高保真遥感图象压缩与分辨率增强联合处理研究
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摘要
日益增长的海量遥感数据为遥感图象的存储、传输及其广泛应用带来了极大的挑战,对其进行有效的数据压缩越发显得迫切和重要。然而,大多数压缩算法都一味追求高的客观质量(如信噪比)或主观质量,而忽略了遥感图象特殊的应用背景。在以信噪比为评价准则下,大多压缩算法一味倾向于舍弃高频信息的做法,易导致恢复图象分辨率的下降,这对具有丰富高频信息且应用背景特殊的遥感图象来说是很不利的。而结合压缩与后期应用目的的联合处理技术可有效避免上述算法的弊端,更好地满足遥感图象的特殊压缩要求。因此,研究适合于遥感图象的高保真压缩与分辨率增强的联合处理算法具有重要的理论意义和应用价值。
JPEG2000作为最新静止图象压缩标准为遥感图象的压缩提供了新的解决途径,其所具有的优良率失真性能在一定程度上保证了压缩质量,然而JPEG2000对于遥感图象压缩也存在一定的局限性,有必要根据遥感图象自身特点及其特殊的应用背景有针对性地对其进行优化。因此,本文基于JPEG2000,主要研究高保真遥感图象压缩与分辨率增强的联合处理技术,并在理论指导下,对基于JPEG2000的遥感图象压缩系统进行了硬件设计与实现。
首先,深入研究了JPEG2000基本原理和编码特性,并针对遥感图象的特点及其应用背景,与传统压缩方法进行了实验比较。在此基础上,从理论和实验两方面分析了JPEG2000在压缩过程中对图象分辨率信息的损失情况,由实验结果可知,即使是具有较高保真性能的JPEG2000在压缩的过程中也会导致图象分辨率的下降。
其次,针对JPEG2000倾向于舍弃高频信息,导致恢复图象分辨率下降的问题,提出了基于JPEG2000的高保真遥感图象压缩与分辨率增强的联合处理算法。该算法充分利用了JPEG2000中小波变换的多分辨率分解和时频局部化分析特性,在小波变换后嵌入信息检测算法,在小波域提取出不同分辨率的重要信息,并在压缩的过程中对检测出的重要信息进行优先保存,从而实现高保真压缩与分辨率增强的联合处理。实验证明,该算法不仅能使重要信息丰富的区域得到高保真压缩,16倍压缩时可获得至少2dB的提高,同时有效增强了恢复图象的分辨率,从边缘检测的应用效果来看,本文算法获得的边缘检测率比Part 1推荐算法和Jasper算法的都更高,且对于不同压缩比,其检测率始终保持在90%以上,检测出的边缘连续性也更好。
最后,在理论研究成果的指导下,结合小型化、集成化、多层次硬件和软件模块的设计思想,在基于专用芯片ADV202+DSP+FPGA的硬件平台上设计实现了遥感图象压缩系统。测试结果表明,该系统的压缩图象质量良好,能满足各项设计指标,且系统性能稳定、集成度较高、功能可扩展,为JPEG2000在星载遥感图象压缩中的实际应用提供了一定的新思路,具有广泛的应用前景。
The rapid increasing data of the remote sensing image (RSI) brings great challenges to storage, transmission and application. Therefore, it’s becoming more and more important to compress RSI efficiently. Most conventional compression methods are blindly pursuing the high subjective quality or objective quality, neglecting the special applications for RSI. In order to get high SNR, these methods tend to discard high-frequency information,and as the results the resolution of reconstructed image is declined. It’s very negative for RSI which has abundant high-frequency information and special applications in military reconnaissance. The combined processing technique, both considering the quality of reconstructed image and special applications, can effectively avoid shortcomings of conventional compression, and be more suitable for RSI compression. Therefore, the research on remote sensing image processing combining high fidelity compression and resolution enhancement has great theoretical significance and application value.
Being a new international still image compression standard, JPEG2000 provides a new solution for RSI compression. Its excellent rate-distortion performance insures the quality of reconstructed image on the whole. However, as a common standard, JPEG2000 has some limitations for RSI compression, and it is necessary to improve it according to the characteristics and special applications of RSI. Therefore, this dissertation, based on JPEG2000 standard, studies the combined processing technique which associates high fidelity image compression with resolution enhancement, then realizes RSI compression in the hardware system.
Firstly, the fundamentals and coding characteristics of JPEG2000 are thoroughly reviewed, then compares the JPEG2000 with conventional compression methods in effect of RSI compression. In order to analyze the decline in resolution during the process of compression, this dissertation gives theoretical analysis and takes same experiments. From both theoretical and experimental analysis, JPEG2000 standard tends to discard high-frequency information,and as the results, the resolution of reconstructed image is declined.
Secondly, JPEG2000 tends to discard high-frequency information to get high quality, but the resolution of reconstructed image is declined. In order to solve this problem, the research on remote sensing image processing combining high fidelity compression and resolution enhancement is proposed. This combined processing algorithm, taking full advantage of multi-resolution decomposition and time-frequency analysis of wavelet transform, detects important information of different resolution in the wavelet domain, and preserve them with higher priority to realize high fidelity compression and resolution enhancement. Experiments are conducted on a real remote sensing image, and the results show that the algorithm can not only get high fidelity compression, at least 2dB improved, but also can enhance the resolution of reconstructed image. For the application of edge detection, the combined processing algorithm gets higher detection rate than Part 1 algorithm and Jasper algorithm. Furthermore, its detection rate has remained at least 90 percent in different compression ratio and the detected edge is more continuity.
Finally, based on the theoretical algorithm study, according to the design idea of miniaturization, integration, hardware modules and software modules on multiple levels, a hardware system for RSI compression using ADV202,DSP and FPGA is designed and implemented. The working status shows that, the system can meet each design specifications and has good compressed effect, stable performance, high integration and wide applications. The system also provides a new idea for employing JPEG2000 standard in the satellite RSI compression.
JPEG2000作为最新静止图象压缩标准为遥感图象的压缩提供了新的解决途径,其所具有的优良率失真性能在一定程度上保证了压缩质量,然而JPEG2000对于遥感图象压缩也存在一定的局限性,有必要根据遥感图象自身特点及其特殊的应用背景有针对性地对其进行优化。因此,本文基于JPEG2000,主要研究高保真遥感图象压缩与分辨率增强的联合处理技术,并在理论指导下,对基于JPEG2000的遥感图象压缩系统进行了硬件设计与实现。
首先,深入研究了JPEG2000基本原理和编码特性,并针对遥感图象的特点及其应用背景,与传统压缩方法进行了实验比较。在此基础上,从理论和实验两方面分析了JPEG2000在压缩过程中对图象分辨率信息的损失情况,由实验结果可知,即使是具有较高保真性能的JPEG2000在压缩的过程中也会导致图象分辨率的下降。
其次,针对JPEG2000倾向于舍弃高频信息,导致恢复图象分辨率下降的问题,提出了基于JPEG2000的高保真遥感图象压缩与分辨率增强的联合处理算法。该算法充分利用了JPEG2000中小波变换的多分辨率分解和时频局部化分析特性,在小波变换后嵌入信息检测算法,在小波域提取出不同分辨率的重要信息,并在压缩的过程中对检测出的重要信息进行优先保存,从而实现高保真压缩与分辨率增强的联合处理。实验证明,该算法不仅能使重要信息丰富的区域得到高保真压缩,16倍压缩时可获得至少2dB的提高,同时有效增强了恢复图象的分辨率,从边缘检测的应用效果来看,本文算法获得的边缘检测率比Part 1推荐算法和Jasper算法的都更高,且对于不同压缩比,其检测率始终保持在90%以上,检测出的边缘连续性也更好。
最后,在理论研究成果的指导下,结合小型化、集成化、多层次硬件和软件模块的设计思想,在基于专用芯片ADV202+DSP+FPGA的硬件平台上设计实现了遥感图象压缩系统。测试结果表明,该系统的压缩图象质量良好,能满足各项设计指标,且系统性能稳定、集成度较高、功能可扩展,为JPEG2000在星载遥感图象压缩中的实际应用提供了一定的新思路,具有广泛的应用前景。
The rapid increasing data of the remote sensing image (RSI) brings great challenges to storage, transmission and application. Therefore, it’s becoming more and more important to compress RSI efficiently. Most conventional compression methods are blindly pursuing the high subjective quality or objective quality, neglecting the special applications for RSI. In order to get high SNR, these methods tend to discard high-frequency information,and as the results the resolution of reconstructed image is declined. It’s very negative for RSI which has abundant high-frequency information and special applications in military reconnaissance. The combined processing technique, both considering the quality of reconstructed image and special applications, can effectively avoid shortcomings of conventional compression, and be more suitable for RSI compression. Therefore, the research on remote sensing image processing combining high fidelity compression and resolution enhancement has great theoretical significance and application value.
Being a new international still image compression standard, JPEG2000 provides a new solution for RSI compression. Its excellent rate-distortion performance insures the quality of reconstructed image on the whole. However, as a common standard, JPEG2000 has some limitations for RSI compression, and it is necessary to improve it according to the characteristics and special applications of RSI. Therefore, this dissertation, based on JPEG2000 standard, studies the combined processing technique which associates high fidelity image compression with resolution enhancement, then realizes RSI compression in the hardware system.
Firstly, the fundamentals and coding characteristics of JPEG2000 are thoroughly reviewed, then compares the JPEG2000 with conventional compression methods in effect of RSI compression. In order to analyze the decline in resolution during the process of compression, this dissertation gives theoretical analysis and takes same experiments. From both theoretical and experimental analysis, JPEG2000 standard tends to discard high-frequency information,and as the results, the resolution of reconstructed image is declined.
Secondly, JPEG2000 tends to discard high-frequency information to get high quality, but the resolution of reconstructed image is declined. In order to solve this problem, the research on remote sensing image processing combining high fidelity compression and resolution enhancement is proposed. This combined processing algorithm, taking full advantage of multi-resolution decomposition and time-frequency analysis of wavelet transform, detects important information of different resolution in the wavelet domain, and preserve them with higher priority to realize high fidelity compression and resolution enhancement. Experiments are conducted on a real remote sensing image, and the results show that the algorithm can not only get high fidelity compression, at least 2dB improved, but also can enhance the resolution of reconstructed image. For the application of edge detection, the combined processing algorithm gets higher detection rate than Part 1 algorithm and Jasper algorithm. Furthermore, its detection rate has remained at least 90 percent in different compression ratio and the detected edge is more continuity.
Finally, based on the theoretical algorithm study, according to the design idea of miniaturization, integration, hardware modules and software modules on multiple levels, a hardware system for RSI compression using ADV202,DSP and FPGA is designed and implemented. The working status shows that, the system can meet each design specifications and has good compressed effect, stable performance, high integration and wide applications. The system also provides a new idea for employing JPEG2000 standard in the satellite RSI compression.
引文
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10 J. Lee. Optimized Quadtree for K-L Transform in Multispactral Image Coding. IEEE Trans. on Image Processing. 1999, 8(4): 453~461
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13 A. Said, W. A. Pearlman. A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees. IEEE Trans. on Circuits and System for Video Technology. 1996, 6(3): 243~250
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2高卫斌,冉承其.遥感卫星数据传输技术发展分析.中国空间科学技术. 2005, (6): 30~36
3邓家先.遥感图象编码技术研究.西安电子科技大学博士学位论文. 2004: 7~8
4陈世平.高分辨率卫星遥感数据传输技术发展的若干问题.空间电子技术. 2003, (3): 1~5
5刘荣科,张晓林,廖小涛.星载遥感图象压缩编码技术综述.遥测遥控. 2004, 25(2): 7~12
6 R. E. Roger, M. C. Cavenor. Lossless Compression of AVIRIS Images. IEEE Trans. on Image Processing. 1996, 5(5): 713~719
7 A. K. Rao, S. Bhargava. Multispectral Data Compression Using Bidirectional Interband Prediction. IEEE Trans. on Geoscience and Remote Sensing. 1996, 34(2): 385~397
8 J. Lee. Optimized Quadtree for K-L Transform in Multispactral Image Coding. IEEE Trans. on Image Processing. 1999, 8(4): 453~461
9 P. Hou, A. Hojjatoleslami, M. Petrou, C. Underwood and J. Kittler. Improved JPEG Coding for Remote Sensing. Proceedings of the Fourteenth International Conference on Pattern Recognition. Surrey University, Guildford, UK. 1998, 2: 1637~1639
10 J. Lee. Optimized Quadtree for K-L Transform in Multispactral Image Coding. IEEE Trans. on Image Processing. 1999, 8(4): 453~461
11王春胜,禹秉熙,戴逸松,文大化.高光谱图象数据变换编码压缩方法.遥感学报. 2000, 4(3): 96~99
12 J. M. Shapiro. Embedded Image Coding Using Zerotrees of Wavelet Coefficients. IEEE Trans. on Signal Processing. 1993, 41(12): 3445~3462
13 A. Said, W. A. Pearlman. A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees. IEEE Trans. on Circuits and System for Video Technology. 1996, 6(3): 243~250
14 A. Guzman, M. Beltran. Satellite On-Board Image Compression Adviser. Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, Rey Juan Carlos University, Madrid Spain. 2004: 296~301
15 T. Yu, A. T. S. Ho, S. C. Tam., S. C. Tan, L. T. Yap. A Novel Hybrid Bi-orthogonal Wavelets/ADPCM Algorithm for Very Low Bit Rate Satellite Image Compression. IEEE International Symposium on Geoscience and Remote Sensing, Hamburg, Germany, 1999, 4: 2051~2053
16 Z. H. Wang, H. B. Xu, Y. Tian, J. W. Tian, J. Liu. Integer Haar Wavelet for Romote Sensing Image Compression. Proceedings of the International Conference on Signal Processing, Beijing China, 2002, 1: 715~718
17 D. W. Ma, S. Z. Yang. RMEZW Algorithm for Remote Sensing Image Compression. Proceedings of the Fourth International Conference on Microwave and Millimeter Wave Technolog, Beijing, China, 2004: 957~960
18 E. Christophe, W. A. Pearlman. Three-dimensional SPIHT Coding of Hyperspectral Images with Random Access and Resolution Scalability. The Fortieth Asilomar Conference on Signals, Systems and Computers, Pacific Grove California, USA, 2006: 1897~1901
19 D. Taubman. High Performance Scalable Image Compression with EBCOT. IEEE Trans. on Image Processing. 2000, 9(7): 1158~1170
20 D. Taubman, E. Ordentlich, M. Weinberger, G. Seroussi, I. Ueno, F. Ono. Embedded Block Coding in JPEG2000. Proceedings of the International Conference on Image Processing. Vancouver, Canada, 2000, 2: 33~36
21 P. S. Yeh, W. H. Miller. The Development of Lossless Data Compression Technology for Remote Sensing Applications. IEEE International Geoscience and Remote Sensing Symposium, Pasadena, California, USA, 1994, 1: 307~309
22 A. Gohar, E. Osman, H.Bayoumy. A Real Time Compression Approach for Aerial and Remote Sensing Images. Proceedings of the 46th IEEE International Midwest Symposium on Circuits and Systems, Cairo, Egypt, 2003, 2: 548~551
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