SPIHT算法硬件适应性改造
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摘要
随着遥感技术的不断发展，传感器的分辨率不断提高，遥感卫星越来越多的应用于国民经济和国防军事领域，并发挥着越来越重要的作用。但是，在获得更多的有价值的信息的同时，遥感数据量也开始日益庞大，无论是在存储和传输方面都给星载系统带来了巨大的压力，如果仅仅依靠增加系统的存储能力传输能力则是不够的。因此，在这种情况下，寻找一种适合星载图像特点的高速的压缩方法，并对其进行硬件适应性改造就具有十分重要的实用价值和研究意义。
基于小波变换的嵌入式图像压缩方法是当前研究较多的一类图像编码技术。其具有的高压缩比，嵌入式码流，复杂度低等特点比较符合遥感图像压缩的要求。因此本文重点对这一类图像编码方法进行研究，然后结合遥感图像、编码算法以及星载系统的特点，从中选择了等级树集合分裂算法作为适应性改造的对象，并对这个算法从小波分解、处理模式、容错性和DSP程序优化这四个方面进行了改进，取得了如下研究成果：
1．采用整数小波提升方式代替卷积运算，使算法更易于硬件实现。
2．根据推帚式成像的原理采用条带式的处理流程，减小了运算时的硬件系统的存储量和负荷，并有效的去除了条带效应的问题。
3．提出了一种基于小波空间方向树集合块的编码方式，增强了容错性，有效防止了误码扩散，减小了传输过程中误码造成的影响。
4．根据使用的DSP芯片的特点，采用了多种优化代码的方法对程序进行了优化，仿真试验表明，经过优化后程序在硬件执行时的速度明显加快。
With the development of the remote sensing technology and the increase of the spatial resolution, satellites of remote sensing have been widely used in the national economy and the national defense. Although we have obtained much information by processing lots of data, more need to be processed. As a result, the On-board System is enduring a great difficulty on storage and transmission remote sensing image, and this problem can not be solved well by only increasing the ability of the storage and transmission. In this situation, it is important to find a high-speed image compression algorithm which is proper for the characteristics of remote sensing image, and to optimize it basing on specific hardware.
The embedded image compression based on the wavelet transform belongs to the image coding techniques which are widely studied in recent years, and its characteristics which include higher-compression-rate, embedded bit-stream, lower computer complexity, satisfy most the requirements in the compression of remote sensing image. In this thesis, we mainly study this kind of image coding technique, and then choose the Set Partition in Hierarchical Trees (SPIHT) to perform the improvement on. The improvement is achieved by taking into account the characteristics of the remote sensing image, the compression algorithms, and the hardware systems. From four aspects, including wavelet transform, processing pattern, error resilient, and optimization of DSP code, we improved SPIHT algorithm and obtained some achievement as bellow:
1. Replace the convolution by the integer wavelet lifting, and the calculation of wavelet transform becomes easy to be implemented in hardware.
基于小波变换的嵌入式图像压缩方法是当前研究较多的一类图像编码技术。其具有的高压缩比，嵌入式码流，复杂度低等特点比较符合遥感图像压缩的要求。因此本文重点对这一类图像编码方法进行研究，然后结合遥感图像、编码算法以及星载系统的特点，从中选择了等级树集合分裂算法作为适应性改造的对象，并对这个算法从小波分解、处理模式、容错性和DSP程序优化这四个方面进行了改进，取得了如下研究成果：
1．采用整数小波提升方式代替卷积运算，使算法更易于硬件实现。
2．根据推帚式成像的原理采用条带式的处理流程，减小了运算时的硬件系统的存储量和负荷，并有效的去除了条带效应的问题。
3．提出了一种基于小波空间方向树集合块的编码方式，增强了容错性，有效防止了误码扩散，减小了传输过程中误码造成的影响。
4．根据使用的DSP芯片的特点，采用了多种优化代码的方法对程序进行了优化，仿真试验表明，经过优化后程序在硬件执行时的速度明显加快。
With the development of the remote sensing technology and the increase of the spatial resolution, satellites of remote sensing have been widely used in the national economy and the national defense. Although we have obtained much information by processing lots of data, more need to be processed. As a result, the On-board System is enduring a great difficulty on storage and transmission remote sensing image, and this problem can not be solved well by only increasing the ability of the storage and transmission. In this situation, it is important to find a high-speed image compression algorithm which is proper for the characteristics of remote sensing image, and to optimize it basing on specific hardware.
The embedded image compression based on the wavelet transform belongs to the image coding techniques which are widely studied in recent years, and its characteristics which include higher-compression-rate, embedded bit-stream, lower computer complexity, satisfy most the requirements in the compression of remote sensing image. In this thesis, we mainly study this kind of image coding technique, and then choose the Set Partition in Hierarchical Trees (SPIHT) to perform the improvement on. The improvement is achieved by taking into account the characteristics of the remote sensing image, the compression algorithms, and the hardware systems. From four aspects, including wavelet transform, processing pattern, error resilient, and optimization of DSP code, we improved SPIHT algorithm and obtained some achievement as bellow:
1. Replace the convolution by the integer wavelet lifting, and the calculation of wavelet transform becomes easy to be implemented in hardware.
引文
[1] V. D. Vaughn, T. S. Wilkinson. System consideration for multi-spectral image compression designs. IEEE Signal Processing Magazine,1995, 12(1): 19-31
[2] David S．Taubman，Michael W．Marcellin．魏江力，柏正尧译．JPEG2000图像压缩基础、标准和实践：电子工业出版社，2004
[3] J. M. Shapiro. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Processing, 1993, 41(12):3445-3462
[4] A. Said and W. A. Pearlman. 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
[5] A.Islam and W.A.Pearlman. An embedded and efficient low-complexity hierarchical image coder. Visual Communications and Image Processing, 1999(3653):294-305
[6] 沈庭芝．数字图像处理及模式识别．北京：北京理土大学出版，1998
[7] K．R Castleman，朱志刚等译．数字图像处理．北京：电子工业出版，2002
[8] Joyce Van de Vegte，候正信等译．数字信号处理基础．北京：电子工业出版社，2003
[9] 吴升，王介生等．图像的排列变换计算机学报．1998，21(6)：514-519
[10] M.Kunt, A.lkonomopoluos, M.Kocher. Second Generation Image Coding Techniques. Proceedings of the IEEE, 1985, 73(4):549-574
[11] 黄贤武等．数字图像处理与压缩编码技术．电子科技大学出版社，2003．12-18
[12] 章毓晋．图象处理和分析．清华大学出版社，1999
[13] 付祖芸．信息论基础．电子工业出版杜，2002．443-449
[14] 胡广书．数字信号处理—理论、算法与实现．清华大学出版，2000．331-337
[15] 张贤达．现代数字信号处理．清华大学出版社，1999．443-448[16] Allen Gersho. Optimal Nonlinear Interpolative Vector Quantization. IEEE Transactions on Communications, 1999, 38(9): 1285-1287
[17] LindeY, Buzo A ,Gray R M. An algorithm for Vector Quantizer Design. IEEE Transactions on Communications. 1980, 28(1):84-95
[18] 李建平．基于小波变换和神经网络的图像压缩．神经网络理论与应用研究．1999：436-471
[19] 温江涛，朱雪龙．基于IFS分形理论的信源编码技术的研究．电子学报．1996，11(10)：1-7
[20] 焦李成．神经网络的应用与实现．西安电子科技大学出版社，1995：215-252
[21] 马维祯．利用子波变换的图象压缩编码技术信号处理．1995，11(3)：129-138
[22] 沈兰荪．图像编码与异步传输．人民邮电出版社，1998
[23] 昊乐南．数据压缩的原理与应用．北京：电子工业出版社，1994年
[24] 李弼程，罗建书．小波分析及其应用．北京：电子工业出版社，2003
[25] S. Mallat. A theory for multiresolution signal decomposition: The wavelet-presentation. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1989, 11(7):674-693
[26] M. D. Adams, F. Kossentini. Reversible Integers to Integers Wavelet Transform for Image Compression Performance Evaluation and Analysis. IEEE Transaction on Image Processing, 2000, 9(6): 1010-1024
[27] W. Sweldens. The lifting scheme: A construction of second generation wavelets. SIAM Journal of Mathematical Analysis, 1998, 29(2):511-546.
[28] K. Ramchandran, M. Orchard. An Investigation of Wavelet-Based Image Coding Using an Entropy-Constrained Quantization Framework. IEEE Transactions on Signal Processing, 1998, 46(2): 342-353
[29] S. D. Servetto, K. Ramchandran, and M. T. Orchard. Image coding based on a morphological representation of wavelet data. IEEE Transaction on Image Processing,??1999, 8(9):1161-1174
[30] B. Chai, J. Vass, X. Zhuang. Significance-linked connected component analysis for wavelet image coding. IEEE Transaction on Image Processing, 1999, 8(6): 774-784
[31] Sun Yunlian, Shi Gang and Chen Yunping. Study of the Coding in the Progressive Image Transmission. Proceedings of ICSP, 1996(12): 1063-1066
[32] 昊乐南．数据压缩．北京：电子工业出版社，2000
[33] M. Antoine, M. Barlaud, E Mathieu, and I. Daubechies. Image coding using wavelet transform. IEEE Transaction on Image Processing, 1992,1 (2): 205-220,.
[34] Jun-Ki Cho, Min-Cheol Hwang, Jin-Sam Kim, Byeong-Doo Choi, Stmg-Jea Ko. Fast Implementation of Wavelet Lifting for JPEG2000 on a Fixed-Point DSP. Proceeding of 2004 International Technical Conference on Circuits Systems, Computers and Communications, 2004
[35] I. Daubechies and W. Sweldens. Factoring wavelet transforms into lifting schemes. The Journal. of Fourier Analysis and Applications, 19984(3):249-269,.
[36] 王秋国，衰卫卫．一般的9／7小波滤波器及其图像压缩性能研究．电子学报，2001，29(1)：130-132．
[37] 石青云．郝鹏威．小波滤波器构造和优选的代数方法
[38] 戴昌达，姜小光，唐伶俐．遥感图像应用处理与分析．清华大学出版社，2004
[39] Shuo Sheng and Salim Manji. A New Unequal Error Protection Scheme for SPIHT in Progressive Image Transmission. 34th Annual Conference on Information Sciences and Systems, Princeton, NJ, March, 2000
[40] G. Sherwood and K. Zeger. Error protection for progressive image transmission over memoryless and fading channels. IEEE Transaction on Communication, 1998,46(12):1555-1559
[41] Shih-Hsuan Yang and Tsung-Chao Cheng. Error-Resilient SPIHT Coding of??Images. Electronics Letters, 2000, 36(3): 208-210
[42] 廖小涛，张晓林，刘荣科．SPIHT算法容错性能的分析和改进．遥测遥控，2003：44-48
[43] Shahab Baqai, Farhan Baqai, Usman Hameed, Sohail Sheikh, Ashfaq Khokhar. Error Resilience of EZW Coder for Image Transmission in Lossy Networks. IEEE Fourth International Symposium on Multimedia Software Engineering, 2002:328-333
[44] 李方慧、王飞、何佩琨。TMS320C6000系列DSPs原理与应用(第2版)．电子工业出版社，2003
[45] Jian Wang, Bogong Su. A Scalable loop Optimization Approach for Scalable DSP Processors. IEEE International Conference on Acoustics, Speech, and Signal. Processing, 2000(6):3646-3649
[2] David S．Taubman，Michael W．Marcellin．魏江力，柏正尧译．JPEG2000图像压缩基础、标准和实践：电子工业出版社，2004
[3] J. M. Shapiro. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Processing, 1993, 41(12):3445-3462
[4] A. Said and W. A. Pearlman. 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
[5] A.Islam and W.A.Pearlman. An embedded and efficient low-complexity hierarchical image coder. Visual Communications and Image Processing, 1999(3653):294-305
[6] 沈庭芝．数字图像处理及模式识别．北京：北京理土大学出版，1998
[7] K．R Castleman，朱志刚等译．数字图像处理．北京：电子工业出版，2002
[8] Joyce Van de Vegte，候正信等译．数字信号处理基础．北京：电子工业出版社，2003
[9] 吴升，王介生等．图像的排列变换计算机学报．1998，21(6)：514-519
[10] M.Kunt, A.lkonomopoluos, M.Kocher. Second Generation Image Coding Techniques. Proceedings of the IEEE, 1985, 73(4):549-574
[11] 黄贤武等．数字图像处理与压缩编码技术．电子科技大学出版社，2003．12-18
[12] 章毓晋．图象处理和分析．清华大学出版社，1999
[13] 付祖芸．信息论基础．电子工业出版杜，2002．443-449
[14] 胡广书．数字信号处理—理论、算法与实现．清华大学出版，2000．331-337
[15] 张贤达．现代数字信号处理．清华大学出版社，1999．443-448[16] Allen Gersho. Optimal Nonlinear Interpolative Vector Quantization. IEEE Transactions on Communications, 1999, 38(9): 1285-1287
[17] LindeY, Buzo A ,Gray R M. An algorithm for Vector Quantizer Design. IEEE Transactions on Communications. 1980, 28(1):84-95
[18] 李建平．基于小波变换和神经网络的图像压缩．神经网络理论与应用研究．1999：436-471
[19] 温江涛，朱雪龙．基于IFS分形理论的信源编码技术的研究．电子学报．1996，11(10)：1-7
[20] 焦李成．神经网络的应用与实现．西安电子科技大学出版社，1995：215-252
[21] 马维祯．利用子波变换的图象压缩编码技术信号处理．1995，11(3)：129-138
[22] 沈兰荪．图像编码与异步传输．人民邮电出版社，1998
[23] 昊乐南．数据压缩的原理与应用．北京：电子工业出版社，1994年
[24] 李弼程，罗建书．小波分析及其应用．北京：电子工业出版社，2003
[25] S. Mallat. A theory for multiresolution signal decomposition: The wavelet-presentation. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1989, 11(7):674-693
[26] M. D. Adams, F. Kossentini. Reversible Integers to Integers Wavelet Transform for Image Compression Performance Evaluation and Analysis. IEEE Transaction on Image Processing, 2000, 9(6): 1010-1024
[27] W. Sweldens. The lifting scheme: A construction of second generation wavelets. SIAM Journal of Mathematical Analysis, 1998, 29(2):511-546.
[28] K. Ramchandran, M. Orchard. An Investigation of Wavelet-Based Image Coding Using an Entropy-Constrained Quantization Framework. IEEE Transactions on Signal Processing, 1998, 46(2): 342-353
[29] S. D. Servetto, K. Ramchandran, and M. T. Orchard. Image coding based on a morphological representation of wavelet data. IEEE Transaction on Image Processing,??1999, 8(9):1161-1174
[30] B. Chai, J. Vass, X. Zhuang. Significance-linked connected component analysis for wavelet image coding. IEEE Transaction on Image Processing, 1999, 8(6): 774-784
[31] Sun Yunlian, Shi Gang and Chen Yunping. Study of the Coding in the Progressive Image Transmission. Proceedings of ICSP, 1996(12): 1063-1066
[32] 昊乐南．数据压缩．北京：电子工业出版社，2000
[33] M. Antoine, M. Barlaud, E Mathieu, and I. Daubechies. Image coding using wavelet transform. IEEE Transaction on Image Processing, 1992,1 (2): 205-220,.
[34] Jun-Ki Cho, Min-Cheol Hwang, Jin-Sam Kim, Byeong-Doo Choi, Stmg-Jea Ko. Fast Implementation of Wavelet Lifting for JPEG2000 on a Fixed-Point DSP. Proceeding of 2004 International Technical Conference on Circuits Systems, Computers and Communications, 2004
[35] I. Daubechies and W. Sweldens. Factoring wavelet transforms into lifting schemes. The Journal. of Fourier Analysis and Applications, 19984(3):249-269,.
[36] 王秋国，衰卫卫．一般的9／7小波滤波器及其图像压缩性能研究．电子学报，2001，29(1)：130-132．
[37] 石青云．郝鹏威．小波滤波器构造和优选的代数方法
[38] 戴昌达，姜小光，唐伶俐．遥感图像应用处理与分析．清华大学出版社，2004
[39] Shuo Sheng and Salim Manji. A New Unequal Error Protection Scheme for SPIHT in Progressive Image Transmission. 34th Annual Conference on Information Sciences and Systems, Princeton, NJ, March, 2000
[40] G. Sherwood and K. Zeger. Error protection for progressive image transmission over memoryless and fading channels. IEEE Transaction on Communication, 1998,46(12):1555-1559
[41] Shih-Hsuan Yang and Tsung-Chao Cheng. Error-Resilient SPIHT Coding of??Images. Electronics Letters, 2000, 36(3): 208-210
[42] 廖小涛，张晓林，刘荣科．SPIHT算法容错性能的分析和改进．遥测遥控，2003：44-48
[43] Shahab Baqai, Farhan Baqai, Usman Hameed, Sohail Sheikh, Ashfaq Khokhar. Error Resilience of EZW Coder for Image Transmission in Lossy Networks. IEEE Fourth International Symposium on Multimedia Software Engineering, 2002:328-333
[44] 李方慧、王飞、何佩琨。TMS320C6000系列DSPs原理与应用(第2版)．电子工业出版社，2003
[45] Jian Wang, Bogong Su. A Scalable loop Optimization Approach for Scalable DSP Processors. IEEE International Conference on Acoustics, Speech, and Signal. Processing, 2000(6):3646-3649