基于DSP的超光谱图像压缩技术的研究及实现
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摘要
超光谱图像在军事和民用领域中的目标分类、目标识别、目标跟踪等方面具有重要的研究价值和应用意义。随着光谱成像技术的发展,光谱成像仪分辨率的提高,超光谱图像作为一种重要的数据源,其数据量日益庞大,给存储和传输都带来了巨大的压力。而到目前为止,一直没有形成一套成熟或标准的超光谱图像压缩技术。因此,对超光谱图像压缩编码的研究具有重要的应用价值。
由于超光谱图像具有细节丰富,容量大的特点,采用传统的压缩方法都存在不同的局限性,而要实现超光谱图像这样的大数据量的快速无损压缩就更难了,目前以实现近无损压缩的研究居多。本文选用以推扫形式获得的超光谱序列图作为研究对象,依据特殊的图像获取手段带来的独特的图像特征,建立了简单DPCM谱间预测模型。在处理预测后得到的残差图像时选用LeGall5/3整数小波基对其作基于提升的整数小波变换。该运算只涉及加减法和移位运算,且可以获得完全还原的小波变换系数,能满足易于硬件实现和无损压缩的要求。本文用C语言编写了超光谱图像数据读取模块、谱间预测模块、整数小波变换模块、嵌入式零树编码模块的相关程序,并利用CCS开发环境和闻亭公司的TDS642EVM开发板对所设计编写的算法进行了成功的实测。最后结合CCS集成开发环境和TDS642EVM开发板,采取编译器优化、软件流水线技术、数据打包处理、DMA技术搬移数据、改进配置和存储空间等方法对算法进行了优化,大大提高了硬件资源的利用率和系统的实用性。
实验表明使用本系统对512×512×8bit的超光谱图像进行压缩时,平均压缩比达到1.878,大于JPEG-LS算法和基于K-L变换的压缩比。解码后得到的重构图像的峰值信噪比为无穷大,满足超光谱图像无损压缩的要求。在TMS320DM642 EVM开发板上,使用本系统处理一幅512×512×8bit的超光谱图像,在算法优化前平均耗时为13.274s,优化后的平均耗时为7.563s,处理速度提高了43.44%,优化效果明显。
In the military and civil domain, hyper-spectral image,having been applied in target classification, target identification, target tracking etc, has important research value and great application significance. With the rapid development of the remote technology and the enhancement of remote sensor resolution,the hyper-spectral image , as one of the main remote sensing data, which increased a lot, brings in serious problems about transmission and storage of hyper-spectral image data. So far, one kind of mature or standard hyper-spectral image compression technology has been not farmed. Therefore, the research on remote hyper-spectral image compression coding has great application value.
Because hyper-spectral image has abundance detail and great data quantity, there always has some limits in some degree to compress the image adopting traditional compression methods,And it is much harder to achieve lossless compression about the hyper-spectral image. At present, much attention has been paid to the nearly lossless compression research. In this paper, taking the moving-scanned hyper-spectral image as the study object, Combined with the image’s unique feature caused by the special shooting method, a simple DPCM predication model is build. And choose the LeGall5/3 wavelet base to realize the integral wavelet transformation on the difference image from the model, base on integral lifting scheme The algorithm above only refer to some add and subtract and shift operations, and it can rebuild the wavelet transform coefficients perfectly. And it is easy to present on the hardware. The related program which includes image-data reading module, image-pretreatment module, spectral-prediction module, IWT module, and Embedded Zero-tree Wavelets module have been completed by using the standard C, and the very algorithm compiled for the design have passed the test by using the CCS integrated development environment and the TDS320DM642EVM development board. Finally, gathered the CCS integrated development environment and the TDS320DM642EVM development board, the algorithm has been optimized by using the compiler optimization, software assembly line technology, data-packing technology, DMA, improved disposition and storage space, etc, which have raised the utilization rate of hardware source and the usability of the system much.
The experiments shows that the average compression ratio of compress a 512×512×8bit hyper-spectral image lossless is about 1.878, by using the system to compress a 512×512×8bit hyper-spectral image. The effect is better than the JPEG-LS alogorithm and the k-l transform compression algorithm to the 2-D images. The peak signal-to-noise ratio(PSNR) of reconstruction images is infinite and satisfies the image quality requirements. Using the system algorithm to deal with a 512×512×8bit hyper-spectral images, under the condition of TMS320DM642, cost about 13.274s. When optimized, the average time is about 7.563s. The processing speed is improved by 43.44%, which the optimizing effect is obvious.
由于超光谱图像具有细节丰富,容量大的特点,采用传统的压缩方法都存在不同的局限性,而要实现超光谱图像这样的大数据量的快速无损压缩就更难了,目前以实现近无损压缩的研究居多。本文选用以推扫形式获得的超光谱序列图作为研究对象,依据特殊的图像获取手段带来的独特的图像特征,建立了简单DPCM谱间预测模型。在处理预测后得到的残差图像时选用LeGall5/3整数小波基对其作基于提升的整数小波变换。该运算只涉及加减法和移位运算,且可以获得完全还原的小波变换系数,能满足易于硬件实现和无损压缩的要求。本文用C语言编写了超光谱图像数据读取模块、谱间预测模块、整数小波变换模块、嵌入式零树编码模块的相关程序,并利用CCS开发环境和闻亭公司的TDS642EVM开发板对所设计编写的算法进行了成功的实测。最后结合CCS集成开发环境和TDS642EVM开发板,采取编译器优化、软件流水线技术、数据打包处理、DMA技术搬移数据、改进配置和存储空间等方法对算法进行了优化,大大提高了硬件资源的利用率和系统的实用性。
实验表明使用本系统对512×512×8bit的超光谱图像进行压缩时,平均压缩比达到1.878,大于JPEG-LS算法和基于K-L变换的压缩比。解码后得到的重构图像的峰值信噪比为无穷大,满足超光谱图像无损压缩的要求。在TMS320DM642 EVM开发板上,使用本系统处理一幅512×512×8bit的超光谱图像,在算法优化前平均耗时为13.274s,优化后的平均耗时为7.563s,处理速度提高了43.44%,优化效果明显。
In the military and civil domain, hyper-spectral image,having been applied in target classification, target identification, target tracking etc, has important research value and great application significance. With the rapid development of the remote technology and the enhancement of remote sensor resolution,the hyper-spectral image , as one of the main remote sensing data, which increased a lot, brings in serious problems about transmission and storage of hyper-spectral image data. So far, one kind of mature or standard hyper-spectral image compression technology has been not farmed. Therefore, the research on remote hyper-spectral image compression coding has great application value.
Because hyper-spectral image has abundance detail and great data quantity, there always has some limits in some degree to compress the image adopting traditional compression methods,And it is much harder to achieve lossless compression about the hyper-spectral image. At present, much attention has been paid to the nearly lossless compression research. In this paper, taking the moving-scanned hyper-spectral image as the study object, Combined with the image’s unique feature caused by the special shooting method, a simple DPCM predication model is build. And choose the LeGall5/3 wavelet base to realize the integral wavelet transformation on the difference image from the model, base on integral lifting scheme The algorithm above only refer to some add and subtract and shift operations, and it can rebuild the wavelet transform coefficients perfectly. And it is easy to present on the hardware. The related program which includes image-data reading module, image-pretreatment module, spectral-prediction module, IWT module, and Embedded Zero-tree Wavelets module have been completed by using the standard C, and the very algorithm compiled for the design have passed the test by using the CCS integrated development environment and the TDS320DM642EVM development board. Finally, gathered the CCS integrated development environment and the TDS320DM642EVM development board, the algorithm has been optimized by using the compiler optimization, software assembly line technology, data-packing technology, DMA, improved disposition and storage space, etc, which have raised the utilization rate of hardware source and the usability of the system much.
The experiments shows that the average compression ratio of compress a 512×512×8bit hyper-spectral image lossless is about 1.878, by using the system to compress a 512×512×8bit hyper-spectral image. The effect is better than the JPEG-LS alogorithm and the k-l transform compression algorithm to the 2-D images. The peak signal-to-noise ratio(PSNR) of reconstruction images is infinite and satisfies the image quality requirements. Using the system algorithm to deal with a 512×512×8bit hyper-spectral images, under the condition of TMS320DM642, cost about 13.274s. When optimized, the average time is about 7.563s. The processing speed is improved by 43.44%, which the optimizing effect is obvious.
引文
[1]田宝凤,孙荣春,徐抒岩.基于提升方案的多光谱遥感图像有损压缩算法.光学技术,2006,8(32):560~562
[2]顾军跃.超光谱图像的压缩算法研究[学位论文].杭州:浙江大学,2007
[3]李中峰,基于C6000 DSP的JPEG2000图像压缩技术研究,[学位论文],武汉:华中科技大学,2004,10.
[4]冈萨雷斯.数字图像处理(MATLAB版).北京:电子工业出版社,2005. 211~240
[5]赵荣椿,赵忠明等.数字图像处理导论.西安:西北工业大学出版社,2000. 133~160
[6]刘恒殊.超光谱遥感图象压缩算法的研究[学位论文].北京:中国科学院,2002
[7]全升学.卫星图象处理算法研究及其硬件实现[学位论文].北京:北京邮电大学2007
[8]王柯俨.遥感图像高效压缩编码方法研究[学位论文].西安:西安电子科技大学,2005
[9] Daubechies.I Ten Lectures on Wavelet, Capital City Press.1992.
[10]柯丽.基于小波变换的空间遥感图像实时压缩方法研究[学位论文],北京:中国科学院,2004.12
[11]王盟.基于第二代小波的图像压缩编码的研究[学位论文].天津:天津工业大学. 2005
[12]陈传波,金先级.数字图像处理.北京:机械工业出版社,2004. 72~87
[13]何小海,腾奇志.图像通信.西安:西安电子科技大学出版社,2005. 132~138
[14]张雪松,倪国强,周立伟.基于JPEG标准实时图像编码系统的研究.北京理工大学学报,1998,18(2):217~221
[15]黄少武.基于提升小波的超光谱图像压缩技术研究及DSP实现[学位论文].西安:西安科技大学,2007.
[16]张钧萍,张晔,周廷显.成像光谱技术超光谱图像分类研究现状与分析.中国空间科学技术,2001,1:52~54
[17]张晔,张钧萍.遥感超光谱图像处理技术.中国图像图形学报,2001,1(6):37~42
[18]吴冬梅等.基于谱间DPCM和整数小波变换的超光谱图像无损压缩.光子学报,2008,37(1):156~159
[19]吴冬梅,黄少武.基于DSP芯片的超光谱图像压缩技术。计算机与信息技术,2007,2,37~39
[20] [美] Kenneth R.Castleman著,朱志刚等译.数字图像处理.电子工业出版社,2002.2
[21] Thomas W F. Hyper-spectral image compression on reconfigurable platforms [MasterThesis]. Washington:University of Washington,2001
[22] Shapiro J M. Embedded image coding using zero-trees of wavelet coefficients.地址:IEEE Trans. Signal Processing,1993,41(12):3445～3462
[23] Charilaos Christopoulos,Athanassios Skodras,Touradj Ebrahimi. The JPEG2000 still image coding system:an overview. 2000,46(4):1103～1127
[24] Thomas Wilson,Curtiss Davis. Hyper-spectral Remote Sensing Technology program and the Naval Earth map Observer satellite. SPIE,2003,3437:2～10
[25]程正兴.小波分析算法与应用.西安:西安交通大学出版社,1998. 210~236
[26]乌崇德.从国外航天遥感器系统的发展现状中得到的启示.航天返回与遥感,1998,(3):18～24
[27]尹勇,欧光军,关荣锋. DSP集成开发环境CCS开发指南.北京:北京航空航天大学出版社,2003. 94~127
[28] Texas Instruments Incorporated,卞红雨等译. TMS320C6000系列DSP的CPU与外设.北京:清华大学出版社,2007.
[29] Texas Instruments Incorporated,田黎育等译. TMS320C6000系列DSP编程工具与指南.北京:清华大学出版社,2006.
[30]李方慧,王飞,何佩琨. TMS320C6000系列DSPs原理于应用.北京:电子工业出版社,2003.
[31]朱秀昌,刘峰,胡栋.数字图像处理与图像通信.北京:北京邮电大学出版社,2002.
[32]杨淑莹,边奠英. VC++图像处理程序设计.北京:清华大学出版社,北京交通大学出版社北京,2005.
[33]李英山,张星明,潘子浩。基于DM642的智能门禁系统设计与实现.微计算机应用,2008,2.66~69
[34] Texas Instrument incorporated. TMS320C6000 DSP Cache User' s Guide. 2003.10
[35] Texas Documents. Applications Using the TMS320C6000 Enhanced DMA.2003.10
[36] Texas Instrument incorporated TMS320C6000 Optimizing C/C++ Compiler User' s Guide. 2001.
[37] TI. TMS320DM642 Video/Imaging Fixed 2Point Digital Signal Processor,2002. 7.
[38] Information technology-coded representation of picture and audio information-lossy/ lossless coding of bi-level images.14492 Final Committee Draft, ISO/IEC JTC1/SC 29/WG1 N1359,1999,(6).
[39] Texas Instruments. Code Composer Studio User’s Guide.2000. 1~226
[40] Texas Instruments. TMS320C6000 Code Composer Studio Tutorial.2000. 1~226
[41] TMS320C6000 Programmer’s Guide, Texas Instruments SPRU198G.2002. 8
[42] Texas Instruments. TMS320C6000 DSP External Memory Interface(EMIF). 2008.
[43] Texas Instruments. TMS320C6000 Technical Brief.1999.
[44]陈升来,黄廉卿,郭静寰。基于整型提升小波变换的图像处理及DSP实现。光学精密工程,2006.6。14(3),498~502.
[45]黄勇坚,基于TMS320DM642的H.264编码器的实现与优化,[学位论文],山东:山东大学,2006,4。
[46]陈林杰,基于DSP的多光谱图像数据压缩技术研究,[学位论文],北京:中国科学院研究生院,2006.5
[47]罗建书,卓红艳,黎明君.基于整数小波变换的多光谱遥感图像压缩技术.国防科技大学学报,2005,27(6):35~43.
[48]陈军波,陈亚光,基于JPEG2000的静态医学图像压缩编码及DSP实现.电视技术,2004,(4),69~71
[49]陈升来,基于小波变换的遥感图像压缩及其DSP实现. [学位论文],北京:中国科学院研究生院,2006,5
[50]丁姗姗,基于DPCM与整数小波变换的红外医学图像压缩方法研究.[学位论文],山东:山东大学,2007,4
[2]顾军跃.超光谱图像的压缩算法研究[学位论文].杭州:浙江大学,2007
[3]李中峰,基于C6000 DSP的JPEG2000图像压缩技术研究,[学位论文],武汉:华中科技大学,2004,10.
[4]冈萨雷斯.数字图像处理(MATLAB版).北京:电子工业出版社,2005. 211~240
[5]赵荣椿,赵忠明等.数字图像处理导论.西安:西北工业大学出版社,2000. 133~160
[6]刘恒殊.超光谱遥感图象压缩算法的研究[学位论文].北京:中国科学院,2002
[7]全升学.卫星图象处理算法研究及其硬件实现[学位论文].北京:北京邮电大学2007
[8]王柯俨.遥感图像高效压缩编码方法研究[学位论文].西安:西安电子科技大学,2005
[9] Daubechies.I Ten Lectures on Wavelet, Capital City Press.1992.
[10]柯丽.基于小波变换的空间遥感图像实时压缩方法研究[学位论文],北京:中国科学院,2004.12
[11]王盟.基于第二代小波的图像压缩编码的研究[学位论文].天津:天津工业大学. 2005
[12]陈传波,金先级.数字图像处理.北京:机械工业出版社,2004. 72~87
[13]何小海,腾奇志.图像通信.西安:西安电子科技大学出版社,2005. 132~138
[14]张雪松,倪国强,周立伟.基于JPEG标准实时图像编码系统的研究.北京理工大学学报,1998,18(2):217~221
[15]黄少武.基于提升小波的超光谱图像压缩技术研究及DSP实现[学位论文].西安:西安科技大学,2007.
[16]张钧萍,张晔,周廷显.成像光谱技术超光谱图像分类研究现状与分析.中国空间科学技术,2001,1:52~54
[17]张晔,张钧萍.遥感超光谱图像处理技术.中国图像图形学报,2001,1(6):37~42
[18]吴冬梅等.基于谱间DPCM和整数小波变换的超光谱图像无损压缩.光子学报,2008,37(1):156~159
[19]吴冬梅,黄少武.基于DSP芯片的超光谱图像压缩技术。计算机与信息技术,2007,2,37~39
[20] [美] Kenneth R.Castleman著,朱志刚等译.数字图像处理.电子工业出版社,2002.2
[21] Thomas W F. Hyper-spectral image compression on reconfigurable platforms [MasterThesis]. Washington:University of Washington,2001
[22] Shapiro J M. Embedded image coding using zero-trees of wavelet coefficients.地址:IEEE Trans. Signal Processing,1993,41(12):3445～3462
[23] Charilaos Christopoulos,Athanassios Skodras,Touradj Ebrahimi. The JPEG2000 still image coding system:an overview. 2000,46(4):1103～1127
[24] Thomas Wilson,Curtiss Davis. Hyper-spectral Remote Sensing Technology program and the Naval Earth map Observer satellite. SPIE,2003,3437:2～10
[25]程正兴.小波分析算法与应用.西安:西安交通大学出版社,1998. 210~236
[26]乌崇德.从国外航天遥感器系统的发展现状中得到的启示.航天返回与遥感,1998,(3):18～24
[27]尹勇,欧光军,关荣锋. DSP集成开发环境CCS开发指南.北京:北京航空航天大学出版社,2003. 94~127
[28] Texas Instruments Incorporated,卞红雨等译. TMS320C6000系列DSP的CPU与外设.北京:清华大学出版社,2007.
[29] Texas Instruments Incorporated,田黎育等译. TMS320C6000系列DSP编程工具与指南.北京:清华大学出版社,2006.
[30]李方慧,王飞,何佩琨. TMS320C6000系列DSPs原理于应用.北京:电子工业出版社,2003.
[31]朱秀昌,刘峰,胡栋.数字图像处理与图像通信.北京:北京邮电大学出版社,2002.
[32]杨淑莹,边奠英. VC++图像处理程序设计.北京:清华大学出版社,北京交通大学出版社北京,2005.
[33]李英山,张星明,潘子浩。基于DM642的智能门禁系统设计与实现.微计算机应用,2008,2.66~69
[34] Texas Instrument incorporated. TMS320C6000 DSP Cache User' s Guide. 2003.10
[35] Texas Documents. Applications Using the TMS320C6000 Enhanced DMA.2003.10
[36] Texas Instrument incorporated TMS320C6000 Optimizing C/C++ Compiler User' s Guide. 2001.
[37] TI. TMS320DM642 Video/Imaging Fixed 2Point Digital Signal Processor,2002. 7.
[38] Information technology-coded representation of picture and audio information-lossy/ lossless coding of bi-level images.14492 Final Committee Draft, ISO/IEC JTC1/SC 29/WG1 N1359,1999,(6).
[39] Texas Instruments. Code Composer Studio User’s Guide.2000. 1~226
[40] Texas Instruments. TMS320C6000 Code Composer Studio Tutorial.2000. 1~226
[41] TMS320C6000 Programmer’s Guide, Texas Instruments SPRU198G.2002. 8
[42] Texas Instruments. TMS320C6000 DSP External Memory Interface(EMIF). 2008.
[43] Texas Instruments. TMS320C6000 Technical Brief.1999.
[44]陈升来,黄廉卿,郭静寰。基于整型提升小波变换的图像处理及DSP实现。光学精密工程,2006.6。14(3),498~502.
[45]黄勇坚,基于TMS320DM642的H.264编码器的实现与优化,[学位论文],山东:山东大学,2006,4。
[46]陈林杰,基于DSP的多光谱图像数据压缩技术研究,[学位论文],北京:中国科学院研究生院,2006.5
[47]罗建书,卓红艳,黎明君.基于整数小波变换的多光谱遥感图像压缩技术.国防科技大学学报,2005,27(6):35~43.
[48]陈军波,陈亚光,基于JPEG2000的静态医学图像压缩编码及DSP实现.电视技术,2004,(4),69~71
[49]陈升来,基于小波变换的遥感图像压缩及其DSP实现. [学位论文],北京:中国科学院研究生院,2006,5
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