SPIHT), compression quality is even more prominent. Although these classical coding algorithms can effectively encode the image coefficients after the wavelet transform, the theoretical analysis and experimental results show that the algorithm is still a number of deficiencies. In this paper, SPIHT coding algorithm has been improved that output bits during the scanning process and scanning methods. Such as output bit after optimizing scanning D set and change number of coefficients of the follow-up scans. Through the experimental results show that the improved encoding algorithm can effectively reduce the code stream of bits which after image wavelet transformed, and the running time in the case of same PSNR. Finally, Through experiments obtained this paper improved adaptive lifting wavelet method combined with improved SPIHT coding algorithm compared with traditional methods has been marked improvement.
传统的图像压缩主要以DCT为核心,在其应用过程中发现,DCT可能导致图像的“方块效应”和“边缘效应”,这一缺陷严重限制了其在图像压缩领域的使用。DWT解决了这一应用缺陷,随之发展起来,继而成为近十年图像压缩领域最主要的变换方法之一,但在其发展和应用过程中发现,DWT变换不能实现整数小波变换,即变换后的小波系数不能实现精确重构。1996年,Swedens率先提出提升小波,提升小波保留了小波变换的优势,且克服了很多DWT的不足之处,成为现在的重点研究内容。近年来,研究者们针对图像不同区域、不同方向的空间几何特性提出了自适应提升小波,以实现针对这些空间几何特征,采用相对应的、最合适的方向进行提升。本文改进了一种基于插值法的自适应方向提升小波,该方法能够自适应选择最优的方向作为提升方向,而不再局限于水平和垂直方向,并且能够根据像素之间的局部特性,使用拉格朗日插值法进行预测。实验结果表明该方法比传统提升小波具有优越性。
DWT比DCT具有更多优良品质而在图像处理中得到广泛应用,通过小波变换能有效去除图像空间冗余,结合适应于小波变换系数特征的编码算法(如EZW、SPIHT),压缩效果更为突出。虽然经典的编码算法能有效对小波变换后的图像系数进行编码,但理论分析和实验结果仍表明该算法存在一些不足。本文通对SPIHT编码算法进行研究,在扫描过程中对输出比特数和扫描方法做出改进,如对D型集合扫描的输出比特进行优化、改变后续扫描系数的次数等。实验结果表明,改进的编码算法在同等的PSNR下,能够有效减少图像小波变换编码后码流的比特数,减少运行时间,提高效率。最后,通过实验论证分析得出,结合改进的自适应提升小波方法和改进的SPIHT编码算法,较传统方法效果更为显著。
With modern information technology and network continued to develop, people is demanding for image quality higher and higher. However, the amount of image data continued growth in the corresponding became an obstacle to forward. Therefore, image compression becomes one of the key issues. Core of the traditional image compression is mainly DCT, and DCT is one of the most mature compression technologies. DCT, however, led to the "box effect" and the "edge effect" seriously limits their use in image compression field.
DWT became one of the most important transformation method in one of the most important transformation method past decade. However, wavelet coefficients can not achieve accurate reconstruction because DWT can not achieve integer wavelet transform. In 1996, Swedens first proposed the lifting wavelet which not only retain the advantages of wavelet transform and overcome the many shortcomings. Lifting wavelet becomes the focus of research content now. Researchers in connection with different regions, different.directions and the geometric properties of space proposed adaptive lifting wavelet to achieve use of appropriate direction to lifting according geometrical features of these spaces. According to this view, this paper improves an interpolation method based on adaptive directional lifting wavelet construction. This approach can not only adaptively choose the best direction as lifting direction instead of horizontal and vertical direction, but also use the Lagrange's interpolation to predict pixels according to the local characteristics between pixels. Experimental result shows that the proposed method has advantages over the traditional lifting wavelet.
DWT has more superior quality than DCT to be widely used in image processing. Through the wavelet transform effectively removed the image spatial redundancy combine with adapted features of wavelet transform coefficients coding algorithms (such as EZW, SPIHT), compression quality is even more prominent. Although these classical coding algorithms can effectively encode the image coefficients after the wavelet transform, the theoretical analysis and experimental results show that the algorithm is still a number of deficiencies. In this paper, SPIHT coding algorithm has been improved that output bits during the scanning process and scanning methods. Such as output bit after optimizing scanning D set and change number of coefficients of the follow-up scans. Through the experimental results show that the improved encoding algorithm can effectively reduce the code stream of bits which after image wavelet transformed, and the running time in the case of same PSNR. Finally, Through experiments obtained this paper improved adaptive lifting wavelet method combined with improved SPIHT coding algorithm compared with traditional methods has been marked improvement.
引文
[1]DS Taubman,MW Marcellin,M Rabbani.JPEG2000:Image compression fundamentals,standards and practice.Journal of Electronic Imaging,2002
[2]申友志,张秦艳,王成优.基于DCT和小波变换图像编码的比较研究.仪表技术,2007,10:5-7
[3]刘刈文,林锦国,梅雪.小波变换图像编码的研究进展.微处理机,2008,2:83-85
[4]SweldensW.The lifting scheme:a construction of second gen2 eration wavelets.S IAM J.Math.Anal,1997,29(2):511-546
[5]Wenpeng Ding,Feng Wu,Shipeng Li.Lifting-based Wavelet Transform with Directionally.Spatial Prediction.presented at the Picture Coding Symp..San Francisco,CA,2004
[6]Wenpeng Ding,Feng Wu,Xiaolin Wu,Shipeng Li,Houqiang Li.Adaptive Directional Lifting-Based Wavelet Transform for Image Coding.Image Processing,IEEE Transactions,2007,16(2):416-427
[7]Pier Luigi Dragotti,Vladan Velisavljevic,Martin Vetterli,Baltasar Beferull-Lozano.Discrete.Directional Wavelet Bases and Frames For Image Compression and Denoising.
[8]Shapiro J M..Embedding image coding using zero trees of wavelet coefficients.IEEE Trans Signal Processing,1993,41(12):3445-3462
[9]Said A,Pearlman W A.A new fast and efficient imagecodec based on set partitioning in hierarchical trees.IEEE Trans on Circuits and Systems for Video Tech,1996,6(03):243-250
[10]Hong Pan,Wan-Chi Siu,Ngai-Fong Law,A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT.Signal Processiong:Image Communication 23(2008) 146-161.
[11]Tahar Brahimi,Ali Melit,Fouad Khelifi.An improved SPIHT algorithm for lossless image coding.Digital Signal Processing.2009,19:220-228
[12]Ki-Lyug Kima,Sung-Woong Ra.Performance improvement of the SPIHT coder.Signal Processing:Image Communication.2004,19:29-36
[13]周景超,戴汝为,肖柏华.图像质量评价研究综述.计算机科学,2008,35(7):1-5
[14]叶盛楠,苏开娜,肖创柏等.基于结构信息提取的图像质量评价.电子学报,2008,36(5):856-861
[15]王宇庆,刘维亚,王勇.一种基于局部方差和结构相似度的图像质量评价方法.光电子激光,2008,19(11):1546-1553
[16]徐普安,叶愚冬,章琦.一种新的图像质量评价方法.计算机工程与设计,2004,25(3),418-420
[17]童辉,张晓美,何国金.小波和傅立叶变换在图像变化检测上的应用比较.计算机工程与应用,2005,32:87-93
[18]王肖芬,徐科军.基于小波变换的基波提取和频率测量.仪器仪表学报,2005,26(2):146-151
[19]Weon-Ki Yoon,Micheal J.Devomey.Power measurement using the wavelet transform.IEEE Trans.on IM.,1998,47(5):1205-1210
[20]董新洲,耿中行,葛耀中等.小波变换应用于电力系统故障信号分析初探.中国电机工程学报,1997,17(6):421-42
[21]韦娜,耿国华,周明全.基于傅立叶变换的医学图像检索算法分析.小型微型计算机系统,2005,26(5):807-809
[22]陈坤,张靖,黄石红.从傅里叶变换到小波变换.汽轮机技术,2007,49(3):183-184
[23]程正兴,杨守志,冯晓霞.小波分析的理论、算法、进展和应用.第一版.北京:国防工业出版社,2007,7
[24]李楠.小波变换—一种新的雷达信号处理方法.现代雷达,1998,2(20):40-42
[25]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,2008,2(41):83-85
[26]薛智刚,李巴津.基于Haar小波的图像变换方法研究.微计算机信息,2006,22(103):275-277
[27]孙百红,李锋.连续小波变换与信号时频分析.火箭推进,2003,29(6):7-11
[28]Zoran C.and Martin V..Discrete-time wavelet extrema representation:Design and consistent reconstruction.Signal Processing,1995,143:681-693
[29]Olivier R..A discrete-time multiresolution theory.IEEE Trans.Signal Processing,41:2591-2606
[30]方勇华,荀毓龙.基于多分辨率分析的光谱信号处理方法.量子电子学报,1999,16(2):164-170
[31]Mallat S.A theory for multi-resolution signal decomposition:The wavelet representation.IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(07):674-693
[32]傅鹂,王丹,吕海翠.一种基于提升小波变换的图像融合新算法.微电子学 与计算机,2009,26(4):64-69
[33]姚屹,邹北骥.一种新的基于自适应提升小波的图像检索算法.小型微型计算机系统,2009,30(6):1160-1164
[34]宗常进,毕军涛,董军宇.基于离散小波变换的信号分解算法研究.计算机工程与应用,2009,45(8):165-167
[35]Information technology—JPEG 2000 image coding system:Core coding system,ISO/IEC 15444-1,2004
[36]Sweldens W..The lifting scheme:A custom-design construction of biorthogonal wavelets.Appl.Comput.Harmon.Anal.,1996,3(2):186-200
[37]Yu Liu,King Ngi Ngan.Weighted Adaptive Lifting-Based Wavelet Transform for Image Coding.IEEE TRANSACTIONS ON IMAGE PROCESSING,2008,17(4):500-511
[38]Zhuoer Shi,G.W.Wei,Donald J.Kouri,David K.Hoffman,and Zheng Bao.Lagrange Wavelets for Signal Processing.Image Processing,IEEE Transactions,2001,10(10):1488-150
[39]刘乾.基于方向预测的图像编码新技术.上海交通大学,2008.7
[40]James E.Fowler and Beatrice Pesquet-Popescu.An Overview on Wavelets in Source Coding.Communications,and Networks,EURASIP Journal on Image and Video Processing Volume 2007,Article ID 60539,27 pages
[41]Said A.and 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,.
[42]Islam A.and Pearlman W.A.,Embedded and efficient low complexity hierarchical image coder,in Visual Communication sand Image Processing,K.Aizawa,R.L.Stevenson,and Y.-Q.Zhang,Eds.,vol.3653 of Proceedings of SPIE,pp.294-305,San Jose,Calif,USA,January 1999.
[43]Pearlman W.A.,Islam A.,Nagaraj N.,and SaidA..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
[44]Fowler J.E..Shape-adaptive coding using binary set splitting with k-d trees,in Proceedings of IEEE International Conference on Image Processing(ICIP '04),2004,2:1301-1304
[45]"Information Technology—JPEG 2000 Image Coding System—Part 1:Core Coding System," ISO/IEC 15444-1,2000.
[46]尹显东,李在铭,姚军等.图像压缩标准研究的发展与前景.信息与电子工程,2003,1(4):326-331
[47]刘方敏,吴永辉,俞建新.JPEG2000图像压缩过程及原理概述.计算机辅助设计与图形学学报,2002,14(10):905-916
[48]Wallace G K.The JPEG Still Picture Compression Standard.IEEE Trans.Consumer Electronics,1992,38(1):18-34.
[49]Wang Y.,Ostermann J.,Y.-Q.Zhang,候正信等译,视频处理与通信,电子工业出版社,2003
[50]Tu C.,Tran T.D.and Liang J.,Error resilient pre/post-filtering for DCT-based block coding systems,IEEE Trans.on Image Processing,2006,15(1):30-39
[51]Oh S.-K.and Park H.W.,Analysis of IDCT and motion-compensation mismatches betweenspatial-domain and transform-domainmotion-compensated coders,IEEE Trans.on Circuits andSystems for Video Technology,Jul.2005,15(7):835-843
[52]薛猛,刘兵.图像编码标准化的发展与现状.计算机技术与发展,2007,17(6):90-93
[53]Shapiro J M.Embedded image coding using zerotrees of wavelet coefficients.IEEE Trans.On Image Processing,1992,1(2):244-250
[54]邵晨.一种基于EZW图像编码的改进算法.电子元器件应用,2008,10(7):68-70
[55]安丹丹,王宝珠.一种改进的嵌入式零树小波编码算法.信息通信,2007,5:38-40
[56]毛立强.嵌入式零树小波编码算法研究.微机发展,2004,14(7):109-114
[57]周熠.基于小波分析的嵌入零树静态图像压缩方法.计算机应用与软件,2004,21(8):78-80
[58]Shapiro J.M.A fast technique for identifying zerotrees in the EZW algorithm.Acoustics,Speech,and Signal Processing,1996,ICASSP-96.Conference Proceedings,1996 IEEE International Conference on,3:1455-1458
[59]杨云峰,钟似玢.一种改进的基于小波零树的图象编码算法.中国图象图形学报,2001,6(6):542-546
[60]Xuhong Liu,Fuheng Liu,Guijuan Kuang,Xiu Yi.Improved Image Coding Algorithm Based on Embedded Zerotree.Software Engineering,Artificial Intelligence,Networking,and Parallel/Distributed Computing,2007.SNPD 2007.Eighth ACIS International Conference on.2:189-192
[61]安丹丹,王宝珠.一种改进的嵌入式零树小波编码算法.信息通信,2007,5:38-40
[62]朱锦华,许茹,陈化宾.基于提升小波变换的SPECK图像编码算法.电子工程师,2006,32(2):37-39
[63]Taubman D.High performance scalable image compression with EBCOT.IEEE Transactions on Image Processing,2000,9(07):1158-1170
[64]邱自华,陈宇拓等.一种改进的SPIHT图像编码方法.计算机与数字工程,2007,35(4):122-125
[65]秦琴,滕奇志.一种改进的SPIHT图像编码算法.四川大学学报(自然科学版),2007,44(3):525-530
[66]王丽,张培珍.基于一种可变分类域值的SPIHT算法.信息与电子工程,2007,5(4):280-283
[67]Wheeler F.W.,Pearlman W.A..SPIHT image compression without lists.Acoustics.Speech,and Signal Processing,2000.ICASSP'00.Proceedings.2000IEEE International Conference on.6(4):2047-2050
[68]张专成,武国斌,赵怀勋,闫小萍.一种基于系数状态表的SPIHT图像编码算法.中国图象图形学报,2006,11(2):162-168
[69]李哲涛,王仕果,王灵矫.一种改进的SPIHT图像压缩方法,科学技术与工程,2008,8(14),4009-4012
[70]王卫国,郭宝龙.嵌入式小波图像编码算法的研究.高技术通信,2002,12(9):106-110
[2]申友志,张秦艳,王成优.基于DCT和小波变换图像编码的比较研究.仪表技术,2007,10:5-7
[3]刘刈文,林锦国,梅雪.小波变换图像编码的研究进展.微处理机,2008,2:83-85
[4]SweldensW.The lifting scheme:a construction of second gen2 eration wavelets.S IAM J.Math.Anal,1997,29(2):511-546
[5]Wenpeng Ding,Feng Wu,Shipeng Li.Lifting-based Wavelet Transform with Directionally.Spatial Prediction.presented at the Picture Coding Symp..San Francisco,CA,2004
[6]Wenpeng Ding,Feng Wu,Xiaolin Wu,Shipeng Li,Houqiang Li.Adaptive Directional Lifting-Based Wavelet Transform for Image Coding.Image Processing,IEEE Transactions,2007,16(2):416-427
[7]Pier Luigi Dragotti,Vladan Velisavljevic,Martin Vetterli,Baltasar Beferull-Lozano.Discrete.Directional Wavelet Bases and Frames For Image Compression and Denoising.
[8]Shapiro J M..Embedding image coding using zero trees of wavelet coefficients.IEEE Trans Signal Processing,1993,41(12):3445-3462
[9]Said A,Pearlman W A.A new fast and efficient imagecodec based on set partitioning in hierarchical trees.IEEE Trans on Circuits and Systems for Video Tech,1996,6(03):243-250
[10]Hong Pan,Wan-Chi Siu,Ngai-Fong Law,A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT.Signal Processiong:Image Communication 23(2008) 146-161.
[11]Tahar Brahimi,Ali Melit,Fouad Khelifi.An improved SPIHT algorithm for lossless image coding.Digital Signal Processing.2009,19:220-228
[12]Ki-Lyug Kima,Sung-Woong Ra.Performance improvement of the SPIHT coder.Signal Processing:Image Communication.2004,19:29-36
[13]周景超,戴汝为,肖柏华.图像质量评价研究综述.计算机科学,2008,35(7):1-5
[14]叶盛楠,苏开娜,肖创柏等.基于结构信息提取的图像质量评价.电子学报,2008,36(5):856-861
[15]王宇庆,刘维亚,王勇.一种基于局部方差和结构相似度的图像质量评价方法.光电子激光,2008,19(11):1546-1553
[16]徐普安,叶愚冬,章琦.一种新的图像质量评价方法.计算机工程与设计,2004,25(3),418-420
[17]童辉,张晓美,何国金.小波和傅立叶变换在图像变化检测上的应用比较.计算机工程与应用,2005,32:87-93
[18]王肖芬,徐科军.基于小波变换的基波提取和频率测量.仪器仪表学报,2005,26(2):146-151
[19]Weon-Ki Yoon,Micheal J.Devomey.Power measurement using the wavelet transform.IEEE Trans.on IM.,1998,47(5):1205-1210
[20]董新洲,耿中行,葛耀中等.小波变换应用于电力系统故障信号分析初探.中国电机工程学报,1997,17(6):421-42
[21]韦娜,耿国华,周明全.基于傅立叶变换的医学图像检索算法分析.小型微型计算机系统,2005,26(5):807-809
[22]陈坤,张靖,黄石红.从傅里叶变换到小波变换.汽轮机技术,2007,49(3):183-184
[23]程正兴,杨守志,冯晓霞.小波分析的理论、算法、进展和应用.第一版.北京:国防工业出版社,2007,7
[24]李楠.小波变换—一种新的雷达信号处理方法.现代雷达,1998,2(20):40-42
[25]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,2008,2(41):83-85
[26]薛智刚,李巴津.基于Haar小波的图像变换方法研究.微计算机信息,2006,22(103):275-277
[27]孙百红,李锋.连续小波变换与信号时频分析.火箭推进,2003,29(6):7-11
[28]Zoran C.and Martin V..Discrete-time wavelet extrema representation:Design and consistent reconstruction.Signal Processing,1995,143:681-693
[29]Olivier R..A discrete-time multiresolution theory.IEEE Trans.Signal Processing,41:2591-2606
[30]方勇华,荀毓龙.基于多分辨率分析的光谱信号处理方法.量子电子学报,1999,16(2):164-170
[31]Mallat S.A theory for multi-resolution signal decomposition:The wavelet representation.IEEE Transactions on Pattern Analysis and Machine Intelligence,1989,11(07):674-693
[32]傅鹂,王丹,吕海翠.一种基于提升小波变换的图像融合新算法.微电子学 与计算机,2009,26(4):64-69
[33]姚屹,邹北骥.一种新的基于自适应提升小波的图像检索算法.小型微型计算机系统,2009,30(6):1160-1164
[34]宗常进,毕军涛,董军宇.基于离散小波变换的信号分解算法研究.计算机工程与应用,2009,45(8):165-167
[35]Information technology—JPEG 2000 image coding system:Core coding system,ISO/IEC 15444-1,2004
[36]Sweldens W..The lifting scheme:A custom-design construction of biorthogonal wavelets.Appl.Comput.Harmon.Anal.,1996,3(2):186-200
[37]Yu Liu,King Ngi Ngan.Weighted Adaptive Lifting-Based Wavelet Transform for Image Coding.IEEE TRANSACTIONS ON IMAGE PROCESSING,2008,17(4):500-511
[38]Zhuoer Shi,G.W.Wei,Donald J.Kouri,David K.Hoffman,and Zheng Bao.Lagrange Wavelets for Signal Processing.Image Processing,IEEE Transactions,2001,10(10):1488-150
[39]刘乾.基于方向预测的图像编码新技术.上海交通大学,2008.7
[40]James E.Fowler and Beatrice Pesquet-Popescu.An Overview on Wavelets in Source Coding.Communications,and Networks,EURASIP Journal on Image and Video Processing Volume 2007,Article ID 60539,27 pages
[41]Said A.and 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,.
[42]Islam A.and Pearlman W.A.,Embedded and efficient low complexity hierarchical image coder,in Visual Communication sand Image Processing,K.Aizawa,R.L.Stevenson,and Y.-Q.Zhang,Eds.,vol.3653 of Proceedings of SPIE,pp.294-305,San Jose,Calif,USA,January 1999.
[43]Pearlman W.A.,Islam A.,Nagaraj N.,and SaidA..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
[44]Fowler J.E..Shape-adaptive coding using binary set splitting with k-d trees,in Proceedings of IEEE International Conference on Image Processing(ICIP '04),2004,2:1301-1304
[45]"Information Technology—JPEG 2000 Image Coding System—Part 1:Core Coding System," ISO/IEC 15444-1,2000.
[46]尹显东,李在铭,姚军等.图像压缩标准研究的发展与前景.信息与电子工程,2003,1(4):326-331
[47]刘方敏,吴永辉,俞建新.JPEG2000图像压缩过程及原理概述.计算机辅助设计与图形学学报,2002,14(10):905-916
[48]Wallace G K.The JPEG Still Picture Compression Standard.IEEE Trans.Consumer Electronics,1992,38(1):18-34.
[49]Wang Y.,Ostermann J.,Y.-Q.Zhang,候正信等译,视频处理与通信,电子工业出版社,2003
[50]Tu C.,Tran T.D.and Liang J.,Error resilient pre/post-filtering for DCT-based block coding systems,IEEE Trans.on Image Processing,2006,15(1):30-39
[51]Oh S.-K.and Park H.W.,Analysis of IDCT and motion-compensation mismatches betweenspatial-domain and transform-domainmotion-compensated coders,IEEE Trans.on Circuits andSystems for Video Technology,Jul.2005,15(7):835-843
[52]薛猛,刘兵.图像编码标准化的发展与现状.计算机技术与发展,2007,17(6):90-93
[53]Shapiro J M.Embedded image coding using zerotrees of wavelet coefficients.IEEE Trans.On Image Processing,1992,1(2):244-250
[54]邵晨.一种基于EZW图像编码的改进算法.电子元器件应用,2008,10(7):68-70
[55]安丹丹,王宝珠.一种改进的嵌入式零树小波编码算法.信息通信,2007,5:38-40
[56]毛立强.嵌入式零树小波编码算法研究.微机发展,2004,14(7):109-114
[57]周熠.基于小波分析的嵌入零树静态图像压缩方法.计算机应用与软件,2004,21(8):78-80
[58]Shapiro J.M.A fast technique for identifying zerotrees in the EZW algorithm.Acoustics,Speech,and Signal Processing,1996,ICASSP-96.Conference Proceedings,1996 IEEE International Conference on,3:1455-1458
[59]杨云峰,钟似玢.一种改进的基于小波零树的图象编码算法.中国图象图形学报,2001,6(6):542-546
[60]Xuhong Liu,Fuheng Liu,Guijuan Kuang,Xiu Yi.Improved Image Coding Algorithm Based on Embedded Zerotree.Software Engineering,Artificial Intelligence,Networking,and Parallel/Distributed Computing,2007.SNPD 2007.Eighth ACIS International Conference on.2:189-192
[61]安丹丹,王宝珠.一种改进的嵌入式零树小波编码算法.信息通信,2007,5:38-40
[62]朱锦华,许茹,陈化宾.基于提升小波变换的SPECK图像编码算法.电子工程师,2006,32(2):37-39
[63]Taubman D.High performance scalable image compression with EBCOT.IEEE Transactions on Image Processing,2000,9(07):1158-1170
[64]邱自华,陈宇拓等.一种改进的SPIHT图像编码方法.计算机与数字工程,2007,35(4):122-125
[65]秦琴,滕奇志.一种改进的SPIHT图像编码算法.四川大学学报(自然科学版),2007,44(3):525-530
[66]王丽,张培珍.基于一种可变分类域值的SPIHT算法.信息与电子工程,2007,5(4):280-283
[67]Wheeler F.W.,Pearlman W.A..SPIHT image compression without lists.Acoustics.Speech,and Signal Processing,2000.ICASSP'00.Proceedings.2000IEEE International Conference on.6(4):2047-2050
[68]张专成,武国斌,赵怀勋,闫小萍.一种基于系数状态表的SPIHT图像编码算法.中国图象图形学报,2006,11(2):162-168
[69]李哲涛,王仕果,王灵矫.一种改进的SPIHT图像压缩方法,科学技术与工程,2008,8(14),4009-4012
[70]王卫国,郭宝龙.嵌入式小波图像编码算法的研究.高技术通信,2002,12(9):106-110