LIU Xiangzeng, FAN Lijia, XU Xueling, et al. Research on quality evaluation of spaceborne JPEG-LS lmage compression algorithm[J]. 2021, 38(9): 45-53.DOI:
Research on quality evaluation of spaceborne JPEG-LS lmage compression algorithm
In order to evaluate the influence of on-board JPEG-LS compression algorithm on image quality
and provide technical support for the setting of on-orbit compression ratio
with the application of high-resolution multi-mode satellites as the background
the image compression evaluation model is built from two aspects: objective evaluation of compression quality of typical scene data and application effects of typical tasks. Through the analysis of objective indicators such as two-dimensional distortion
spectral distortion
and spatial distortion of image quality before and after compression
and comparison of typical application effects such as image classification
target detection
feature extraction and matching
which provides objective index statistical data and application effect comparison data support for the impact of compression algorithms on image quality
confirming that the algorithm can meet user needs. At the same time
it provides suggestions for users specific application needs
and supports the rational distribution and expansion of the on-orbit image data market.
关键词
Keywords
references
ZHOU L C, ZHANG C, WU M. D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Salt Lake City: IEEE, 2018: 192-196. DOI: 10.1109/CVPRW.2018.00034.
LI J, LIU Z L, TIAN S F. An efficient onboard compression method for multispectral images using distributed post-transform in the wavelet domain in conjunction with a fast spectral decorrelator[J]. Optical Review, 2019, 26(2): 247-261. DOI: 10.1007/s10043-019-00492-9.
ZHANG L, ZHANG J Y, WEI W, et al. Learning discriminative compact representation for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(10): 8276-8289. DOI: 10.1109/TGRS.2019.2919938.
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc., 2012: 1097-1105. DOI: 10.5555/2999134.2999257.
PENNA B, TILLO T, MAGLI E, et al. Transform coding techniques for lossy hyperspectral data compression[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(5): 1408-1421. DOI: 10.1109/TGRS.2007.894565.
PICKERING M R, RYAN M J. Efficient spatial-spectral compression of hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(7): 1536-1539. DOI: 10.1109/36.934084.
LI L, WANG Z S. Compression quality prediction model for JPEG2000[J]. IEEE Transactions on Image Processing, 2010, 19(2): 384-398. DOI: 10.1109/TIP.2009.2034706.
FILHO A L, D'AMORE R. A tolerant JPEG-LS image compressor foreseeing COTS FPGA implementation[J]. Microprocessors and Microsystems, 2017(49): 54-63. DOI: 10.1016/j.micpro.2017.01.008.
MIELIKAINEN J, TOIVANEN P. Clustered DPCM for the lossless compression of hyperspectral Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(12): 2943-2946. DOI: 10.1109/TGRS.2003.820885.
RABBANI M, JOSHI R, An overview of the JPEG 2000 still image compression standard[J]. Signal Processing: Image Communication, 2002, 17(1): 3-48. DOI: 10.1016/S092 3-5965(01)00024-8.
YU G X, VLADIMIROVA T, SWEETING M N. Image compression systems on board satellites[J]. Acta Astronautica, 2009, 64(9/10): 988-1005. DOI: 10.1016/j.actaastro.2008.12.006.
MIELIKAINEN J S, TOIVANEN P J, KAARNA A. Linear prediction in lossless compression of hyperspectral images[J]. Optical Engineering, 2003, 42(4): 1013-1017. DOI: 10.1117/1.1557174.
LIU X Z, AI Y F, TIAN B, et al. Robust and fast registration of infrared and visible images for Electro-Optical pod[J]. IEEE Transactions on Industrial Electronics, 2019, 66(2): 1335-1344. DOI: 10.1109/ TIE.2018.2833051.