卫星遥感影像压缩
|
摘要
高分辨率遥感对地观测技术是关系到国家安全和可持续发展的重点技术。从二十世纪八十年代起，世界各主要强国纷纷就这项技术的发展提出了一系列庞大的研究计划。面对严峻的国际形势和世界的发展趋势，我国在“十五”863高科技发展计划中明确提出：大力开展高分辨力多维空间信息获取前沿技术研究，突破多维高分辨力集成光学对地观测系统……。当前，遥感对地观测技术存在几个重要的发展趋势，除了影像的空间分辨率、时间分辨率(重复观测时间短)和光谱分辨率不断提高之外，立体成像技术也是一个重要的发展方向，已经发射和计划中的一些遥感卫星都已具备立体观测功能。
海量数据压缩作为信息获取与处理的一项关键技术，在过去的几年内得到了良好的发展，不过，总体而言，数据压缩技术在航天遥感中的应用尚处于初级阶段，有关的一些研究主要局限于高分辨率光学遥感影像，并且所采用的技术也还有进一步发展的余地。随着遥感卫星小型化的发展，空间数据的规模还将持续增大，信息获取和数据传输的矛盾将日益加剧，现有的压缩技术在效率和速度上无法满足应用要求，数据传输的困难将严重影响到影像分辨率的进一步提高，
针对遥感对地观测技术的发展现状，本文以高分辨率遥感影像、遥感立体像对和三线阵CCD立体影像为研究对象，研究海量遥感影像的高保真压缩技术。根据研究对象之间的逻辑关系(单幅——两幅——三幅)，本文研究分为三个部分，高分辨率遥感影像的压缩部分主要解决单幅遥感影像的实时压缩问题；在单幅影像压缩的基础上，进一步研究遥感立体像对的左、右片之间的相关性，设计相应的补偿算法以提高遥感立体像对的压缩效率；三线阵CCD影像的压缩则可视为遥感立体像对压缩研究的进一步扩展。本文的主要工作可以概括为以下几个方面：
1)提出了基于整数小波变换的遥感影像压缩SBC算法；
高分辨率遥感影像压缩具有一般图像压缩所不具备的特点，压缩算法的设计必须考虑到以下限制条件：a)高分辨率推扫式光学CCD像机幅宽达到两万像素以上；b)压缩算法复杂度应尽可能地降低，以便硬件实现；c)星上缓存空间有限；d)数据传数系统的码率固定不变；e)压缩算法的效率必须比现有基于DCT变换的明显更高。整数小波变换的出现为上述条件的满足提供了一个理想的工具。参考其他小波编码算法，本文提出了子带比特平面编码算法Sub-band Bit-plane Coding(SBC)，该算法采用整数小波变换，最优比特平面编码和上下文相关熵编码，可用于实现高分辨率遥感影像的快速编码。SBC算法支持多分辨率、多信噪比的嵌入式码流结构，可实现从无损到有损任意码率或多种质量的图像压缩。和其他压缩算法相比，SBC算法简单高效，很容易移植到硬件，而且可以并行运算，非常适合于压缩高分辨率单色或彩色遥感影像。在压缩质量上，SBC算法在低码率压缩下重建图像的质量略逊于EBCOT算法，在高码率压缩下重建图像的质量则略优于EBCOT算法。
2)提出了基于虚拟分块的图像压缩容错编码算法；
无线通信系统具有相对较高的误码率，在遭受强电磁干扰的情况下，卫星通信系统的误
码率将迅速上升。在SBC编码算法的基础上，我们提出了一种简单有效的容错编码方案一
一虚拟分块。该算法的基本思想就是对编码图像进行虚拟分块，给每个子块的码流加上一个
同步头，使得压缩以后各个子块能够独立的解码，从而提高系统抗噪能力。虚拟分块并不对
图像分块，而是对小波变换之后的系数分块，因此不会在重建图像上留下分块的痕迹。加入
容错编码以后，sBc算法的抗误码能力有了较大的提高，任何误码只对局部图像发生影响，
对其他分块的图像毫无影响。
3)提出了基于视差补偿和辐射补偿的遥感立体像对压缩算法:
遥感立体像对应用于地形量测时总是左、右片联合地发生作用，但是在前人的研究当中，
左、右片总是被当做孤立的图像分别处理，丝毫不考虑两幅影像之间的关系。针对遥感立体
像对左右片之间存在的较大相关，本文提出了一种基于自适应重叠块立体补偿的遥感立体像
对压缩算法。该算法以左图为基准图像，采用自适应分块视差估计方法计算出右图的视差矢
量，结合辐射校正和重叠块视差补偿技术得到平滑的右图的预测图像，以右图减去预测图像
得到残差图像，然后采用小波压缩算法对残差图像进行压缩。与左、右片独立压缩相比，该
算法的单图有损压缩效率可提高30一40%左右，无损压缩的性能提高5%左右，
4)提出了基于全局辐射补偿和双向视差补偿的三线阵CCD立体影像压缩算法;
针对三线阵CCD立体影像的特性，在遥感立体像对压缩算法的基础上提出了三线阵
ccD立体影像压缩算法。该算法以正视影像为基准，通过双向立体补偿和全局辐射补偿预
测前视影像和后视影像的三线阵CCD立体补偿算法。双向立体补偿是立体补偿技术的扩展，
主要用于抵消前视、后视视差和分块辐射差对影像匹配及预测的影响;全局辐射补偿用于校
正三幅影像由于拍摄角度不同而形成的辐射差。实验中我们发现三线阵CCD立体补偿算法
的预测效率和两幅影像的立体补偿效率相比有所降低，导致预测效率降低的因素主要在于:
l)三线阵CcD影像的前
The earth observation Technique of high-resolution remote sensing is critical to nation security and sustainable development. From 1980's on, all the developed country in the world hold up a serious of research plan in this fields. Faced on such international situation, China proposed definetly in "tenth-five" 863 high-tech development plan: Pay more attention to research the advanced technique of acquiring high-resolution information in Multi-dimension space. At present, not only the increase in spatial, spectral and time resolution but also stereo image technique, which has been primarily carried out by the sent remote sensing satellites, become main tendencies in the fields of the earth observation Technique of remote sensing.
As an important technique to acquire and process information, the data compression has achieved great progress in past years, however, generally it is still the beginning of wide application in space-flight remote sensing because some related research just located in optical high resolution remote sensing image, and the current adopted techniques exist space to develop further. With the development of small size remote sensing satellite, the number of space data will augment unremittingly, and the conflict between acquiring information and transmitting data will be more and more sharp. The present available compression technique can't meet the demands of application in the aspects of efficiency and speed so that it is difficult to transmit the data, which in turn cause not to improve further image resolution.
In this paper, we aim to research high fidelity compression of high-resolution remote sensing image, stereo image pair and three-lined CCD image. According to the logic relationship among research objects (one breadth-two breadth-three breadth), this paper is separated to three parts: the first part, high-resolution remote sensing image compression, focus on real time compression of single breadth; the second part, stereo image pair compression based on the first part, is the estimation of disparity between the left frame and the right frame, a problem similar to the estimation of motion vectors in video coding; the last part, three-lined CCD image compression, is the extension of the second part. The main work of this paper is concluded as following several aspects. 1) Propose the remote sensing image compression scheme (SBC) based on integer
wavelet transformation
Since high-resolution remote sensing compression possesses the special character that the other type image compression have not, the design of compression scheme is restricted within the following limits: a) the swath of high-resolution push-broom optional CCD is beyond 20,000 pixels; b) the compression scheme complexity is as
possible as low so as to be realized in the way of hardware; c) the on-board memory is limited; d) the code rate of data transmitting is fixed; e) the compression efficiency is much higher than that based on DCT. Integer wavelet transformation provides an ideal tool for meeting the above limits. Referencing the other wavelet coding scheme, this paper presents Sub-band Bit-plane Coding (SBC), which adopt integer wavelet transformation, most priority Bit-plane Coding and related context entropy coding, and be applied to realize the fast compression coding of high-resolution image. SBC supports embedded coding enabling arbitrary Rate-distortion control. Contrasted with the other compression approaches, SBC is low complexity, efficient, easy to transplant to hardware, and can parallel computer. In low code rate, the reconstructed image quality of SBC is lightly worse than that of EBCOT, but in high code rate, the result is reverse.
2) Propose error-resilient image compression coding scheme based on virtual partition .
Wireless communication system holds relatively high rate of error code, so error code rate of satellite communication system rise greatly under the condition of intensive electromagnetism interruption. On the basis of SBC, we propose a simple and efficient error-resilient imag
海量数据压缩作为信息获取与处理的一项关键技术，在过去的几年内得到了良好的发展，不过，总体而言，数据压缩技术在航天遥感中的应用尚处于初级阶段，有关的一些研究主要局限于高分辨率光学遥感影像，并且所采用的技术也还有进一步发展的余地。随着遥感卫星小型化的发展，空间数据的规模还将持续增大，信息获取和数据传输的矛盾将日益加剧，现有的压缩技术在效率和速度上无法满足应用要求，数据传输的困难将严重影响到影像分辨率的进一步提高，
针对遥感对地观测技术的发展现状，本文以高分辨率遥感影像、遥感立体像对和三线阵CCD立体影像为研究对象，研究海量遥感影像的高保真压缩技术。根据研究对象之间的逻辑关系(单幅——两幅——三幅)，本文研究分为三个部分，高分辨率遥感影像的压缩部分主要解决单幅遥感影像的实时压缩问题；在单幅影像压缩的基础上，进一步研究遥感立体像对的左、右片之间的相关性，设计相应的补偿算法以提高遥感立体像对的压缩效率；三线阵CCD影像的压缩则可视为遥感立体像对压缩研究的进一步扩展。本文的主要工作可以概括为以下几个方面：
1)提出了基于整数小波变换的遥感影像压缩SBC算法；
高分辨率遥感影像压缩具有一般图像压缩所不具备的特点，压缩算法的设计必须考虑到以下限制条件：a)高分辨率推扫式光学CCD像机幅宽达到两万像素以上；b)压缩算法复杂度应尽可能地降低，以便硬件实现；c)星上缓存空间有限；d)数据传数系统的码率固定不变；e)压缩算法的效率必须比现有基于DCT变换的明显更高。整数小波变换的出现为上述条件的满足提供了一个理想的工具。参考其他小波编码算法，本文提出了子带比特平面编码算法Sub-band Bit-plane Coding(SBC)，该算法采用整数小波变换，最优比特平面编码和上下文相关熵编码，可用于实现高分辨率遥感影像的快速编码。SBC算法支持多分辨率、多信噪比的嵌入式码流结构，可实现从无损到有损任意码率或多种质量的图像压缩。和其他压缩算法相比，SBC算法简单高效，很容易移植到硬件，而且可以并行运算，非常适合于压缩高分辨率单色或彩色遥感影像。在压缩质量上，SBC算法在低码率压缩下重建图像的质量略逊于EBCOT算法，在高码率压缩下重建图像的质量则略优于EBCOT算法。
2)提出了基于虚拟分块的图像压缩容错编码算法；
无线通信系统具有相对较高的误码率，在遭受强电磁干扰的情况下，卫星通信系统的误
码率将迅速上升。在SBC编码算法的基础上，我们提出了一种简单有效的容错编码方案一
一虚拟分块。该算法的基本思想就是对编码图像进行虚拟分块，给每个子块的码流加上一个
同步头，使得压缩以后各个子块能够独立的解码，从而提高系统抗噪能力。虚拟分块并不对
图像分块，而是对小波变换之后的系数分块，因此不会在重建图像上留下分块的痕迹。加入
容错编码以后，sBc算法的抗误码能力有了较大的提高，任何误码只对局部图像发生影响，
对其他分块的图像毫无影响。
3)提出了基于视差补偿和辐射补偿的遥感立体像对压缩算法:
遥感立体像对应用于地形量测时总是左、右片联合地发生作用，但是在前人的研究当中，
左、右片总是被当做孤立的图像分别处理，丝毫不考虑两幅影像之间的关系。针对遥感立体
像对左右片之间存在的较大相关，本文提出了一种基于自适应重叠块立体补偿的遥感立体像
对压缩算法。该算法以左图为基准图像，采用自适应分块视差估计方法计算出右图的视差矢
量，结合辐射校正和重叠块视差补偿技术得到平滑的右图的预测图像，以右图减去预测图像
得到残差图像，然后采用小波压缩算法对残差图像进行压缩。与左、右片独立压缩相比，该
算法的单图有损压缩效率可提高30一40%左右，无损压缩的性能提高5%左右，
4)提出了基于全局辐射补偿和双向视差补偿的三线阵CCD立体影像压缩算法;
针对三线阵CCD立体影像的特性，在遥感立体像对压缩算法的基础上提出了三线阵
ccD立体影像压缩算法。该算法以正视影像为基准，通过双向立体补偿和全局辐射补偿预
测前视影像和后视影像的三线阵CCD立体补偿算法。双向立体补偿是立体补偿技术的扩展，
主要用于抵消前视、后视视差和分块辐射差对影像匹配及预测的影响;全局辐射补偿用于校
正三幅影像由于拍摄角度不同而形成的辐射差。实验中我们发现三线阵CCD立体补偿算法
的预测效率和两幅影像的立体补偿效率相比有所降低，导致预测效率降低的因素主要在于:
l)三线阵CcD影像的前
The earth observation Technique of high-resolution remote sensing is critical to nation security and sustainable development. From 1980's on, all the developed country in the world hold up a serious of research plan in this fields. Faced on such international situation, China proposed definetly in "tenth-five" 863 high-tech development plan: Pay more attention to research the advanced technique of acquiring high-resolution information in Multi-dimension space. At present, not only the increase in spatial, spectral and time resolution but also stereo image technique, which has been primarily carried out by the sent remote sensing satellites, become main tendencies in the fields of the earth observation Technique of remote sensing.
As an important technique to acquire and process information, the data compression has achieved great progress in past years, however, generally it is still the beginning of wide application in space-flight remote sensing because some related research just located in optical high resolution remote sensing image, and the current adopted techniques exist space to develop further. With the development of small size remote sensing satellite, the number of space data will augment unremittingly, and the conflict between acquiring information and transmitting data will be more and more sharp. The present available compression technique can't meet the demands of application in the aspects of efficiency and speed so that it is difficult to transmit the data, which in turn cause not to improve further image resolution.
In this paper, we aim to research high fidelity compression of high-resolution remote sensing image, stereo image pair and three-lined CCD image. According to the logic relationship among research objects (one breadth-two breadth-three breadth), this paper is separated to three parts: the first part, high-resolution remote sensing image compression, focus on real time compression of single breadth; the second part, stereo image pair compression based on the first part, is the estimation of disparity between the left frame and the right frame, a problem similar to the estimation of motion vectors in video coding; the last part, three-lined CCD image compression, is the extension of the second part. The main work of this paper is concluded as following several aspects. 1) Propose the remote sensing image compression scheme (SBC) based on integer
wavelet transformation
Since high-resolution remote sensing compression possesses the special character that the other type image compression have not, the design of compression scheme is restricted within the following limits: a) the swath of high-resolution push-broom optional CCD is beyond 20,000 pixels; b) the compression scheme complexity is as
possible as low so as to be realized in the way of hardware; c) the on-board memory is limited; d) the code rate of data transmitting is fixed; e) the compression efficiency is much higher than that based on DCT. Integer wavelet transformation provides an ideal tool for meeting the above limits. Referencing the other wavelet coding scheme, this paper presents Sub-band Bit-plane Coding (SBC), which adopt integer wavelet transformation, most priority Bit-plane Coding and related context entropy coding, and be applied to realize the fast compression coding of high-resolution image. SBC supports embedded coding enabling arbitrary Rate-distortion control. Contrasted with the other compression approaches, SBC is low complexity, efficient, easy to transplant to hardware, and can parallel computer. In low code rate, the reconstructed image quality of SBC is lightly worse than that of EBCOT, but in high code rate, the result is reverse.
2) Propose error-resilient image compression coding scheme based on virtual partition .
Wireless communication system holds relatively high rate of error code, so error code rate of satellite communication system rise greatly under the condition of intensive electromagnetism interruption. On the basis of SBC, we propose a simple and efficient error-resilient imag
引文
[1] 阿尔贝茨．克赖林，摄影测量袖珍手册，解放军出版社，1987
[2] 鲍秀芝，航测概论，武汉测绘科技大学摄影测量教研室内部教材，1991
[3] 常迥，信息理论基础，北京：清华大学出版社，1993
[4] 承继成，林珲，周成虎，曾杉，数字地球导论，北京：科学出版社，2000
[5] 程正兴，小波算法与应用，西安交通大学出版社，1998
[6] [德]H.P.贝尔，数字图像处理及其在摄影测量与遥感中的应用，解放军出版社，1990
[7] 耿则勋．影像压缩几何畸变度量方法的改进，感光科学与光化学，17(1)，1999
[8] 韩心志，航天遥感CCD推帚式成象系统，哈尔滨工业大学出版社，1990
[9] 李琦，吴少岩，数字地球，北京大学出版社，1999
[10] 李德仁，郑肇葆，解析摄影测量学，测绘出版社，1992
[11] 宁津生，陈军，晁定波，数字地球与测绘，北京：清华大学出版社，2001
[12] 秦前清，杨宗凯，实用小波分析，西安电子科技大学出版社，1994
[13] 钱曾波，耿则勋，一种评价图像压缩对量测性能影响的可靠方法，中国图形图像学报，1(3)，1996
[14] [苏]B.B.杜宾诺夫斯基，像片校准，测绘出版社，1987
[15] [苏]A.H.洛班诺夫，航空摄影地形测量学，测绘出版社，1983
[16] 沈兰荪，图像编码与异步传输，人民邮电出版社，1998
[17] 沈振元，聂志泉，赵雪荷，通讯系统原理，西安电子科技大学出版社，1993
[18] 沈未名，朱立，李国宽，错误多发异质网络环境下视频编码系统的总体设计，武汉大学学报信息科学版，Vol．27，No．1，Feb，2002
[19] 沈芸，江潮，孙洪等，视频通讯系统中的错误隐藏技术，武汉大学学报(理学版)，Vol．48 No．1，Feb．2002，103～106
[20] [美]唐纳德·L·莱特主编，林美德译，卫星摄影测量，北京：测绘出版社，1985
[21] [美]T.M.利勒桑德，R.W.基弗，遥感与图像判读，高等教育出版社，1986
[22] [英]C.D.伯恩萨德，航空摄影测量学，测绘出版社，1983
[23] 王任享，利用卫星三线阵CCD影像进行光束法平差的数字模拟实验研究，武汉测绘科技大学学报，Vol．23，No．4，1998年12月．
[24] 吴杰，H.263中的几种容错编码模式，现代有线传输，2002年3月第1期
[25] 吴伯修，归少升，祝宗泰，俞槐铨，信息论与编码，北京：电子工业出版社，1987
[26] 魏政刚，图象质量评价方法的历史、现状和未来，中国图形图像学报，1998
[27] 王昱，数字遥感影像构像质量评价方法初探，遥感信息，2000
[28] 宣家斌，航空与航天摄影技术，测绘出版社，1992
[29] 宣家斌等．遥感图像准无损压缩技术的研究[J]．武汉测绘科技大学学报，24(4)，1999
[30] 杨长生，图象与声音压缩技术，浙江：浙江大学出版社，1999
[31] 杨福生，小波变换的工程分析与应用，科学出版社，1999
[32] 颜昌翔，于平，王家骐，一种特殊图像数据压缩存储及差错控制方法，光学精密工程，
Vol．9，No．1，Feb．，2001
[33] 叶勤，陈鹰，图像压缩对影像匹配精度影响的研究，遥感信息，2001
[34] 袁修孝，李志林，林伟强，JPEG压缩对摄影测量点定位精度的影响[J]．遥感学报，5(3)，2001
[35] 钟玉琢，乔秉新，祁卫，运动图像及其伴音通用编码国际标准，清华大学出版社，1997
[36] 总装备部卫星有效载荷及应用技术专业组，卫星应用现状与发展，中国科学技术出版社，2001
[37] 张祖勋等，数字摄影测量学[M]，武汉测绘科技大学出版社，1996
[38] 张永生，张云彬，航天遥感工程，科学出版社，2001
[39] 张海翔，陈纯，庄越挺，基于零块编码的小波图像多表达容错压缩方法，通信学报，Vol．23 No．9，2002
[40] 张宗平，刘贵忠，基于小波的视频图像压缩研究进展，电子学报，Vol．30，No．6，2002
[41] 曾勇，廖明生，张剑清等．保持匹配一致性的遥感影像压缩编码．武汉测绘科技大学学报，22(3)，1997
[42] 周建鹏等，一种图像质量的感知测量方法，中国图像图形学报，3(3)：200～204，1998
[43] Ahmed, N.. Natarajan, T., and Rao, K. R., Discrete Cosine Transform, IEEE Trans. Computers, vol. C-23, Jan. 1974, pp. 90-93
[44] Allan G.Hanbury, Jean Serra, Morphological Operators on the Unit Circle, IEEE Transactions on Image Processing,Vol. 10, No. 12, 2001
[45] Andrew J.Penrose, Neil A.Dodgson, Error resilient lossless image coding, IEEE,1999
[46] Andrew Perkis, Daniel Conzalo cardelo, Transmission of still image over noisy channels,ISSPA,99
[47] Andy C. Hung, Ely K. Tsern, Teresa H. Meng, Error-Resilient Pyramid Vector Quantization for Image Compression,IEEE, Transactions on image processing, VOL.7, NO. 10, 1998
[48] Angela Altmaier, Christoph Kany, Digital surface model generation from CORONA satellite images, ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 221-235,2002
[49] Anko B(?)rner, Ralf Reulke, Martin Scheele, Thomas Terzibaschian, Stereo processing of image data from an airborne three-line CCD scanner, Third Interational Airborne Remote Sensing Conference and Exhibition, Vol.Ⅰ, p. 423
[50] Armin Gruen, Zhang Li, Xinhua Wang, 3D City Modeling with TLS (Three-Line Scanner) Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. ⅩⅩⅩⅣ-5/W10
[51] Armin Gruen, Zhang Li, Automatic DTM Generation from Three-Line-Scanner (TLS) Images.
[52] Armin Gruen, Zhang Li, Sensor Modeling for Aerial Triangulation with Three Line Scanner (TLS) Imagery.
[53] Armin Gruen, Zhang Li, Sensor Modeling for Aerial Mobile Mapping with Three Line
Scanner(TLS) Imagery.
[54] A.R. Calderbank, I. Daubechies, W. Sweldens, Wavelet transforms that map integers to integers, Applied and Computational Harmonic Analysis, vol. 5, no. 3, pp. 332-369, July 1998.
[55] Azriel Rosenfeld, Image Analysis and Computer Vision: 1998, Computer Vision and Image Understanding,Vol. 74, No. 1, April, pp. 36-95, 1999, http://www.idealibrary.com
[56] Azriel Rosenfeld, Image Analysis and Computer Vision: 1999, Computer Vision and Image Understanding 78, 222-302 (2000), http://www.idealibrary.com
[57] A. Puri, R. V. Kollarits and B. G. Haskell, Stereoscopic Video Compression Using Temporal Scalability, SPIE Vol.2501 pp 745-756, 1995.
[58] B.Tao ,M.Orchard, Window design for overlapped block motion compensation through statistical motion modeling, in Proc.31st Asilomar Conf.Signals,Systems,Computers,1997.
[59] Bo Tao, Michael T.Orchard, A Parametric Solution for Optimal Overlapped Block, IEEE Transaction Image Processing,Vol.10, No.3, 2001
[60] Buccigrossi, R., and Simoncelli, E. P. EPWIC: Embedded Predictive Wavelet Image Coder, GRASP Laboratory, TR#414, http://www.cis.upenn.edu/～butch/EPWIC/index.html
[61] Byung Cheol Song, Jong Beom Ra, A hierarchical block matching algorithm using partial distortion criteria, SPIE3309 VCIP Visual Communications and Image processing, 1998, San Jose, CA, pp88-95
[62] Calderbank, R. C., Daubechies, I., Sweldens, W., and Yeo, B. L. Wavelet Transforms that Map Integers to Integers, Applied and Computational Harmonic Analysis (ACHA), vol. 5, no. 3, pp. 332-369, 1998, http://cm.bell-labs.com/who/wim/papers/integer.pdf
[63] Campbell, J.B., Introduction to Remote Sensing Taylor & Francis, London,1996
[64] C.K. Chui. An Introduction to Wavelets. Academic Press, San Diego, CA, 1992
[65] C.K. Chui, J. Z. Wang, A cardinal spline approach to wavelets, Proc. Amer. Math. Soc., 113:785-793,1991
[66] Cohen, A., Daubechies, I., and Feauveau, J. C. Biorthogonal Bases of Compactly Supported Wavelets, Comm. on Pure and Applied Mathematics, 1992, vol. ⅩLV, pp. 485-560
[67] Coifman, R. R. and Wickerhauser, M. V. Entropy Based Algorithms for Best Basis Selection, IEEE Trans. Information Theory, vol. 38, no. 2, Mar. 1992, pp. 713-718
[68] CCSDS Report: LOSSLESS DATA COMPRESSION, http://www.ccsds.org/ccsds/
[69] Daniela Poli, General model for airborne and spaceborne linear array sensors, Proceedings of ISPRS Commission I Symposium "Integrating Remote Sensing at the Global, Regional and Local Scale". Denver, CO (USA), 10-15 November 2002. Volume 34,Part B1, pp177-182.
[70] D.Craievich, B.Barnett, A.C.Bovik, A stereo visual pattern image coding system, Image and Vision Computing,18,21-37,1999
[71] D.Nister, C. Christopoulos, Lossless region of interest with a naturally progressive still
image coding algorithm, in Proc. of IEEE International Conference on Image Processing, Chicago, IL, USA, Oct. 1998, vol. 3, pp. 856-860.
[72] Estaban, D. and Galand, C. Application of Quadrature Mirror Filters to Split Band Voice Coding Schemes, Proc. ICASSP, May 1977, pp. 191-195
[73] E. Atsumi, N. Farvardin, Lossy/lossless region-of-interest image coding based on set partitioning in hierarchical trees, in Proc. of IEEE International Conference on Image Processing, Chicago, IL, USA, Oct. 1998, vol. 1, pp. 87-91.
[74] E. LePenncc, S.Mallat, Image compression with geometrical wavelets, in Proc. ICIP, Vancouver, pp.661-664, Sept.2000.
[75] Froment, J. and Mallat, S. Second Generation Compact Image Coding with Wavelets, in C.K. Chui, editor, Wavelets: A Tutorial in Theory and Applications, vol. 2, Academic Press, NY, 1992
[76] Faouzi Kossentini, Yuen-Wen Lee, Mark J. T. Smith, Rabab K. Ward, Predictive RD Optimized Motion Estimation for Very Low Bit-Rate Video Coding, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 15, NO. 9, DECEMBER 1997
[77] F. JHampson, J.-C.Pesquet, M-band nonlinear sub-band decompositions with perfect reconstruction, IEEE Trans. Image Processing, vol.7,pp. 1547-1560,Nov. 1998.
[78] F. Sheng, A. Bilgin, P. J. Sementilli, M. W. Marcellin, Lossy and lossless image compression using reversible integer wavelet transforms, in Proc. of IEEE International Conference on Image Processing, Chicago, IL, USA, vol. 3, 1998
[79] G. NEUKUM, J. OBERST, The Multiple Line Scanner Camera Experiment for the Russian Mars 96 Mission: Status Report and Prospects for the Future.
[80] Gersho, A. and Gray, R. M. Vector Quantization and Signal Compression, Kluwer Academic Publishers, 1991
[81] Gemma Piella, HenkJ.A.M.Heijmans, Adaptive Lifting Schemes With Perfect Reconstruction, IEEE Transactions on signal processing,Vol.50,No.7,2002
[82] Gemma Piella, B(?)atrice Pesquet-Popescu, HenkHeijmans, Adaptive Update Lifting With a Decision Rule Based on Derivative Filters, IEEE signal processing letters, Vol.9, No.10, 2002
[83] G.W. Drost, N.G.Bourbakis, A hybrid system for real-time lossless image compression, microprocessors and Microsystems 25 19-31,2001.
[84] Hilton, M. L., Jawerth, B. D., and Sengupta, A. Compressing Still and Moving Images with Wavelets, Multimedia Systems, vol. 2 no.3, April, 1994, ftp://ftp.math scarolina.edu/pub/wavelet/papers/varia/tutorial/tutorial.ps.Z
[85] Hongyang Chao, An Approach to Fast Integer Reversible Wavelet Transforms for Image Compression
[86] H. Chao, P. Fisher, and Z. Hua, An approach to integer wavelet transforms for lossless for image compression, in Proc. Of International Symposium on Computational Mathematics,
Guangzhou, China, Aug. pp. 19-38.1997
[87] H.J.A.M.Heijmans, B.Pesquet-Popescu, G.Piella, Building nonredundant adaptive wavelets by update lifting, CWI, Amsterdam, The Netherlands,Res.Rep.PNA-R0212,2002.
[88] H.J.A.M.Heijmansand, J.Goutsias, Nonlinear multi-resolution signal decomposition schemes. PartⅡ: Morphological wavelets, IEEE Trans. Image Processing, vol.9, pp. 1897-1913, Nov.2000.
[89] H.J.A.M. Heijmans, R. van den Boomgaard, Algebraic framework for linear and morphological scale-spaces, Centrum voor Wiskunde en Informatica, PNA-R0003 February 29, 2000
[90] H.J.A.M. Heijmans, J. Goutsias, Multiresolution signal decomposition schemes. Part 2: Morphological wavelets, Centrum voor Wiskunde en Informatica, PNA-R9905 June 30, 1999
[91] H.J.A.M. Heijmans, P. Maragos, Lattice calculus of the morphological slope transform, Centrum voor Wiskunde en Informatica report, BS-R9531 1995
[92] Henk J.A.M. Heijmans, Composing morphological filters, Centrum voor Wiskunde en Informatica report, BS-R9504 1995
[93] H.J.A.M. Heijmans, On the construction of morphological operators which reselfdual and activity-extensive, Centrum voor Wiskunde en Informatica report, B S-R9307 1993
[94] ISO/IEC/JTC1/SC29/WG1 N390R, JPEG 2000 Image Coding System, Mar. 1997, http://www.jpeg.org/public/wg1n505.pdf
[95] I.Daubechies, Orthonormal Bases of Compactly Supported Wavelets, Comm. Pure and Applied Math., vol. 41, Nov. 1988, pp. 909-996
[96] I.Daubechies, Ten Lectures on Wavelets. CBMS-NSF Regional Conf, Series in Appl. Math, 1992
[97] I.Daubechies, W. Sweldens, Factoring wavelet transforms into lifting steps, Technical report, Bell Laboratories,Lucent Technologies, 1996
[98] Iole Moccagatta, Salma Soudagar, Jie Liang, Homer Chen, Error-Resilient Coding in JPEG-2000 and MPEG-4, IEEE Journal on selected areas in communications, Vol.18, No.6, 2000
[99] Jaakkola,J, E.Orava, The Effect of Pixel Size and Compression on Metric Quality of Digital Aerial Images, International Archives of Photogrammetric & Remote sensing, Munich, 30(3/1), pp, 409-415,1994
[100] Jens-Rainer Ohm, An Object-Based System for Stereoscopic Viewpoint Synthesis, IEEE Transaction on Circuit and Systems for Video Technology, Vol. 7, No.5, Oct 1997.
[101] J. D. Villasenor, B. Belzer, J. Liao, Wavelet filter evaluation for image compression, IEEE Trans. on Image Processing, vol. 4, no. 8, pp.1053-1060, Aug. 1995.
[102] Jorg Kliewer, Norbert Gortz, Error-Resilient transmission of compressed image over very
noisy channels using soft-input source decoding,IEEE,2000
[103] JonathanK.Su, Russell M.Mersereau, Motion Estimation Methods for Overlapped Block Motion Compensation, IEEE Transaction image processing,Vol.9, No.9,2000
[104] J. Serra, Image Analysis and Mathematical Morphology, Academic Press, New York, 1982.
[105] Jizheng Xu, Zixiang Xiong, and Shipeng Li, High performance wavelet-based stereo image coding.
[106] Jie-Bin Xu, Lai-man Po Chok-Kwan Cheung, A new prediction model search algorithm for fast block motion estimation, IEEE Int. Conf. Image Processing, ICIP97, Santa Barbara, 1997
[107] J. Goutsias, H.J.A.M. Heijmans, Multiresolution signal decomposition schemes. Part 1: Linear and morphological pyramids, Centrum voor Wiskunde en Informatica, PNA-R9810 October 31, 1998
[108] J. Goutsias et al., Mathemtical Morphology and its applications to image and signal processing, Kluwer Academic Publishers, 2000.
[109] J. Song and E. J. Delp, The analysis of morphological filters with multiple structuring elements, Computer Vision, Graphics, and Image Processing, Vol. 50, pp. 308-328,1990
[110] J. Xu, Z. Xiong, S. Li and Y.-Q. Zhang, Three- dimensional embedded subband coding with optimized truncation (3-D ESCOT), J. Appl. Comput. Harmon. Anal., vol. 10, pp. 290-315, May 2001.
[111] Karsten Jacobsen, Calibration of IRS-1C PAN-camera.
[112] Karsten Jacobsen, Status and tendency of sensors for mapping.
[113] K.S.Thyagarajan,G.bendak, A strategy for satellite data archival low noise variable_rate vector quantization with application to AVHRR satellite images: A tutorial review, signal processing: Image communication, 14,245-267,1999
[114] Kui Zhang, Miroslaw Bober, JosefKittler, Image Sequence Coding Using Multiple-Level Segmentation and Affine Motion Estimation, IEEE Journal on selected areas in communications,Vol. 15, No.9, 1997
[115] Lewis, A. S. and Knowles, G. Image Compression Using the 2-D Wavelet Transform, IEEE Trans. IP, vol. 1, no. 2, April 1992, pp. 244-250
[116] Luca Lucchese, A New Method for Perspective View Registration.
[117] Leachtenauer, J., Daniel, K., Vogl T., Digitizing satellite imagery: quality and cost considerations. Photogrammetric Engineering and Remote Sensing 64(1), 29-34,1998
[118] Malavar, H. S. Signal Processing with Lapped Transforms, Norwood, MA, Artech House, 1992
[119] Mc Donald, R.A., Photogrammetric Engineering and Remote Sensing 61(6), 689-720,1995
[120] Mallat, S. G. A Theory for Multiresotution Signal Decomposition: The Wavelet Representation, IEEE Trans. PAMI, vol. 11, no. 7, July 1989, pp. 674-693
[121] Maria Pateraki, Emmanuel Baltsavias, Analysis and performance of the adaptive multi-image matching algorithm for airborne digital sensor ADS40.
[122] Michael D. Adams, Faouzi Kossentini, Reversible Integer-to-Integer Wavelet Transforms for Image Compression: Performance Evaluation and Analysis.
[123] Mikhail, E.M., Bethel, J.S., Introduction to Modern Photogrammetry Wiley, New York, Mc Glone, J.C., 2001.
[124] Milan Sonka, Vaclav Hlavac,Roger Boyle, Image processing, Analysis, and Machine Vision, 人民邮电出版社, 2002
[125] M. D. Adams, Reversible wavelet transforms and their application to embedded image compression, M.A.Sc. thesis, Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada, Jan. 1998, Available from http://www.ece.ubc.ca/~mdadams.
[126] M. D. Adams and F. Kossentini, Performance evaluation of several reversible integer-to-integer wavelet transforms in the JPEG-2000 baseline system (VM release 0.0), ISO/IEC JTC 1/SC 29/WG 1 N866, June 1998, http://www.ece.ubc.ca/~mdadams.
[127] M.J.Ryan,J.F.Arnold, A suitable distortion measure for the lossy compression.IEEE, 0-7803-4403-0, 1998
[128] M.R.Pickering,M.R.Frator, J.F.Arnold, A statistical error detection technique for low bit-rate video.IEEE,speech and Image technologies for computing and telecommunications, 1997
[129] M. S. Moellenhoff, M. W. Maier, Transform coding of stereo image residues, IEEE Trans. Image Processing, vol. 7, pp. 804-812, June 1998.
[130] Min Wu, Motion Estimation (Part -2) and Hybrid Video Coding Based on Block -M.E.,http://www.ece.umd.edu/class/enee631/
[131] Nelson, M. The Data Compression Book,2nd ed., M&T books, Nov. 1995, http://www1.fatbrain.com/asp/bookinfo/bookinfo.asp?theisbn=1558514341
[132] Novak K, Shah, A Comparison of Two Image Compression Techniques for Softcopy Photogrammetry[J], Photogrammetric Engineering &Remote Sensing,62(6),1996
[133] NRO (Ed.), http://www.nro.gov/corona(accessed January)2002.
[134] (?).N. Gerek, A.E.Cetin, Adaptive poly-phase sub-band decomposition structures for image compression, IEEE Trans. Image Processing, vol.9, pp.1649-1659, Oct. 2000.
[135] Pascal Chesnais, Wendy Plesniak, Color coding stereo pairs for non-interlaced displays.
[136] Pennebaker, W. B. and Mitchell, J. L. JPEG - Still Image Data Compression Standards, Van Nostrand Reinhold, 1993
[137] Pen-shu Yeh,Jack Venbrux,A real-time high performance data compression technique for space applications,0-7803-6359-0,IEEE,2000
[138] Pen-Shu Yeh, Implementation of CCSDS Lossless data compression for space and data archive applications, NASA/Goddard Space Flight Center.
[139] P.Fricker, R. Sandau, A.S. Walker, Airborne digital sensors-a new approach.
[140] Philippe Burlina, Fady Alajaji, An Error Resilient Scheme for Image Transmission over Noisy channels with Memory, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 7, NO. 4, APRIL 1998
[141] Q. Jiang, J. J. Lee and M. H. Hayes, A wavelet based stereo image coding algorithm, Proc. ICASSP'99, pp.3137-3160, 1999
[142] Qi Wei, Hong-Jiang Zhang, Zhong Yuzhuo, New Robust Global Motion Estimation Approach Usedin MPEG-4.
[143] Ru-Shang Wang, Yao Wang, Non-Coplanar Multiview Sequence Compression.
[144] R. Wang, Y. Wang, Stereo Sequence Analysis, Compression, and Virtual Viewpoint Synthesis, IEEE Signal Processing Society 1998 Workshop on Multimedia Signal Processing December 7-9, 1998, Los Angeles, California, USA Electronic Proceedings. pp. 492-497.
[145] Rao, K. R. and Yip, E Discrete Cosine Transforms-Algorithms, Advantages, Applications, Academic Press, 1990
[146] Renato kresch, PhD dissertation:Morphological Image Reprentation for coding applications. 1996
[147] R.L.Claypoole, G.Davis, W.Sweldens, R.D.Baraniuk, Nonlinear wavelet transforms for image coding, in Proc.31st Asilomar Conf. Signals, Systems, and Computers, vol.1, pp.662-667.1997
[148] Saha, S. and Vemuri, R. Adaptive Wavelet Coding of Multimedia Images, Proc. ACM Multimedia Conference, Nov. 1999, to appear
[149] Said, A. and Pearlman, W. A. A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees, IEEE Trans. CSVT, vol. 6, no. 3, June 1996, pp. 243-250, ftp://ipl.rpi.edu/pub/EW_Code/SPIHT.ps.gz
[150] Shapiro, J. M. Embedded Image Coding Using Zerotrees of Wavelet Coefficients, IEEE Trans. SP, vol. 41, no. 12, Dec. 1993, pp. 3445-3462
[151] Shih-Hsuan Yang,Tsung-chao cheng, Error-resilient SPHIT image coding, Electronics Letters,Vol. 36,No. 3,2000
[152] Shan Zhu, Kai-Kuang Ma, A new diamond search algorithm for fast block matching, IEEE Transactions on Circuits and Systems on Video Technology, Vol.9, No.2, Feb.2000, pp287-290
[153] Strang, G. and Nguyen, T. Wavelets and Filter Banks, Wellesley-Cambridge Press, Wellesley, MA, 1996, http://www-math.mit.edu/~gs/books/wfb.html
[154] W.Sweldens, The lifting scheme: A new philosophy in bi-orthogonal wavelet construction, Proc. of SPIE, Wavelet Applications in Signal and Image Processing Ⅲ,1995,2569:68-79
[155] W.Sweldens, The lifting scheme: A construction of second generation wavelets, Technical Report 1995:6, Industrial Mathematics Initiative, Department of Mathematics, University of South Carolina, 1995
[156] W. Sweldens, The lifting scheme: A custom-design construction of bi-orthogonal wavelets. Appl. Comput. Harmon. Anal., 3(2):186-200, 1996
[157] W. Sweldens and P. Schroder, Building your own wavelets at home, Wavelets in Computer Graphics, ACM SIGGRAPH Course notes, 1996
[158] T.Collins,P.Atkins, Error-tolerent SPIHT image compression, IEE, Image signal process,Vol. 148,NO.3,2001
[159] Taubman, D., High Performance Scalable Image Compression with EBCOT, submitted to IEEE Tran. IP, Mar. 1999, http://maestro.ee.unsw.edu.au/~taubman/activities/preprints/ebcot.zip
[160] Tsai, M. J., Villasenor, J. D., and Chen, F. Stack-Run Image Coding, IEEE Trans. CSVT, vol. 6, no. 5, Oct. 1996, pp. 519-521, http://www.icsl.ucla.edu/~ipl/papers/stack_run.html
[161] Tamotsu Igarashi, ALOS Mission Requirement and sensor specifications, Adv Space Res. Voh 28, No. 1. pp. 127-131, 2001
[162] T.-Q.n Deng, H.J.A.M. Heijmans, Grey-scale Morphology Based on Fuzzy Logic, Centrum voor Wiskunde en Informatica Report, October 31, 2000
[163] Vaidyanathan, P. P. Multirate Systems and Filter Banks, Prentice Hall, Englewood Cliffs, N.J., 1993
[164] Vetterli, M.,Herley, C., Wavelets and Filter Banks: Theory and Design, IEEE Trans. SP, vol. 40, no. 9, Sep. 1992, pp. 2207-2232
[165] Vetterli, M. and Kovacevic, J. Wavelets and Subband Coding, Englewood Cliffs, NJ, Prentice Hall, 1995, http://cm.bell-labs.com/who/jelena/Book/home.html
[166] Wallace, G. K. The JPEG Still Picture Compression Standard, Comm. ACM, vol. 34, no. 4, April 1991, pp. 30-44
[167] Wang, Y., Principles and applications of structural image matching. ISPRS Journal of Photogrammetry and Remote Sensing 53(3), 154-165,1998
[168] Wang, Y., 1999. Automated triangulation of linear scanner imagery. Proc. Joint Workshop of ISPRS WG I/1, I/3 and IV/4 "Sensors and Mapping from Space 1999", Hannover, September 27-30
[169] Wang, Y., Yang, X., Stojic, M., Automatic triangulation and rectification of images from air borne and spaceborne sensors Paper presented at the 19th ISPRS Congress, Amsterdam,cJulyl 6-23, 2000. (paper erroneously not included in the proceedings),2000
[170] Woods, J. W. and O'Neil, S. D. Subband Coding of Images IEEE Trans. ASSP, vol. 34, no. 5, October 1986, pp. 1278-1288
[171] W. Woo, A. Ortega, Overlapped block disparity compensation with adaptive windows for stereo image coding, IEEE Trans. on CSVT, vol. 10, pp. 194-200, March. 2000
[172] W. Woo and A. Ortega, Optimal blockwise dependent quantization for stereo image coding, IEEE Trans. on CSVT, vol. 9, pp.861-867, September 1999.
[173] Xiong, Z., Ramachandran, K. and Orchard, M. T. Space-Frequency Quantization for
Wavelet Image Coding, IEEE Trans. IP,, vol. 6, no. 5, May 1997, pp. 677-693, http://troi.ifp.uiuc.edu/~kannan/my_papers/xiong_ramchandran_orchard_ip97.ps.gz
[174] Xiaobing Lee, Ya-Qin Zhang, A fast hierarchical Motion-Compensation Scheme for Vedio Coding using Block-feature Matching, IEEE Transactions on Circuits and Systems for Video Technology, Vol.6, No.6, Dec. 1996, pp627-635
[175] Y.Q. Shi, X. Xia, A thresholding Multiresolution Block Matching Algorithm, IEEE Transactions on Circuits and Systems on Video Technology, Vol.7, No.2, April 1997, pp437-440
[176] Yao Wang, Jorn Ostermann,Ya-qin zhang, Video processing and communications, Prentice-Hall Inc,2002
[177] Yew-San Lee, Cheng-Mou Yu, A novel Dct-based bit plane error resilient entropy coding scheme and codec for wireless image communication.
[178] Yew-San Lee, Cheng-Mou Yu, Error resilient hybrid variable length codec with tough error synchronization for wireless image communication,
[179] Yew-San Lee, Wei-shin chang, Construction of error resilient synchronization codeword for variable-length code in image transmission.
[180] Zhongli He, M.L. Liou, A high performance fast search algorithm for block matching motion estimation, IEEE Transactions on Circuits and Systems on Video Technology, Vol.7, No.5, Oct. 1997, pp826-828
[181] http://www.gsfc.nasa.gov/ccsds/ccsds_home.html
[2] 鲍秀芝，航测概论，武汉测绘科技大学摄影测量教研室内部教材，1991
[3] 常迥，信息理论基础，北京：清华大学出版社，1993
[4] 承继成，林珲，周成虎，曾杉，数字地球导论，北京：科学出版社，2000
[5] 程正兴，小波算法与应用，西安交通大学出版社，1998
[6] [德]H.P.贝尔，数字图像处理及其在摄影测量与遥感中的应用，解放军出版社，1990
[7] 耿则勋．影像压缩几何畸变度量方法的改进，感光科学与光化学，17(1)，1999
[8] 韩心志，航天遥感CCD推帚式成象系统，哈尔滨工业大学出版社，1990
[9] 李琦，吴少岩，数字地球，北京大学出版社，1999
[10] 李德仁，郑肇葆，解析摄影测量学，测绘出版社，1992
[11] 宁津生，陈军，晁定波，数字地球与测绘，北京：清华大学出版社，2001
[12] 秦前清，杨宗凯，实用小波分析，西安电子科技大学出版社，1994
[13] 钱曾波，耿则勋，一种评价图像压缩对量测性能影响的可靠方法，中国图形图像学报，1(3)，1996
[14] [苏]B.B.杜宾诺夫斯基，像片校准，测绘出版社，1987
[15] [苏]A.H.洛班诺夫，航空摄影地形测量学，测绘出版社，1983
[16] 沈兰荪，图像编码与异步传输，人民邮电出版社，1998
[17] 沈振元，聂志泉，赵雪荷，通讯系统原理，西安电子科技大学出版社，1993
[18] 沈未名，朱立，李国宽，错误多发异质网络环境下视频编码系统的总体设计，武汉大学学报信息科学版，Vol．27，No．1，Feb，2002
[19] 沈芸，江潮，孙洪等，视频通讯系统中的错误隐藏技术，武汉大学学报(理学版)，Vol．48 No．1，Feb．2002，103～106
[20] [美]唐纳德·L·莱特主编，林美德译，卫星摄影测量，北京：测绘出版社，1985
[21] [美]T.M.利勒桑德，R.W.基弗，遥感与图像判读，高等教育出版社，1986
[22] [英]C.D.伯恩萨德，航空摄影测量学，测绘出版社，1983
[23] 王任享，利用卫星三线阵CCD影像进行光束法平差的数字模拟实验研究，武汉测绘科技大学学报，Vol．23，No．4，1998年12月．
[24] 吴杰，H.263中的几种容错编码模式，现代有线传输，2002年3月第1期
[25] 吴伯修，归少升，祝宗泰，俞槐铨，信息论与编码，北京：电子工业出版社，1987
[26] 魏政刚，图象质量评价方法的历史、现状和未来，中国图形图像学报，1998
[27] 王昱，数字遥感影像构像质量评价方法初探，遥感信息，2000
[28] 宣家斌，航空与航天摄影技术，测绘出版社，1992
[29] 宣家斌等．遥感图像准无损压缩技术的研究[J]．武汉测绘科技大学学报，24(4)，1999
[30] 杨长生，图象与声音压缩技术，浙江：浙江大学出版社，1999
[31] 杨福生，小波变换的工程分析与应用，科学出版社，1999
[32] 颜昌翔，于平，王家骐，一种特殊图像数据压缩存储及差错控制方法，光学精密工程，
Vol．9，No．1，Feb．，2001
[33] 叶勤，陈鹰，图像压缩对影像匹配精度影响的研究，遥感信息，2001
[34] 袁修孝，李志林，林伟强，JPEG压缩对摄影测量点定位精度的影响[J]．遥感学报，5(3)，2001
[35] 钟玉琢，乔秉新，祁卫，运动图像及其伴音通用编码国际标准，清华大学出版社，1997
[36] 总装备部卫星有效载荷及应用技术专业组，卫星应用现状与发展，中国科学技术出版社，2001
[37] 张祖勋等，数字摄影测量学[M]，武汉测绘科技大学出版社，1996
[38] 张永生，张云彬，航天遥感工程，科学出版社，2001
[39] 张海翔，陈纯，庄越挺，基于零块编码的小波图像多表达容错压缩方法，通信学报，Vol．23 No．9，2002
[40] 张宗平，刘贵忠，基于小波的视频图像压缩研究进展，电子学报，Vol．30，No．6，2002
[41] 曾勇，廖明生，张剑清等．保持匹配一致性的遥感影像压缩编码．武汉测绘科技大学学报，22(3)，1997
[42] 周建鹏等，一种图像质量的感知测量方法，中国图像图形学报，3(3)：200～204，1998
[43] Ahmed, N.. Natarajan, T., and Rao, K. R., Discrete Cosine Transform, IEEE Trans. Computers, vol. C-23, Jan. 1974, pp. 90-93
[44] Allan G.Hanbury, Jean Serra, Morphological Operators on the Unit Circle, IEEE Transactions on Image Processing,Vol. 10, No. 12, 2001
[45] Andrew J.Penrose, Neil A.Dodgson, Error resilient lossless image coding, IEEE,1999
[46] Andrew Perkis, Daniel Conzalo cardelo, Transmission of still image over noisy channels,ISSPA,99
[47] Andy C. Hung, Ely K. Tsern, Teresa H. Meng, Error-Resilient Pyramid Vector Quantization for Image Compression,IEEE, Transactions on image processing, VOL.7, NO. 10, 1998
[48] Angela Altmaier, Christoph Kany, Digital surface model generation from CORONA satellite images, ISPRS Journal of Photogrammetry & Remote Sensing 56 (2002) 221-235,2002
[49] Anko B(?)rner, Ralf Reulke, Martin Scheele, Thomas Terzibaschian, Stereo processing of image data from an airborne three-line CCD scanner, Third Interational Airborne Remote Sensing Conference and Exhibition, Vol.Ⅰ, p. 423
[50] Armin Gruen, Zhang Li, Xinhua Wang, 3D City Modeling with TLS (Three-Line Scanner) Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. ⅩⅩⅩⅣ-5/W10
[51] Armin Gruen, Zhang Li, Automatic DTM Generation from Three-Line-Scanner (TLS) Images.
[52] Armin Gruen, Zhang Li, Sensor Modeling for Aerial Triangulation with Three Line Scanner (TLS) Imagery.
[53] Armin Gruen, Zhang Li, Sensor Modeling for Aerial Mobile Mapping with Three Line
Scanner(TLS) Imagery.
[54] A.R. Calderbank, I. Daubechies, W. Sweldens, Wavelet transforms that map integers to integers, Applied and Computational Harmonic Analysis, vol. 5, no. 3, pp. 332-369, July 1998.
[55] Azriel Rosenfeld, Image Analysis and Computer Vision: 1998, Computer Vision and Image Understanding,Vol. 74, No. 1, April, pp. 36-95, 1999, http://www.idealibrary.com
[56] Azriel Rosenfeld, Image Analysis and Computer Vision: 1999, Computer Vision and Image Understanding 78, 222-302 (2000), http://www.idealibrary.com
[57] A. Puri, R. V. Kollarits and B. G. Haskell, Stereoscopic Video Compression Using Temporal Scalability, SPIE Vol.2501 pp 745-756, 1995.
[58] B.Tao ,M.Orchard, Window design for overlapped block motion compensation through statistical motion modeling, in Proc.31st Asilomar Conf.Signals,Systems,Computers,1997.
[59] Bo Tao, Michael T.Orchard, A Parametric Solution for Optimal Overlapped Block, IEEE Transaction Image Processing,Vol.10, No.3, 2001
[60] Buccigrossi, R., and Simoncelli, E. P. EPWIC: Embedded Predictive Wavelet Image Coder, GRASP Laboratory, TR#414, http://www.cis.upenn.edu/～butch/EPWIC/index.html
[61] Byung Cheol Song, Jong Beom Ra, A hierarchical block matching algorithm using partial distortion criteria, SPIE3309 VCIP Visual Communications and Image processing, 1998, San Jose, CA, pp88-95
[62] Calderbank, R. C., Daubechies, I., Sweldens, W., and Yeo, B. L. Wavelet Transforms that Map Integers to Integers, Applied and Computational Harmonic Analysis (ACHA), vol. 5, no. 3, pp. 332-369, 1998, http://cm.bell-labs.com/who/wim/papers/integer.pdf
[63] Campbell, J.B., Introduction to Remote Sensing Taylor & Francis, London,1996
[64] C.K. Chui. An Introduction to Wavelets. Academic Press, San Diego, CA, 1992
[65] C.K. Chui, J. Z. Wang, A cardinal spline approach to wavelets, Proc. Amer. Math. Soc., 113:785-793,1991
[66] Cohen, A., Daubechies, I., and Feauveau, J. C. Biorthogonal Bases of Compactly Supported Wavelets, Comm. on Pure and Applied Mathematics, 1992, vol. ⅩLV, pp. 485-560
[67] Coifman, R. R. and Wickerhauser, M. V. Entropy Based Algorithms for Best Basis Selection, IEEE Trans. Information Theory, vol. 38, no. 2, Mar. 1992, pp. 713-718
[68] CCSDS Report: LOSSLESS DATA COMPRESSION, http://www.ccsds.org/ccsds/
[69] Daniela Poli, General model for airborne and spaceborne linear array sensors, Proceedings of ISPRS Commission I Symposium "Integrating Remote Sensing at the Global, Regional and Local Scale". Denver, CO (USA), 10-15 November 2002. Volume 34,Part B1, pp177-182.
[70] D.Craievich, B.Barnett, A.C.Bovik, A stereo visual pattern image coding system, Image and Vision Computing,18,21-37,1999
[71] D.Nister, C. Christopoulos, Lossless region of interest with a naturally progressive still
image coding algorithm, in Proc. of IEEE International Conference on Image Processing, Chicago, IL, USA, Oct. 1998, vol. 3, pp. 856-860.
[72] Estaban, D. and Galand, C. Application of Quadrature Mirror Filters to Split Band Voice Coding Schemes, Proc. ICASSP, May 1977, pp. 191-195
[73] E. Atsumi, N. Farvardin, Lossy/lossless region-of-interest image coding based on set partitioning in hierarchical trees, in Proc. of IEEE International Conference on Image Processing, Chicago, IL, USA, Oct. 1998, vol. 1, pp. 87-91.
[74] E. LePenncc, S.Mallat, Image compression with geometrical wavelets, in Proc. ICIP, Vancouver, pp.661-664, Sept.2000.
[75] Froment, J. and Mallat, S. Second Generation Compact Image Coding with Wavelets, in C.K. Chui, editor, Wavelets: A Tutorial in Theory and Applications, vol. 2, Academic Press, NY, 1992
[76] Faouzi Kossentini, Yuen-Wen Lee, Mark J. T. Smith, Rabab K. Ward, Predictive RD Optimized Motion Estimation for Very Low Bit-Rate Video Coding, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 15, NO. 9, DECEMBER 1997
[77] F. JHampson, J.-C.Pesquet, M-band nonlinear sub-band decompositions with perfect reconstruction, IEEE Trans. Image Processing, vol.7,pp. 1547-1560,Nov. 1998.
[78] F. Sheng, A. Bilgin, P. J. Sementilli, M. W. Marcellin, Lossy and lossless image compression using reversible integer wavelet transforms, in Proc. of IEEE International Conference on Image Processing, Chicago, IL, USA, vol. 3, 1998
[79] G. NEUKUM, J. OBERST, The Multiple Line Scanner Camera Experiment for the Russian Mars 96 Mission: Status Report and Prospects for the Future.
[80] Gersho, A. and Gray, R. M. Vector Quantization and Signal Compression, Kluwer Academic Publishers, 1991
[81] Gemma Piella, HenkJ.A.M.Heijmans, Adaptive Lifting Schemes With Perfect Reconstruction, IEEE Transactions on signal processing,Vol.50,No.7,2002
[82] Gemma Piella, B(?)atrice Pesquet-Popescu, HenkHeijmans, Adaptive Update Lifting With a Decision Rule Based on Derivative Filters, IEEE signal processing letters, Vol.9, No.10, 2002
[83] G.W. Drost, N.G.Bourbakis, A hybrid system for real-time lossless image compression, microprocessors and Microsystems 25 19-31,2001.
[84] Hilton, M. L., Jawerth, B. D., and Sengupta, A. Compressing Still and Moving Images with Wavelets, Multimedia Systems, vol. 2 no.3, April, 1994, ftp://ftp.math scarolina.edu/pub/wavelet/papers/varia/tutorial/tutorial.ps.Z
[85] Hongyang Chao, An Approach to Fast Integer Reversible Wavelet Transforms for Image Compression
[86] H. Chao, P. Fisher, and Z. Hua, An approach to integer wavelet transforms for lossless for image compression, in Proc. Of International Symposium on Computational Mathematics,
Guangzhou, China, Aug. pp. 19-38.1997
[87] H.J.A.M.Heijmans, B.Pesquet-Popescu, G.Piella, Building nonredundant adaptive wavelets by update lifting, CWI, Amsterdam, The Netherlands,Res.Rep.PNA-R0212,2002.
[88] H.J.A.M.Heijmansand, J.Goutsias, Nonlinear multi-resolution signal decomposition schemes. PartⅡ: Morphological wavelets, IEEE Trans. Image Processing, vol.9, pp. 1897-1913, Nov.2000.
[89] H.J.A.M. Heijmans, R. van den Boomgaard, Algebraic framework for linear and morphological scale-spaces, Centrum voor Wiskunde en Informatica, PNA-R0003 February 29, 2000
[90] H.J.A.M. Heijmans, J. Goutsias, Multiresolution signal decomposition schemes. Part 2: Morphological wavelets, Centrum voor Wiskunde en Informatica, PNA-R9905 June 30, 1999
[91] H.J.A.M. Heijmans, P. Maragos, Lattice calculus of the morphological slope transform, Centrum voor Wiskunde en Informatica report, BS-R9531 1995
[92] Henk J.A.M. Heijmans, Composing morphological filters, Centrum voor Wiskunde en Informatica report, BS-R9504 1995
[93] H.J.A.M. Heijmans, On the construction of morphological operators which reselfdual and activity-extensive, Centrum voor Wiskunde en Informatica report, B S-R9307 1993
[94] ISO/IEC/JTC1/SC29/WG1 N390R, JPEG 2000 Image Coding System, Mar. 1997, http://www.jpeg.org/public/wg1n505.pdf
[95] I.Daubechies, Orthonormal Bases of Compactly Supported Wavelets, Comm. Pure and Applied Math., vol. 41, Nov. 1988, pp. 909-996
[96] I.Daubechies, Ten Lectures on Wavelets. CBMS-NSF Regional Conf, Series in Appl. Math, 1992
[97] I.Daubechies, W. Sweldens, Factoring wavelet transforms into lifting steps, Technical report, Bell Laboratories,Lucent Technologies, 1996
[98] Iole Moccagatta, Salma Soudagar, Jie Liang, Homer Chen, Error-Resilient Coding in JPEG-2000 and MPEG-4, IEEE Journal on selected areas in communications, Vol.18, No.6, 2000
[99] Jaakkola,J, E.Orava, The Effect of Pixel Size and Compression on Metric Quality of Digital Aerial Images, International Archives of Photogrammetric & Remote sensing, Munich, 30(3/1), pp, 409-415,1994
[100] Jens-Rainer Ohm, An Object-Based System for Stereoscopic Viewpoint Synthesis, IEEE Transaction on Circuit and Systems for Video Technology, Vol. 7, No.5, Oct 1997.
[101] J. D. Villasenor, B. Belzer, J. Liao, Wavelet filter evaluation for image compression, IEEE Trans. on Image Processing, vol. 4, no. 8, pp.1053-1060, Aug. 1995.
[102] Jorg Kliewer, Norbert Gortz, Error-Resilient transmission of compressed image over very
noisy channels using soft-input source decoding,IEEE,2000
[103] JonathanK.Su, Russell M.Mersereau, Motion Estimation Methods for Overlapped Block Motion Compensation, IEEE Transaction image processing,Vol.9, No.9,2000
[104] J. Serra, Image Analysis and Mathematical Morphology, Academic Press, New York, 1982.
[105] Jizheng Xu, Zixiang Xiong, and Shipeng Li, High performance wavelet-based stereo image coding.
[106] Jie-Bin Xu, Lai-man Po Chok-Kwan Cheung, A new prediction model search algorithm for fast block motion estimation, IEEE Int. Conf. Image Processing, ICIP97, Santa Barbara, 1997
[107] J. Goutsias, H.J.A.M. Heijmans, Multiresolution signal decomposition schemes. Part 1: Linear and morphological pyramids, Centrum voor Wiskunde en Informatica, PNA-R9810 October 31, 1998
[108] J. Goutsias et al., Mathemtical Morphology and its applications to image and signal processing, Kluwer Academic Publishers, 2000.
[109] J. Song and E. J. Delp, The analysis of morphological filters with multiple structuring elements, Computer Vision, Graphics, and Image Processing, Vol. 50, pp. 308-328,1990
[110] J. Xu, Z. Xiong, S. Li and Y.-Q. Zhang, Three- dimensional embedded subband coding with optimized truncation (3-D ESCOT), J. Appl. Comput. Harmon. Anal., vol. 10, pp. 290-315, May 2001.
[111] Karsten Jacobsen, Calibration of IRS-1C PAN-camera.
[112] Karsten Jacobsen, Status and tendency of sensors for mapping.
[113] K.S.Thyagarajan,G.bendak, A strategy for satellite data archival low noise variable_rate vector quantization with application to AVHRR satellite images: A tutorial review, signal processing: Image communication, 14,245-267,1999
[114] Kui Zhang, Miroslaw Bober, JosefKittler, Image Sequence Coding Using Multiple-Level Segmentation and Affine Motion Estimation, IEEE Journal on selected areas in communications,Vol. 15, No.9, 1997
[115] Lewis, A. S. and Knowles, G. Image Compression Using the 2-D Wavelet Transform, IEEE Trans. IP, vol. 1, no. 2, April 1992, pp. 244-250
[116] Luca Lucchese, A New Method for Perspective View Registration.
[117] Leachtenauer, J., Daniel, K., Vogl T., Digitizing satellite imagery: quality and cost considerations. Photogrammetric Engineering and Remote Sensing 64(1), 29-34,1998
[118] Malavar, H. S. Signal Processing with Lapped Transforms, Norwood, MA, Artech House, 1992
[119] Mc Donald, R.A., Photogrammetric Engineering and Remote Sensing 61(6), 689-720,1995
[120] Mallat, S. G. A Theory for Multiresotution Signal Decomposition: The Wavelet Representation, IEEE Trans. PAMI, vol. 11, no. 7, July 1989, pp. 674-693
[121] Maria Pateraki, Emmanuel Baltsavias, Analysis and performance of the adaptive multi-image matching algorithm for airborne digital sensor ADS40.
[122] Michael D. Adams, Faouzi Kossentini, Reversible Integer-to-Integer Wavelet Transforms for Image Compression: Performance Evaluation and Analysis.
[123] Mikhail, E.M., Bethel, J.S., Introduction to Modern Photogrammetry Wiley, New York, Mc Glone, J.C., 2001.
[124] Milan Sonka, Vaclav Hlavac,Roger Boyle, Image processing, Analysis, and Machine Vision, 人民邮电出版社, 2002
[125] M. D. Adams, Reversible wavelet transforms and their application to embedded image compression, M.A.Sc. thesis, Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada, Jan. 1998, Available from http://www.ece.ubc.ca/~mdadams.
[126] M. D. Adams and F. Kossentini, Performance evaluation of several reversible integer-to-integer wavelet transforms in the JPEG-2000 baseline system (VM release 0.0), ISO/IEC JTC 1/SC 29/WG 1 N866, June 1998, http://www.ece.ubc.ca/~mdadams.
[127] M.J.Ryan,J.F.Arnold, A suitable distortion measure for the lossy compression.IEEE, 0-7803-4403-0, 1998
[128] M.R.Pickering,M.R.Frator, J.F.Arnold, A statistical error detection technique for low bit-rate video.IEEE,speech and Image technologies for computing and telecommunications, 1997
[129] M. S. Moellenhoff, M. W. Maier, Transform coding of stereo image residues, IEEE Trans. Image Processing, vol. 7, pp. 804-812, June 1998.
[130] Min Wu, Motion Estimation (Part -2) and Hybrid Video Coding Based on Block -M.E.,http://www.ece.umd.edu/class/enee631/
[131] Nelson, M. The Data Compression Book,2nd ed., M&T books, Nov. 1995, http://www1.fatbrain.com/asp/bookinfo/bookinfo.asp?theisbn=1558514341
[132] Novak K, Shah, A Comparison of Two Image Compression Techniques for Softcopy Photogrammetry[J], Photogrammetric Engineering &Remote Sensing,62(6),1996
[133] NRO (Ed.), http://www.nro.gov/corona(accessed January)2002.
[134] (?).N. Gerek, A.E.Cetin, Adaptive poly-phase sub-band decomposition structures for image compression, IEEE Trans. Image Processing, vol.9, pp.1649-1659, Oct. 2000.
[135] Pascal Chesnais, Wendy Plesniak, Color coding stereo pairs for non-interlaced displays.
[136] Pennebaker, W. B. and Mitchell, J. L. JPEG - Still Image Data Compression Standards, Van Nostrand Reinhold, 1993
[137] Pen-shu Yeh,Jack Venbrux,A real-time high performance data compression technique for space applications,0-7803-6359-0,IEEE,2000
[138] Pen-Shu Yeh, Implementation of CCSDS Lossless data compression for space and data archive applications, NASA/Goddard Space Flight Center.
[139] P.Fricker, R. Sandau, A.S. Walker, Airborne digital sensors-a new approach.
[140] Philippe Burlina, Fady Alajaji, An Error Resilient Scheme for Image Transmission over Noisy channels with Memory, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 7, NO. 4, APRIL 1998
[141] Q. Jiang, J. J. Lee and M. H. Hayes, A wavelet based stereo image coding algorithm, Proc. ICASSP'99, pp.3137-3160, 1999
[142] Qi Wei, Hong-Jiang Zhang, Zhong Yuzhuo, New Robust Global Motion Estimation Approach Usedin MPEG-4.
[143] Ru-Shang Wang, Yao Wang, Non-Coplanar Multiview Sequence Compression.
[144] R. Wang, Y. Wang, Stereo Sequence Analysis, Compression, and Virtual Viewpoint Synthesis, IEEE Signal Processing Society 1998 Workshop on Multimedia Signal Processing December 7-9, 1998, Los Angeles, California, USA Electronic Proceedings. pp. 492-497.
[145] Rao, K. R. and Yip, E Discrete Cosine Transforms-Algorithms, Advantages, Applications, Academic Press, 1990
[146] Renato kresch, PhD dissertation:Morphological Image Reprentation for coding applications. 1996
[147] R.L.Claypoole, G.Davis, W.Sweldens, R.D.Baraniuk, Nonlinear wavelet transforms for image coding, in Proc.31st Asilomar Conf. Signals, Systems, and Computers, vol.1, pp.662-667.1997
[148] Saha, S. and Vemuri, R. Adaptive Wavelet Coding of Multimedia Images, Proc. ACM Multimedia Conference, Nov. 1999, to appear
[149] Said, A. and Pearlman, W. A. A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees, IEEE Trans. CSVT, vol. 6, no. 3, June 1996, pp. 243-250, ftp://ipl.rpi.edu/pub/EW_Code/SPIHT.ps.gz
[150] Shapiro, J. M. Embedded Image Coding Using Zerotrees of Wavelet Coefficients, IEEE Trans. SP, vol. 41, no. 12, Dec. 1993, pp. 3445-3462
[151] Shih-Hsuan Yang,Tsung-chao cheng, Error-resilient SPHIT image coding, Electronics Letters,Vol. 36,No. 3,2000
[152] Shan Zhu, Kai-Kuang Ma, A new diamond search algorithm for fast block matching, IEEE Transactions on Circuits and Systems on Video Technology, Vol.9, No.2, Feb.2000, pp287-290
[153] Strang, G. and Nguyen, T. Wavelets and Filter Banks, Wellesley-Cambridge Press, Wellesley, MA, 1996, http://www-math.mit.edu/~gs/books/wfb.html
[154] W.Sweldens, The lifting scheme: A new philosophy in bi-orthogonal wavelet construction, Proc. of SPIE, Wavelet Applications in Signal and Image Processing Ⅲ,1995,2569:68-79
[155] W.Sweldens, The lifting scheme: A construction of second generation wavelets, Technical Report 1995:6, Industrial Mathematics Initiative, Department of Mathematics, University of South Carolina, 1995
[156] W. Sweldens, The lifting scheme: A custom-design construction of bi-orthogonal wavelets. Appl. Comput. Harmon. Anal., 3(2):186-200, 1996
[157] W. Sweldens and P. Schroder, Building your own wavelets at home, Wavelets in Computer Graphics, ACM SIGGRAPH Course notes, 1996
[158] T.Collins,P.Atkins, Error-tolerent SPIHT image compression, IEE, Image signal process,Vol. 148,NO.3,2001
[159] Taubman, D., High Performance Scalable Image Compression with EBCOT, submitted to IEEE Tran. IP, Mar. 1999, http://maestro.ee.unsw.edu.au/~taubman/activities/preprints/ebcot.zip
[160] Tsai, M. J., Villasenor, J. D., and Chen, F. Stack-Run Image Coding, IEEE Trans. CSVT, vol. 6, no. 5, Oct. 1996, pp. 519-521, http://www.icsl.ucla.edu/~ipl/papers/stack_run.html
[161] Tamotsu Igarashi, ALOS Mission Requirement and sensor specifications, Adv Space Res. Voh 28, No. 1. pp. 127-131, 2001
[162] T.-Q.n Deng, H.J.A.M. Heijmans, Grey-scale Morphology Based on Fuzzy Logic, Centrum voor Wiskunde en Informatica Report, October 31, 2000
[163] Vaidyanathan, P. P. Multirate Systems and Filter Banks, Prentice Hall, Englewood Cliffs, N.J., 1993
[164] Vetterli, M.,Herley, C., Wavelets and Filter Banks: Theory and Design, IEEE Trans. SP, vol. 40, no. 9, Sep. 1992, pp. 2207-2232
[165] Vetterli, M. and Kovacevic, J. Wavelets and Subband Coding, Englewood Cliffs, NJ, Prentice Hall, 1995, http://cm.bell-labs.com/who/jelena/Book/home.html
[166] Wallace, G. K. The JPEG Still Picture Compression Standard, Comm. ACM, vol. 34, no. 4, April 1991, pp. 30-44
[167] Wang, Y., Principles and applications of structural image matching. ISPRS Journal of Photogrammetry and Remote Sensing 53(3), 154-165,1998
[168] Wang, Y., 1999. Automated triangulation of linear scanner imagery. Proc. Joint Workshop of ISPRS WG I/1, I/3 and IV/4 "Sensors and Mapping from Space 1999", Hannover, September 27-30
[169] Wang, Y., Yang, X., Stojic, M., Automatic triangulation and rectification of images from air borne and spaceborne sensors Paper presented at the 19th ISPRS Congress, Amsterdam,cJulyl 6-23, 2000. (paper erroneously not included in the proceedings),2000
[170] Woods, J. W. and O'Neil, S. D. Subband Coding of Images IEEE Trans. ASSP, vol. 34, no. 5, October 1986, pp. 1278-1288
[171] W. Woo, A. Ortega, Overlapped block disparity compensation with adaptive windows for stereo image coding, IEEE Trans. on CSVT, vol. 10, pp. 194-200, March. 2000
[172] W. Woo and A. Ortega, Optimal blockwise dependent quantization for stereo image coding, IEEE Trans. on CSVT, vol. 9, pp.861-867, September 1999.
[173] Xiong, Z., Ramachandran, K. and Orchard, M. T. Space-Frequency Quantization for
Wavelet Image Coding, IEEE Trans. IP,, vol. 6, no. 5, May 1997, pp. 677-693, http://troi.ifp.uiuc.edu/~kannan/my_papers/xiong_ramchandran_orchard_ip97.ps.gz
[174] Xiaobing Lee, Ya-Qin Zhang, A fast hierarchical Motion-Compensation Scheme for Vedio Coding using Block-feature Matching, IEEE Transactions on Circuits and Systems for Video Technology, Vol.6, No.6, Dec. 1996, pp627-635
[175] Y.Q. Shi, X. Xia, A thresholding Multiresolution Block Matching Algorithm, IEEE Transactions on Circuits and Systems on Video Technology, Vol.7, No.2, April 1997, pp437-440
[176] Yao Wang, Jorn Ostermann,Ya-qin zhang, Video processing and communications, Prentice-Hall Inc,2002
[177] Yew-San Lee, Cheng-Mou Yu, A novel Dct-based bit plane error resilient entropy coding scheme and codec for wireless image communication.
[178] Yew-San Lee, Cheng-Mou Yu, Error resilient hybrid variable length codec with tough error synchronization for wireless image communication,
[179] Yew-San Lee, Wei-shin chang, Construction of error resilient synchronization codeword for variable-length code in image transmission.
[180] Zhongli He, M.L. Liou, A high performance fast search algorithm for block matching motion estimation, IEEE Transactions on Circuits and Systems on Video Technology, Vol.7, No.5, Oct. 1997, pp826-828
[181] http://www.gsfc.nasa.gov/ccsds/ccsds_home.html