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Energy Interpolated Mapping for Image Compression with Hierarchical Coding

Affiliations

  • Electronics and Communications Engineering Department, SSJ Engineering College, Hyderabad-500075, Telangana,, India
  • Electronics and Communications Engineering Department, CMR Engineering College, Hyderabad – 501401, Telangana, Hyderabad,, India

Abstract


Objectives: To achieve high compression rate by reducing the more redundant information in medical images. More compression rate results in less resource utilization and also reduces the processing overhead and time consumption. Methods/Statistical Analysis: In this approach, initially the medical image was decomposed through multiwavelet transform. Then a band selection procedure is performed on the obtained sub bands to select the bands which are noncorrelated. Thus, the redundant information existing in the bands will be reduced. Then, the selected bands were processed for energy based interpolation to select the features which are more informative and also to reduce the redundant information further. Next, the hierarchical coding was applied over the obtained features. Findings: Simulation results are carried out over various medical images and for every image, the quality was checked through PSNR and the performance was checked through processing overhead and computation time (sec). Compared with earlier approaches, the processing overhead of proposed approach observed to be less and the computation time also. Similar, the PSNR is observed to be high and MSE as low. Applications/Improvements: The proposed medical image coding system will be used in telemedicine applications where there is a need of efficient resource utilization to transmit the data with fewer resources.

Keywords

Band Selection, Computation Time, Energy Interpolated Features, Hierarchical Coding, Medical Image Compression, , PSNR, Processing Overhead,

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References


  • Jagdeesh S, Nagabhooshanam E. Multi Spectral Band Selective Coding for Medical Image Compression.Global Journal of Computer Science and Technology: F Graphics and Vision. 2015 Mar; 15(1):1205–15.
  • Pearlman AS. A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits System Video Technology. 1996; 6(3):243–49.Available from: https://doi.org/10.1109/76.499834
  • Taubman D. High Performance scalable image compression with EBCOT. IEEE Transactions on Image Processing.2000; 9(7):1158–70. Available from: https://doi.org/10.1109 /83.847830PMid:18262955
  • Rabbani M, Joshi R. An overview of the JPEG 2000 still image compression standard. Signal Processing: Image Communication. 2002; 7(1):3–48. Available from: https:// doi.org/10.1016/S0923-5965(01)00024-8
  • Hsiang ST, Woods JW. Embedded image coding using zero blocks and of subband/wavelet coefficients and context modeling. IEEEcs06. 2000 May; 13(5):662–5.
  • Shapiro JM. Embedded image coding using zero trees of wavelet coefficients. IEEE Transactions on signal processing. 1993 Dec; 41:3445–62. https://doi.org/10.1109/78.258085
  • Wallace GK. The JPEG still picture compression standard.Communications of the ACM. 1991 Apr; 30(5):30–44.Available from: https://doi.org/10.1145/103085.103089
  • Pennebaker WP, Mitchell JL. JPEG Still Image Data Compression Standard 1st ed. Van Nostrand Reinhold.New York.1992.
  • Woods J. ed. Subband Image Coding. Kluwer Academic Publishers. Canada, 1991.
  • VetterliM, Kovacevic J. Wavelets and Subband Coding.Englewood Cliffs.NJ: Prentice-Hall. 1995.
  • Antonini M, Barlaud M, Mathieu I. DaubechiesImage coding using wavelet transform. IEEE Transactions on Image Processing. 1992 Apr; 20(1):205–20. Available from: https://doi.org/10.1109/83.136597 PMid:18296155
  • Hsiang ST, Woods JW. Highly scalable and perceptually tuned embedded subband/wavelet image
  • coding. SPIE Conference on Visual Communications and Image Processing. 2002 Jan; 46(7):1153–64.
  • Available from: https://doi.org/10.1117/12.453039
  • Jagadeesh S, Nagabhooshanam E. Scalable Identity Wavelet in Hierarchical Image Codec.IOSR Journal of VLSI and SignalProcessing (IOSR-JVSP).2016 Dec; 6(6):1–12.
  • Memon AP, Uqaili MA, Memon ZA, Ali AA, Zafar A. Combined Novel Approach of DWT and Feed-forward MLP-RBF Network for the Classification of Power Signal Waveform Distortion. Indian Journal of Science and Technology.2014 Jan; 7(5):1–3.
  • Modi TM, Anilkumar PH, Alex JSR. Low Complexity DWT Architecture Implementation for Feature Extraction using Different Multipliers. Indian Journal of Science and Technology. 2015 Sep; 8(21):1–7.
  • Indira KP, Hemamalini RR. Evaluation of Choose Max and Contrast based Fusion Rule Using DWT for PET, CT Images.Indian Journal of Science and Technology. 2015 Jul; 8(16):1–4.https://doi.org/10.17485/ijst/2015/v8i16/74556
  • Patel R. Lossless DWT Image Compression using Parallel Processing. Indian Journal of Science and Technology. 2016 Aug; 9(29):1–4.
  • Khemiri R, Sayadi FE, Atri M. MatLab-GPU-based 2D-DWT Acceleration for JPEG2000 with Single and Double-Precision. Indian Journal of Science and Technology. 2016 Mar; 9(12):1–7.
  • Jhingan A, Kansal L. Performance Assessment of OFDM Utilizing FFT/DWT over Rician Channel Effected by CFO.Indian Journal of Science and Technology. 2016 Mar; 9(12):1–7.

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