基于实时星点定位的感兴趣区域星图压缩方法

doi:10.13229/j.cnki.jdxbgxb.20240734

摘要/Abstract

摘要：

针对空间探测相机存储和传输星图占用过多星上资源的问题，提出了一种基于实时星点定位的感兴趣区域星图压缩方法。首先，利用环状结构元的顶帽算法分割星图中的星点目标与背景；然后，通过改进的星点定位算法，扫描星图的同时检测星点目标并输出坐标；接着，根据星点坐标对原星图相应位置实时开窗得到星点图像，再利用JPEG-LS图像压缩算法对星点图像无损压缩，同时对原星图背景进行压缩参数可调的近无损压缩。FPGA仿真实验表明：对星点个数小于20的模拟星图，本文方法处理时间在扫描一遍星图时间的基础上仅增加不到6%。当背景压缩参数为2时，本文方法较JPEG-LS无损压缩算法压缩比提升2倍以上，PSNR大于40 dB、SSIM大于0.95，该方法压缩性能优势明显，具有显著工程应用价值。

关键词: 信号与信息处理, 感兴趣区域星图压缩, 实时星点定位, JPEG-LS

Abstract:

To solve the problem of storing and transmitting star maps by space detection cameras， a method of compression of star maps of region of interest based on real-time star location is proposed in this paper. First， the top hat algorithm of the ring structure element is used to separate the star object and the background in the star map. Then， through the improved star location algorithm， the star target is detected and the coordinates are output while scanning the star map. Then， the star image is obtained by opening the window in real time according to the star coordinates of the original star map. The star image is lossless compressed by JPEG-LS image compression algorithm， and the original star map background is compressed by near lossless compression with adjustable parameters. FPGA simulation results show that the processing time of the proposed method increases by less than 6% on the basis of scanning a star map with less than 20 star points. When the background compression parameter is 2， the compression ratio of the proposed method is more than 2 times higher than that of JPEG-LS lossless compression algorithm， and the PSNR is greater than 40 dB and SSIM is greater than 0.95. This method has obvious advantages in compression performance and has significant engineering application value.

Key words: signals and signal processing, region of interst star map compression, real-time star location, JPEG-LS

中图分类号:

V448.22

张刘,梁嘉新,刘赫,张贵祥,刘威,李岩,章家保. 基于实时星点定位的感兴趣区域星图压缩方法[J]. 吉林大学学报(工学版), 2026, 56(3): 802-810.

Liu ZHANG,Jia-xin LIANG,He LIU,Gui-xiang ZHANG,Wei LIU,Yan LI,Jia-bao ZHANG. Region of interest star map compression method based on real-time star location[J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(3): 802-810.

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参考文献 17

[1]	黄冠. 星图无损压缩算法研究及其硬件描述语言设计[D]. 哈尔滨: 哈尔滨工业大学航天学院, 2023.
	Huang Guan. Research on star map lossless compression algorithm and its hardware description language design[D]. Harbin: School of Astronautics, Harbin Institute of Technology, 2023.
[2]	Zhou F, Ye T, Chai X, et al. Novel autonomous on-orbit calibration method for star sensors[J]. Optics and Lasers in Engineering, 2015, 67: 135-144.
[3]	刘薇, 刘松林, 郭子博, 等. 基于模型定义的光学遥感卫星星上处理系统设计[J]. 测绘学报, 2024, 53(4): 689-699.
	Liu Wei, Liu Song-lin, Guo Zi-bo, et al. Design of an optical remote sensing satellite onboard processing system based on model definition[J]. Journal of Surveying and Mapping, 2024, 53(4): 689-699.
[4]	刘向增, 范立佳, 徐雪灵, 等. 星载JPEG-LS图像压缩质量评价研究[J]. 微电子学与计算机, 2021, 38(9): 45-53.
	Liu Xiang-zeng, Fan Li-jia, Xu Xue-ling, et al. Research on quality evaluation of spaceborne JPEG-LS image compression[J]. Microelectronics and Computer Science, 2021, 38(9): 45-53.
[5]	李灵珊. 基于目标检测的星图压缩算法研究[D]. 长春: 吉林大学通信工程学院, 2024.
	Li Ling-shan. Research on star map compression algorithm based on object detection[D]. Changchun: College of Communication Engineering, Jilin University, 2024.
[6]	邓宸伟, 赵保军, 刘海燕. 星空图像压缩算法[J]. 北京理工大学学报, 2008, 28(12): 1096-1100.
	Deng Chen-wei, Zhao Bao-jun, Liu Hai-yan. Starry sky image compression algorithm[J]. Transactions of Beijing institute of Technology, 2008, 28(12): 1096-1100.
[7]	陈静远, 刘晓, 杜丽丽, 等. 面阵CMOS传感器图像噪声仿真与验证[J]. 光学学报, 2024, 44(12): 375-383.
	Chen Jing-yuan, Liu Xiao, Du Li-li, et al. Simulation and verification of image noise of area array CMOS sensor[J]. Acta Optica Sinica, 2024, 44(12): 375-383.
[8]	习腾彦, 袁丽华, 王树鹏. 基于局部对比度的自适应Top-Hat红外小目标检测[J]. 激光与光电子学进展, 2023, 60(16): 372-384.
	Xi Teng-yan, Yuan Li-hua, Wang Shu-peng.Adaptive Top-Hat infrared small target detection based on local contrast[J]. Advances in Laser and Optoelectronics, 2023, 60(16): 372-384.
[9]	Bai X Z, Zhou F G. Analysis of new top-hat transformation and the application for infrared dim small target detection[J]. Pattern Recognition, 2010, 43(6): 2145-2156.
[10]	徐彬, 郑勇, 陈张雷, 等. 星光导航星点目标区域提取算法改进[J]. 测绘学报, 2023, 52(5): 760-768.
	Xu Bin, Zheng Yong, Chen Zhang-lei, et al. The improvement of star target region extraction algorithm for star centroid[J]. Journal of Surveying and Mapping, 2023, 52(5): 760-768.
[11]	练达, 周琦, 余路伟, 等. 高动态条件下星点像斑建模与补偿[J]. 飞控与探测, 2020, 3(3): 86-94.
	Lian Da, Zhou Qi, Yu Lu-wei, et al. Modeling and compensation of star spot images under high dynamic conditions[J]. Flight Control and Detection, 2020, 3(3): 86-94.
[12]	章家保, 张剑阳, 刘赫, 等. 改进游程编码算法的快速星点提取[J]. 吉林大学学报: 工学版, 2025, 55(4): 1467-1473.
	Zhang Jia-bao, Zhang Jian-yang, Liu He, et al. Fast star point extraction using improved run length encoding algorithm[J]. Journal of Jilin University (Engineering and Technology Edition), 2025, 55(4): 1467-1473.
[13]	Xuan Wang, Lei Gong, Chao Wang, et al. UH-JLS:A parallel ultra-high throughput JPEG-LS encoding architecture for lossless image compression[C]∥IEEE 39th International Conference on Computer Design, USA: University of Connecticut at Storrs, 2021, 335-343.
[14]	韩宇, 袁素春, 张建华, 等. 高分七号卫星图像压缩FPGA设计与实现技术[J]. 航天器工程, 2020, 29(3): 169-176.
	Han Yu, Yuan Su-chun, Zhang Jian-hua, et al. FPGA design and implementation technology for compression of high-resolution satellite images from GF-7 satellite[J]. Spacecraft Engineering,2020, 29(3): 169-176.
[15]	Deng L H, Huang Z H, Yao S K. A block-based JPEG-LS compression technique with lossless region of interest[C]∥MIPPR 2017: Parallel Processing of Images and Optimization Techniques; and Medical Imaging, Xiangyang, China, 2018: 56-64.
[16]	张皓晨. 杂光背景下星点提取与星图识别技术的研究[D]. 长春: 吉林大学仪器科学与电气工程学院, 2019.
	Zhang Hao-chen. Research on star point extraction and star map recognition technology under clutter background[D]. Changchun: College of Instrument Science and Electrical Engineering, Jilin University, 2019.
[17]	刘向增, 范立佳, 徐雪灵, 等. 星载JPEG-LS图像压缩质量评价研究[J]. 微电子学与计算机, 2021, 38(9): 45-53.
	Liu Xiang-zeng, Fan Li-jia, Xu Xue-ling, et al. Research on quality evaluation of spaceborne JPEG-LS image compression[J]. Microelectronics and Computer Science, 2021, 38(9): 45-53.

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测试图像名	JPEG-LS无损压缩	感兴趣区域星图压缩
测试图像名	JPEG-LS无损压缩	背景near=1	背景near=2
月光干扰图1	3.419	7.897	15.546
月光干扰图2	3.325	7.532	15.556
高斯噪声图1	2.032	3.262	4.411
高斯噪声图2	2.033	3.263	4.410
均匀噪声图1	2.029	3.250	4.403
均匀噪声图2	2.029	3.250	4.042
线性噪声图1	3.580	5.740	7.176
线性噪声图2	3.580	5.739	7.722

测试图像名	感兴趣区域星图压缩（near=1）		JPEG2000 （near=1）		感兴趣区域星图压缩（near=2）		JPEG2000 （near=2）
测试图像名	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
高斯噪声图1	49.405	0.992	46.534	0.983	45.145	0.975	43.806	0.967
高斯噪声图2	49.905	0.991	46.515	0.983	45.154	0.975	43.834	0.968
均匀噪声图1	49.890	0.992	46.471	0.983	45.123	0.975	43.784	0.966
均匀噪声图2	49.894	0.992	46.483	0.983	45.429	0.975	44.329	0.972
线性噪声图1	51.955	0.991	49.177	0.990	47.070	0.971	46.376	0.982
线性噪声图2	51.970	0.991	49.248	0.990	47.049	0.971	46.374	0.982
月光干扰图1	50.427	0.992	49.436	0.990	46.262	0.981	46.955	0.983
月光干扰图2	50.367	0.992	49.265	0.989	46.273	0.982	46.930	0.983