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Low memory block tree coding for hyperspectral images

  • Shrish Bajpai
  • Naimur Rahman Kidwai
  • Harsh Vikram Singh
  • Amit Kumar SinghEmail author
Article
  • 47 Downloads

Abstract

Hyperspectral image sensors are resource constrained and have limited on-board memory. Processing of high volume hyperspectral images pose a challenge to the memory and resources of the sensor. Contemporary wavelet based image compression schemes have intensive memory requirement of which 3D-WBTC have superior coding performance due to through the exploitation of the inter sub-band & intra sub-band redundancy. This paper presents a low memory implementation of 3D-WBTC which is a listless scheme by using the fixed size state memory to keep track of block set partitioning and significance testing of wavelet coefficients of transformed hyperspectral images. Memory access time is significantly reduced due to the elimination of lists that leads to reduced complexity. Simulation results of the proposed scheme shows that proposed coder is fast and have very low memory requirement thereby making it a suitable candidate for implementation in resource constrained hyper spectral image sensor.

Keywords

Hyperspectral image compression Transform coding Wavelet transform Set partition coding scheme Low memory image coder 

Notes

References

  1. 1.
    Abousleman GP, Marcellin MW, Hunt BR (1997) Hyperspectral image compression using entropy-constrained predictive trellis coded quantization. IEEE Trans Image Process 6(4):566–573CrossRefGoogle Scholar
  2. 2.
    Aiazzi B, Baronti S, Alparone L (1999) Lossless image compression based on an enhanced fuzzy regression prediction. IEEE International Conference on Image Processing, Kobe, Japan 1:435–439Google Scholar
  3. 3.
    Alam M, Khan E, Gopal B (2012) Modified Listless Set Partitioning In Hierarchical Trees (MLS) For Memory Constrained Image Coding Applications. Current Trends in Signal Processing 2(2)Google Scholar
  4. 4.
    Álvarez-Cortés S, Amrani N, Serra-Sagristà J (2018) Low complexity regression wavelet analysis variants for hyperspectral data lossless compression. Int J Remote Sens 39(7):1971–2000CrossRefGoogle Scholar
  5. 5.
    Amigo JM, Babamoradi H, Elcoroaristizabal S (2015) Hyperspectral image analysis. A tutorial. Anal Chim Acta 896:34–51CrossRefGoogle Scholar
  6. 6.
    Bajpai S, Kidwai NR, Singh HV (2019) 3D wavelet block tree coding for hyperspectral images. International Journal of Innovative Technology and Exploring Engineering (IJITEE) 8(6C):64–68Google Scholar
  7. 7.
    Bajpai S, Singh HV, Kidwai NR (2017) Feature extraction & classification of hyperspectral images using singular spectrum analysis & multinomial logistic regression classifiers. IEEE International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), pp 97–100Google Scholar
  8. 8.
    Bruylants T, Munteanu A, Alecu A, Deklerck R, Schelkens P (2007) Volumetric image compression with JPEG2000. SPIE The International Society for Optical Engineering:1–2Google Scholar
  9. 9.
    Bruylants T, Munteanu A, Schelkens P (2015) Wavelet based volumetric medical image compression. Signal Process Image Commun 31:112–133CrossRefGoogle Scholar
  10. 10.
    Cheng KJ, Dill J (2013) Lossless to lossy compression for hyperspectral imagery based on wavelet and integer KLT transforms with 3D binary EZW. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX 8743:87430U-1–87430U10CrossRefGoogle Scholar
  11. 11.
    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):2334–2346MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Chutia D, Bhattacharyya DK, Sarma KK, Kalita R, Sudhakar S (2016) Hyperspectral remote sensing classifications: a perspective survey. Trans GIS 20(4):463–490CrossRefGoogle Scholar
  13. 13.
    Datta A, Ghosh S, Ghosh A (2018) PCA, Kernel PCA and Dimensionality Reduction in Hyperspectral Images. In: Advances in Principal Component Analysis Springer, Singapore, pp 19–46Google Scholar
  14. 14.
    Datta A, Ghosh S, Ghosh A (2019) Hyperspectral Remote Sensing Images and Supervised Feature Extraction. In: Cloud Computing for Geospatial Big Data Analytics Springer, Cham: pp. 265–289Google Scholar
  15. 15.
    Diwakar M, Kumar P, Singh AK (2018) CT image denoising using NLM and its method noise thresholding. Multimed Tools Appl:1–16Google Scholar
  16. 16.
    Dusselaar R, Paul M (2017) Hyperspectral image compression approaches: opportunities, challenges, and future directions: discussion. J Opt Soc Am A 34(12):2170–2180CrossRefGoogle Scholar
  17. 17.
    Fowler JE, Rucker JT (2007) Three-dimensional wavelet-based compression of hyperspectral imagery. Hyperspectral Data Exploitation: Theory and Applications:379–407Google Scholar
  18. 18.
    Goetz AFH (2009) Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sens Environ 113:S5–S16CrossRefGoogle Scholar
  19. 19.
    Gunasheela KS, Prasantha HS (2018) Satellite Image Compression-Detailed Survey of the Algorithms. In Proceedings of International Conference on Cognition and Recognition Springer pp, 187–198Google Scholar
  20. 20.
    HouY LG (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 6790:1–7Google Scholar
  21. 21.
    Islam A, Pearlman WA (1999) An embedded and efficient low-complexity hierarchical image coder in Visual Communications and Image Processing, K. Aizawa, R. L. Stevenson, and Y.-Q. Zhang, Eds. San Jose, CA: Proc. SPIE 3653: pp. 294–305Google Scholar
  22. 22.
    Jia S, Qian Y (2007) Spectral and spatial complexity-based hyperspectral unmixing. IEEE Trans Geosci Remote Sens 45(12):3867–3879CrossRefGoogle Scholar
  23. 23.
    Khelifi F, Kurugollu F, Bouridane A (2008) SPECK-based lossless multispectral image coding. IEEE Signal Processing Letters 15:69–72CrossRefGoogle Scholar
  24. 24.
    Kouadria N, Mechouek K, Messadeg D, Doghmane N (2017) Pruned discrete Tchebichef transform for image coding in wireless multimedia sensor networks. AEU-International Journal of Electronics and Communications 74:123–127CrossRefGoogle Scholar
  25. 25.
    Lim S, Sohn K, Lee C (2001) Compression for hyperspectral images using three dimensional wavelet transform. In: IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium 1: pp, 109–111Google Scholar
  26. 26.
    Menegaz G, Thiran JP (2002) Lossy to lossless object-based coding of 3-D MRI data. IEEE Trans Image Process 11(9):1053–1061CrossRefGoogle Scholar
  27. 27.
    Mohan BK, Porwal A (2015) Hyperspectral image processing and analysis. Curr Sci 108(5):833–841Google Scholar
  28. 28.
    Moinuddin AA, Khan E, Ghanbari M (2008) Efficient algorithm for very low bit rate embedded image coding. IET Image Process 2(2):59–71MathSciNetCrossRefGoogle Scholar
  29. 29.
    Motta G, Rizzo F, Storer JA (eds) (2006) Hyperspectral data compression. Springer Science & Business Media, BerlinzbMATHGoogle Scholar
  30. 30.
    Mrityunjaya V, Ayachit NH, Deshpande DK (2006) Reduced memory listless speck image compression. Digital Signal Processing 16(6):817–824CrossRefGoogle Scholar
  31. 31.
    Ngadiran R, Boussakta S, Sharif B, Bouridane A (2010) Efficient implementation of 3D listless SPECK. International Conference on Computer and Communication Engineering:1–4Google Scholar
  32. 32.
    Pearlman WA, Islam A, Nagaraj N, Said A (2004) Efficient, low-complexity image coding with a set-partitioning embedded block coder. IEEE Transactions on Circuits and Systems for Video Technology 14(11):1219–1235CrossRefGoogle Scholar
  33. 33.
    Pearlman WA, Kim BJ, Xiong Z (2002) Embedded Video Subband Coding with 3D SPIHT. In: Topiwala P.N. (eds) Wavelet Image and Video Compression. The International Series in Engineering and Computer Science 450:397–432CrossRefGoogle Scholar
  34. 34.
    Penna B, Tillo T, Magli E, Olmo G (2006) Progressive 3-D coding of hyperspectral images based on JPEG 2000. IEEE Geosci Remote Sens Lett 3(1):125–129CrossRefGoogle Scholar
  35. 35.
    Penna B, Tillo T, Magli E, Olmo G (2007) Transform coding techniques for lossy hyperspectral data compression. IEEE Trans Geosci Remote Sens 45(5):1408–1421CrossRefGoogle Scholar
  36. 36.
    Kidwai NR, Alam M, Khan E, Beg R (2012) A efficient memory no list set partitioned embedded block (NLSK) wavelet image coding algorithm for low memory devices. Int J Signal Process, Image Process Pattern Recognit 5(4):93–106Google Scholar
  37. 37.
    Kidwai NR, Khan E, Beg R (2012) A memory efficient listless SPECK (MLSK) image compression algorithm for low memory applications. Int J Adv Res Comput Sci 3(4):209–215Google Scholar
  38. 38.
    Kidwai NR, Khan E, Reisslein M (2016) ZM-SPECK: a fast and memoryless image coder for multimedia sensor networks. IEEE Sensors J 16(8):2575–2587Google Scholar
  39. 39.
    Rai A, Singh HV (2017) SVM based robust watermarking for enhanced medical image security. Multimed Tools Appl 76(18):18605–18618CrossRefGoogle Scholar
  40. 40.
    Senapati RK, Prasad PK, Swain G, Shankar TN (2016) Volumetric medical image compression using 3D listless embedded block partitioning. SpringerPlus 5(1):1–16CrossRefGoogle Scholar
  41. 41.
    Tang X, Pearlman WA (2006) Three-dimensional wavelet-based compression of hyperspectral images. Hyperspectral Data Compression: pp. 273–308Google Scholar
  42. 42.
    Tang X, Pearlman WA, Modestino JW (2003) Hyperspectral image compression using threedimensional wavelet coding. Image and Video Communications and Processing Proc SPIE 5022:1037–1047Google Scholar
  43. 43.
    Tausif M, Khan E, Hasan M, Reisslein M (2017) SFrWF: Segmented fractional wavelet filter based Dwt for low memory image coders. IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON) 4:593–597Google Scholar
  44. 44.
    Tausif M, Kidwai NR, Ekram K (2017) Low-memory image coder for wearable visual sensors. In: Wearable sensors applications, design and implementation, vol 10. IOP Publishing Ltd, pp 1–34Google Scholar
  45. 45.
    Tausif M, Kidwai NR, Khan E, Reisslein M (2015) FrWF-based LMBTC: memory-efficient image coding for visual sensors. IEEE Sensors J 15(11):6218–6228Google Scholar
  46. 46.
    Uthayakumar J, Vengattaraman T, Dhavachelvan P (2018) A Survey on Data Compression Techniques: From the Perspective of Data Quality, Coding Schemes, Data Type and Applications. Journal of King Saud University-Computer and Information Sciences.  https://doi.org/10.1016/j.jksuci.2018.05.006
  47. 47.
    Wang L, Ma Y, Yan J, Chang V, Zomaya AY (2018) pipsCloud: High performance cloud computing for remote sensing big data management and processing. Futur Gener Comput Syst 78:353–368CrossRefGoogle Scholar
  48. 48.
    Wang X, Tao J, Shen Y, Qin M, Song C (2018) Distributed Source Coding of Hyperspectral Images Based on Three-Dimensional Wavelet. Journal of the Indian Society of Remote Sensing 46(4):667–673CrossRefGoogle Scholar
  49. 49.
    Wheeler FW, Pearlman WA (2000) SPIHT image compression without lists, IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings, pp 1–4Google Scholar
  50. 50.
    Zhang X, Pan Z, Lu X, Hu B, Zheng X (2018) Hyperspectral image classification based on joint spectrum of spatial space and spectral space. Multimed Tools Appl 77(22):29759–29777CrossRefGoogle Scholar
  51. 51.
    Zhang L, Zhang L, Tao D, Huang X, Du B (2015) Compression of hyperspectral remote sensing images by tensor approach. Neurocomputing 147:358–363CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Shrish Bajpai
    • 1
  • Naimur Rahman Kidwai
    • 2
  • Harsh Vikram Singh
    • 3
  • Amit Kumar Singh
    • 4
    Email author
  1. 1.Electronics Engineering DepartmentDr. A. P. J. Abdul Kalam Technical UniversityLucknowIndia
  2. 2.Department of ECE, Faculty of EngineeringIntegral UniversityLucknowIndia
  3. 3.Department of Electronics EngineeringKamla Nehru Institute of Technology (KNIT)SultanpurIndia
  4. 4.Department of Computer Science and EngineeringNational Institute of Technology PatnaPatnaIndia

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