Abstract
Memory management and coding complexity are the major challenging issues of any hyperspectral image sensor. The hyperspectral image compression algorithm plays a greater role to improve the hyperspectral image sensor performance and save sensor memory. Many compression algorithms for hyperspectral images were proposed in past. The wavelet transform-based set partitioned hyperspectral image compression algorithms generate embedded output bit stream and also perform both lossy & lossless compression which makes them an ideal choice for any type of image sensor. The set portioned image compression algorithms use linked list or state table or markers to track the significance or insignificance of the block cube or coefficients. The linked lists grow with the bit rate which creates memory management issue while state tables or marker size is fixed which is not favorable with the low bit rate. In this study, a novel implementation of the set partitioned compression algorithm is proposed which employs parallel processing to reduce the coding complexity and exploits the linear indexing of the wavelet transform to track the set or coefficients to save the coding memory. The simulation results show the proposed compression algorithm 3D-BCP-ZM-SPECK reduces the coding complexity multiple folds with no need of coding memory.
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References
Bajpai S, Singh HV, Kidwai NR (2017) Feature extraction & classification of hyperspectral images using singular spectrum analysis & multinomial logistic regression classifiers. In: IEEE international conference on multimedia, signal processing and communication technologies (IMPACT) Aligarh, India, pp 97–100. https://doi.org/10.1109/MSPCT.2017.8363982
Bajpai S, Singh HV, Kidwai NR (2019) 3D modified wavelet block tree coding for hyperspectral images. Indones J Electr Eng Comput Sci (IJEECS) 15(2):1001–1008. https://doi.org/10.11591/ijeecs.v15.i2.pp1001-1008
Bajpai S, Singh HV, Kidwai NR (2019) 3D Wavelet Block Tree Coding for Hyperspectral Images. Int J Innov Technol Exploring Eng 8(6C):64–68 ISSN: 2278–3075. https://www.ijitee.org/wp-content/uploads/papers/v8i6c/F12200486C19.pdf. Accessed 28 Feb 2022.
Bajpai S, Kidwai NR, Singh HV, Singh AK (2019) Low memory block tree coding for hyperspectral images. Multimed Tools Appl 78(19):27193–27209. https://doi.org/10.1007/s11042-019-07797-6
Bajpai S, Kidwai NR, Singh HV, Singh AK (2022) A low complexity hyperspectral image compression through 3D set partitioned embedded zero block coding. Multimed Tools Appl 81(1):841–872. https://doi.org/10.1007/s11042-021-11456-0
Bilgin A, Zweig G, Marcellin MW (2000) Three-dimensional image compression with integer wavelet transforms. Appl Opt 39(11):1799–1814. https://doi.org/10.1364/AO.39.001799
Boettcher JB, Du Q, Fowler JE (2007) Hyperspectral image compression with the 3D dual-tree wavelet transform. IEEE International Geoscience and Remote Sensing Symposium: 1033-1036. https://doi.org/10.1109/IGARSS.2007.4422977
Bose S, Lala MGN, Krishna AP (2022) Photometric correction of images of visible and near-infrared bands from Chandrayaan-1 hyper-spectral imager (HySI). Earth Moon Planet 126(1):1–33. https://doi.org/10.1007/s11038-021-09544-0
Cheng KJ, Dill J (2014) Lossless to lossy dual-tree BEZW compression for hyperspectral images. IEEE Trans Geosci Remote Sens 52(9):5765–5770. https://doi.org/10.1109/TGRS.2013.2292366
Christophe E, Mailhes C, Duhamel P (2008) Hyperspectral image compression: adapting SPIHT and EZW to anisotropic 3-D wavelet coding. IEEE Trans Image Process 17(12):2334–2346. https://doi.org/10.1109/TIP.2008.2005824
Chutia D, Bhattacharyya DK, Sarma KK, Kalita R, Sudhakar S (2016) Hyperspectral remote sensing classifications: a perspective survey. Trans GIS 20(4):463–490. https://doi.org/10.1111/tgis.12164
Das S (2021) Hyperspectral image, video compression using sparse tucker tensor decomposition. IET Image Process 15(4):964–973. https://doi.org/10.1049/ipr2.12077
Datta A, Ghosh S, Ghosh A (2017) Supervised feature extraction of hyperspectral images using partitioned maximum margin criterion. IEEE Geosci Remote Sens Lett 14(1):82–86. https://doi.org/10.1109/LGRS.2016.2628078
Du Q, Fowler JE (2007) Hyperspectral image compression using JPEG2000 and principal component analysis. IEEE Geosci Remote Sens Lett 4(2):201–205. https://doi.org/10.1109/LGRS.2006.888109
Gunasheela KS, Prasantha HS (2019) Compressive sensing approach to satellite hyperspectral image compression. Inf Commun Technol Intell Syst. https://doi.org/10.1007/978-981-13-1742-2_49
Hou Y, Liu G (2007) 3D set partitioned embedded zero block coding algorithm for hyperspectral image compression. Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications. Vol. 6790: 679056. International Society for Optics and Photonics. https://doi.org/10.1117/12.750975
Hou Y, Liu G (2008). Hyperspectral image lossy-to-lossless compression using the 3D embedded Zeroblock coding alogrithm. International Workshop on Earth Observation and Remote Sensing Applications: 1-6. https://doi.org/10.1109/EORSA.2008.4620308
Hou Y, Liu G (2008) Lossy-to-lossless compression of hyperspectral image using the improved AT-3D SPIHT algorithm. Int Conf Comput Sci Softw Eng 2:963–966. https://doi.org/10.1109/CSSE.2008.1351
Jiang X, ChengKe W, YunSong L, LiBin X, JianFeng Y (2005) Compression of the multispectral image by the three-dimensional EBCOT coding algorithm. J Xidian Univ 32(4):549–554
Jiang Z, Pan WD, Shen H (2020) Spatially and spectrally concatenated neural networks for efficient lossless compression of hyperspectral imagery. J Imaging 6(6):38. https://doi.org/10.3390/jimaging6060038
Kumar Suresh R, Manimegalai P (2019) Near lossless image compression using parallel fractal texture identification. Biomedical Signal Processing and Control 58:101862. https://doi.org/10.1016/j.bspc.2020.101862
Kumar S, Chaudhuri S, Banerjee B, Ali F (2018) Onboard hyperspectral image compression using compressed sensing and deep learning. In: Proceedings of the 2018 IEEE European conference on computer vision (ECCV), Munich, Germany, pp. 30–42. https://doi.org/10.1007/978-3-030-11012-3_3
Kumar V, Singh RS, Dua Y (2022) Morphologically dilated convolutional neural network for hyperspectral image classification. Signal Process Image Commun 101:116549. https://doi.org/10.1016/j.image.2021.116549
Li R, Pan Z, Wang Y (2019) The linear prediction vector quantization for hyperspectral image compression. Multimed Tools Appl 78(9):11701–11718. https://doi.org/10.1007/s11042-018-6724-8
Mei S, Yuan X, Ji J, Zhang Y, Wan S, Du Q (2017) Hyperspectral image spatial super-resolution via 3D full convolutional neural network. Remote Sens 9(11):1139–1160. https://doi.org/10.3390/rs9111139
Mishra MK, Gupta A, John J, Shukla BP, Dennison P, Srivastava SS, Kaushik NK, Misra A, Dhar D (2019) Retrieval of atmospheric parameters and data-processing algorithms for AVIRIS-NG Indian campaign data. Curr Sci 116(7):1089–1100. https://doi.org/10.18520/cs/v116/i7/1089-1100
Mitran T, Sreenivas K, Janakirama Suresh KG, Sujatha G, Ravisankar T (2021) Spatial prediction of calcium carbonate and clay content in soils using airborne hyperspectral data. J Indian Soc Remote Sens 49:1–12. https://doi.org/10.1007/s12524-021-01415-5C
Mohan BK, Porwal A (2015) Hyperspectral image processing and analysis. Curr Sci 108(5):833–841 ISSN: 0011–3891. http://www.jstor.org/stable/24216512
Nadia Z, Lahdir M, Helbert D (2019) Support vector regressionbased 3D-wavelet texture learning for hyperspectral image compression. Vis Comput 36(7):1473–1490. https://doi.org/10.1007/s00371-019-01753-z
Nagendran R, Vasuki A (2020) Hyperspectral image compression using hybrid transform with different wavelet-based transform coding. Int J Wavelets Multiresolution Inf Process 18(01):1941008. https://doi.org/10.1142/S021969131941008X
Ngadiran R, Boussakta S, Sharif B, Bouridane A (2010) Efficient implementation of 3D listless SPECK. IEEE international conference on computer and communication engineering, pp 1–4. https://doi.org/10.1109/ICCCE.2010.5556843.
Nian Y, He M, Wan J (2013) Low-complexity compression algorithm for hyperspectral images based on distributed source coding. Math Probl Eng 9(2):224–227. https://doi.org/10.1155/2013/825673
Plaza A, Benediktsson JA, Boardman JW, Brazile J, Bruzzone L, Camps-Valls G, Chanussot J, Fauvel M, Gamba P, Gualtieri A, Marconcini M (2009) Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 113:S110–S122. https://doi.org/10.1016/j.rse.2007.07.028
Quesada-Barriuso P, Argüello F, Heras DB (2014) Computing efficiently spectral-spatial classification of hyperspectral images on commodity GPUs. In: Recent advances in knowledge-based paradigms and applications, pp 19–42. https://doi.org/10.1007/978-3-319-01649-8_2
Ramakrishnan D, Bharti R (2015) Hyperspectral remote sensing and geological applications. Curr Sci 108(5):879–891
Rucker JT, Fowler JE, Younan NH (2005) JPEG2000 coding strategies for hyperspectral data. In Proceedings. 2005 IEEE international geoscience and remote sensing symposium, Seoul, South Korea. https://doi.org/10.1109/IGARSS.2005.1526121
Rupali B (2018) Enhanced encrypted reversible data hiding algorithm with minimum distortion through homomorphic encryption. Journal of Electronic Imaging 27(2):023017. https://doi.org/10.1117/1.JEI.27.2.023017
Rupali B (2021) An improved reversible and secure patient data hiding algorithm for telemedicine applications. J Ambient Intell Humaniz Comput 12(2):2915–2929. https://doi.org/10.1007/s12652-020-02449-2
Schelkens P (2001) Multi-dimensional wavelet coding-algorithms and implementations. Ph.D dissertation, Department of Electronics and Information Processing, Vrije Universiteit Brussel, Brussels
Sharma D, Prajapati YK, Tripathi R (2018) Spectrally efficient 1.55 Tb/s Nyquist- WDM superchannel with mixed line rate approach using 27.75 Gbaud PM-QPSK and PM-16QAM. Opt Eng 57(7):076102. https://doi.org/10.1117/1.OE.57.7.076102
Sharma D, Prajapati YK, Tripathi R (2018) Success journey of coherent PM-QPSK technique with its variants: a survey. IETE Tech Rev 37(1):36–55. https://doi.org/10.1080/02564602.2018.1557569
Singh PS, Karthikeyan S (2022) Salient object detection in hyperspectral images using deep background reconstruction based anomaly detection. Remote Sensing Letters 13(2):184–195. https://doi.org/10.1080/2150704X.2021.2005270
Sudha VK, Sudhakar R (2013) 3D listless embedded block coding algorithm for compression of volumetric medical images. J Sci Ind Res 72:735–748
Tang X, Pearlman WA (2004) Lossy-to-lossless block-based compression of hyperspectral volumetric data. IEEE International Conference on Image Processing, Singapore Vol 5: 3283–3286. https://doi.org/10.1109/ICIP.2004.1421815
Tang X, Pearlman WA (2006) Three-dimensional wavelet-based compression of hyperspectral images. In: Hyperspectral data compression. Springer, Boston, pp 273–308. https://doi.org/10.1007/0-387-28600-4_10
Uddin MP, Mamun MA, Hossain MA (2021) PCA-based feature reduction for hyperspectral remote sensing image classification. IETE Tech Rev 38(4):377–396. https://doi.org/10.1080/02564602.2020.1740615
Wang Y, Rucker JT, Fowler JE (2004) Three-dimensional tarp coding for the compression of hyperspectral images. IEEE Geosci Remote Sens Lett 1(2):136–140. https://doi.org/10.1109/LGRS.2004.824762
Wang L, Bai J, Wu J, Jeon G (2015) Hyperspectral image compression based on lapped transform and Tucker decomposition. Signal Process Image Commun 36:63–69. https://doi.org/10.1016/j.image.2015.06.002
Wang X, Tao J, Shen Y, Qin M, Song C (2018) Distributed source coding of hyperspectral images based on three-dimensional wavelet. J Indian Soc Remote Sens 46(4):667–673. https://doi.org/10.1007/s12524-017-0735-1
Wei P, Yi Zou, Lu Ao (2008). A compression algorithm of hyperspectral remote sensing image based on 3-D wavelet transform and fractal. 3rd International Conference on Intelligent System and Knowledge Engineering 1: 1237–1241. https://doi.org/10.1109/ISKE.2008.4731119
Wu J, Wu Z, Wu C (2006) Lossy to lossless compressions of hyperspectral images using three-dimensional set partitioning algorithm. Opt Eng 45(2):027005. https://doi.org/10.1117/1.2173996
Xu J, Xiong Z, Li S, Zhang YQ (2001) Three-dimensional embedded subband coding with optimized truncation (3-D ESCOT). Appl Comput Harmon Anal 10(3):290–315. https://doi.org/10.1006/acha.2000.0345
Yaman D, Kumar V, Singh RS (2020) Comprehensive review of hyperspectral image compression algorithms. Opt Eng 59(9):090902. https://doi.org/10.1117/1.OE.59.9.090902
Yaman D, Kumar V, Singh RS (2021) Parallel lossless HSI compression based on RLS filter. J Parallel Distrib Comput 150:60–68. https://doi.org/10.1016/j.jpdc.2020.12.004
Yaman D, Singh RS, Parwani K, Lunagariya S, Kumar V (2021) Convolution neural network based lossy compression of hyperspectral images. Signal Process Image Commun 95:116255. https://doi.org/10.1016/j.image.2021.116255
Yang J, Li Y, Chan J, Shen Q (2017) Image fusion for spatial enhancement of hyperspectral image via pixel group based non-local sparse representation. Remote Sens 9(1):53–71. https://doi.org/10.3390/rs9010053
Zhang J, Fowler JE, Liu G (2008) Lossy-to-lossless compression of hyperspectral imagery using three-dimensional TCE and an integer KLT. IEEE Geosci Remote Sens Lett 5(4):814–818. https://doi.org/10.1109/LGRS.2008.2006571
Zhang J, Fowler JE, Du Q, Liu G (2008, July) Improvements to 3D-TARP Coding for the Compression of Hyperspectral Imagery. In: IGARSS 2008-2008 IEEE international geoscience and remote sensing symposium, Boston, MA, USA https://doi.org/10.1109/IGARSS.2008.4779161
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I am sincerely thankful to the anonymous reviewers for their critical comments and suggestions to improve the quality of the paper. The author wants to express his gratitude to Integral University, Lucknow for providing manuscript number IU/R&D/2022-MCN0001700 for the present research work.
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Bajpai, S. Low complexity image coding technique for hyperspectral image sensors. Multimed Tools Appl 82, 31233–31258 (2023). https://doi.org/10.1007/s11042-023-14738-x
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DOI: https://doi.org/10.1007/s11042-023-14738-x