Data Compression with High Peak Signal to Noise Ratio Using Bisectional Cylindrical Wavelet Transform For a Satellite Image

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Satellite imaging is an energetic way for the analyst to review about the space information, geoscience and space report analysis. Image compression is an essential specification which provides the actual information in transmitted data on earth observation satellites. It takes advantage to reduce the capacity of space info with a unique term intention to lower the memory capacity for intelligence accumulation, moderating, data deportation content modification considering performance transition. Image compression, it helps to obtain the original image file with deficient recognition capacity. Degradation in the content of an image file confesses enhanced images which are directed towards particular storage recognition. Image compression downgrades the content of an image file without depressing the original quality. In this paper, for satellite image compression, existing wavelet transforms like continuous wavelet transform (CWT), stationary wavelet transform (SWT), data compression using 2D wavelet analysis and the proposed method Bisectional cylindrical wavelet transform (BCWT) are performed and compared with the appropriate results. Performance parameters like Peak Signal to Noise Ratio, Signal to Noise Ratio, Maximum Absolute Error, Mean Square Error, Compression Ratio, Bits per Pixel and threshold value are evaluated and tabulated.


  • Keywords

    CWT; SWT; Data compression; 2D wavelet; BCWT.

  • References

      [1] Ahmed Hagag, Emad .S. Hassan, Mohammed Amin, Fathi.E. Abd El-samie, XiaopengFan, 2017, Satellite Multispectral image compression based on remote sub-bands, optic 131(2017) 1023-1035[Elsevier].

      [2] Guo M.F and Yang N.C Dec. 2017, Features-clustering-based earth fault detection using singular-value decomposition and fuzzy C-means in resonant grounding distribution systems, International Journal of Electrical Power and Energy Systems, vol. 93, no. 1, pp. 97–108.

      [3] Haddad .S, Coatrieux .G, Cozie. M, Bouslimi.D, 2017, Joint Watermarking and Lossless JPEG – LS Compression for Medical Image Security, [Elsevier Masson] RITS 2017

      [4] Nan Jiang, Yi Zhuang, Dickson K.W. Chiu, 2017, Multiple Transmission Optimization Medical images in Recourse – Constraint Mobile Telemedicine Systems, [Elsevier], Computer Methods and Programs in Biomedicine

      [5] Karim M.Nasr, Maria G. Martini, 2017, A Visual Quality Evaluation Method for Telemedicine Applications,[Elsevier], Signal Processing: Image Communication.

      [6] Wei – Yen Hsu, 2017, Clustering – Based Compression Connected to Cloud Databases in Telemedicine and Long – Term Care Applications,[Elsevier], Telematics and Informatics 34(2017)299 – 310.

      [7] David selean, Gregor Kirbin, Izbok Kramberges, 2015, FPGA based CCSDS compliant miniaturized satellite communication stack, IFAC -48-10(2015)028-033[Elsevier].

      [8] Bouslimi D, Coatrieux G, Quantin C, Allaërt F-A, Cozic M, RouxC. A teleassistance protocol based on joint watermarking–encryption evidence for identification of liabilities in case of litigation.Science Direct IRBM 2015;36 (5):279–86

      [9] Wei – Yen Hsu, 2015, Segmentation Based Compression: New Frontiers of Telemedicine in Telecommunication, [Elsevier], Telematics and Informatics 32(2015)475 – 485.

      [10] Myint S.W, Zhu .T, B. Zheng; 2015. ―A Novel Image Classification Algorithm Using Over complete Wavelet Transforms‖; IEEE Geoscience and Remote Sensing Letters; Volume: 12 Issue: 6.

      [11] Zhang .J, Suo .Y, Mitra .S, Chin S.P, Hsiao .S, Yazicioglu R.F, Tran T.D, and Etienne-Cummings.R, "An Efficient and Compact Compressed Sensing Microsystem for Implantable Neural Recordings," IEEE Transactions on Biomedical Circuits and Systems, vol. 8, pp. 485-496, 2014.

      [12] Wang .Y, Zhou .J, Li .Z, Dong .Z, and Xu .Y, ―Discriminant-analysis based single-phase earth fault protection using improved PCA in distribution systems,‖ IEEE Transactions on Power Delivery, vol. 30, no. 4, pp. 1974–1982,Aug. 2015.

      [13] Jiang, zhang H.Y, Shen .H.F, Zhang L.P, Two-step sparse coding for the pan- sharpening of remote sensing images, IEEEJ.Sel.TopicsAppl.EarthObserv.7 (5(2014)1792–1805.

      [14] Shaeri M.A and Sodagar A.M, "A Method for Compression of Intra-Cortically-Recorded Neural Signals Dedicated to Implantable Brain-Machine Interfaces," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. PP, pp. 1-1, 2014.

      [15] Zhou .Y, Wu .T, Rastegarnia .A, Guan .C, Keefer.E, and Yang .E, "On the robustness of EC–PC spike detection method for online neural recording," Journal of Neuroscience Methods, vol. 235, pp. 316-330, 2014.

      [16] Zhixia .Z, Xiao .L, and Zailin .P, ―Fault line detection in neutral point ineffectively grounding power system based on phase-locked loop,‖ IET Generation Transmission and Distribution, vol. 8, no. 2, pp. 273–280, Feb. 2014.

      [17] McDuff .D et aI., "Remote measurement of cognitive stress via Heart rate variability," in Proceedings IEEE Conference Engineering in Medicine and Biological Society (EMBS),Chicago, IL, USA, Aug. 2014, pp. 2957-2960

      [18] Naveen .Ch, Satpute V.R, Kulat K.D, Keskar A.G,‖ Comparison of 3D-DWT Based Video Pre-Post Processing Techniques‘, Selected at World Congress on Engineering and Computer Science, San Francisco, USA, 22-24 October, 2014.

      [19] Pramit Parekh, Charusat, Robinson Macwan,Charusat, Priteshkumar Prajapati, ―Comparative Study and Analysis of Medical Image Fusion Techniques‖ International Journal of Computer Applications (0975 – 8887) Volume 90– No.19, March 2014.

      [20] Mohammed Abu-Zahhad, ―A new algorithm for the compression of ECG signals based on mother wavelet parametrization and best threshold level selection, ELSEVIER Digital Signal Processing journal,pp. 1002-1011, 2013.

      [21] Pranitha .K, Dr.G.Kavya, Literature Survey OfImage Compression/Decompression Techniques For Telehealth applications‖ in Proceedings IEEE International Conference on Photonics and High Speed Optical Networks ICPHON 2018 pp. 63-68.

      [22] Rodriguez-Perez, Ruiz-Amaya .J, Delgado-Restituto .M, and Rodriguez-Vazquez .A, "A Low-Power Programmable Neural Spike Detection Channel with Embedded Calibration and Data Compression," IEEE Transactions on Biomedical Circuits and systems, vol. 6, pp. 87-100, 2012.




Article ID: 28648
DOI: 10.14419/ijet.v7i4.6.28648

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.