Advertisement

Image Filtering with Iterative Wavelet Transform Based Compression

  • Vikas MahorEmail author
  • Srishti Agrawal
  • Rekha Gupta
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1046)

Abstract

This paper attempts to propose a methodology to reduce the size of high definition colored images taken by the professional photographers. In this digital era as technology is advancing so fast, high definition photos are captured. But these pictures take a lot of memory space. Therefore data and image compression techniques are in great requirement. The major goal is to find computationally efficient algorithm to significantly reduce the storage size with capability to retrieve the quality of image. The proposed work effectively uses Discrete Wavelet Transform (DWT) and only low frequency part of the image is transmitted, after that further iterations are performed to increase the compression ratio. Numbers of iterations are decided by making a trade-off between compression ratio (CR) and Peak Signal to Noise Ratio (PSNR) value. Arithmetic coding is applied for further compressing the image. To improve the image quality (PSNR) further at higher iteration, filters has been applied. Switching weighted median filter and simple median filter has been studied. Analysis on different window size of median filter has also been done to achieve improved PSNR value.

Keywords

Peak Signal to Noise Ratio (PSNR) Arithmetic coding Discrete Wavelet Transform (DWT) HAAR transform Compression ratio (CR) Median filter Switching weighted median filter (SWMF) 

References

  1. 1.
    Ning, T., Huang, C.H., Jensen, J.A., Wong, V., Chan, H.: Optical emission spectrum processing using wavelet compression during wafer fabrication. IEEE Trans. Semicond. Manuf. 30(4), 380–387 (2017)CrossRefGoogle Scholar
  2. 2.
    Kekre, H.B., Natu, P., Sarode, T.: Color image compression using vector quantization and hybrid wavelet transform. Procedia Comput. Sci. 89, 778–784 (2016). Twelfth International Multi-Conference on Information ProcessingGoogle Scholar
  3. 3.
    Vijaya Kumar, C.N., Kumar, D., Anil Kumar, R.: Performance analysis of image compression using discrete wavelet transform. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 7(3), 186 (2017)Google Scholar
  4. 4.
    Rawat, S., Verma, A.K.: Survey paper on image compression techniques. Int. Res. J. Eng. Technol. 4(3) (2017)Google Scholar
  5. 5.
    Siddeq, M.M., Rodrigues, M.A.: A novel image compression algorithm for high resolution 3D reconstruction. 3D Res. 5, 7 (2014)CrossRefGoogle Scholar
  6. 6.
    Kumar, G., Brar, E.S.S., Kumar, R., Kumar, A.: A review: DWT-DCT technique and arithmetic-Huffman coding based image compression. Int. J. Eng. Manuf. 5, 20 (2015)Google Scholar
  7. 7.
    Murugan, V., Balasubramanian, R.: An efficient Gaussian noise removal image enhancement technique for gray scale images. Int. Sci. Index World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 9(3) (2015)Google Scholar
  8. 8.
    Mahor, V., Pattanaik, M.: An aging-aware reliable FinFET-based low-power 32-Word x 32-bit register file. Circ. Syst. Signal Process. 36(12), 4789–4808 (2017)CrossRefGoogle Scholar
  9. 9.
    Nair, M.S., Mol, P.A.: An efficient adaptive weighted switching median filter for removing high density impulse noise. J. Inst. Eng. India Ser. B 95, 255–278 (2014)CrossRefGoogle Scholar
  10. 10.
    Sayood, K.: Introduction to Data Compression, pp. 81–88, 473–512, 3rd edn. Elsevier, Boston (2005)CrossRefGoogle Scholar
  11. 11.
    Lo, S.-C.B., Li, H., Freedman, M.T.: Optimization of wavelet decomposition for image compression and feature preservation. IEEE Trans. Med. Imaging 22(9), 1141–1151 (2003)CrossRefGoogle Scholar
  12. 12.
    Grgic, S., Grgic, M., Zovko-Cihlar, B.: Performance analysis of image compression using wavelets. IEEE Trans. Ind. Electron. 48(3), 682–695 (2001)CrossRefGoogle Scholar
  13. 13.
    Kundu, S., Mahor, V., Gupta, R.: A highly accurate fire detection method using discriminate method. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE (2018)Google Scholar
  14. 14.
    Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5), 961–1005 (1990)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mahor, V., Pattanaik, M.: A state-of-the-art current mirror-based reliable wide fan-in FinFET domino OR gate design. Circ. Syst. Signal Process. 37(2), 475–499 (2018)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of ElectronicsMITSGwaliorIndia

Personalised recommendations