Image Compression using Hybrid Wavelet Transform

Authors

  • Ch. Sree Sai Sumanth  Department. of ECE, Sri Krishnadevaraya University, Anantapur Andhra Pradesh, India
  • S. Narasimhulu  Department. of ECE, Sri Krishnadevaraya University, Anantapur Andhra Pradesh, India

Keywords:

Image compression, Lifting Scheme, DWT, Haar, AES Algorithm, Image quality measurements.

Abstract

Image Compression is used to decrease the storage of data and transmit the data easily without reducing or affecting the image quality. In this project, the hybrid wavelet transform, DWT-Lift hybrid wavelet transform is proposed for better image compression that improves the performance of the wavelet transform and implemented image security for the compressed image using the AES Algorithm. This hybrid wavelet transform gives better performance and less quantization error. This paper obtains a better compression ratio after multiple decomposition levels are held. The image can be recreated or reconstructed without losing the contents of the original image. Some IQA measurements using Peak Signal to Noise Ratio (PSNR) and Mean square error (MSE) are calculated for result performance.

References

  1. L. Yi-bo, X. Hong, and Z. Sen-Yue, “The wrinkle generation method for facial reconstruction based on extraction of partition wrinkle line features and fractal interpolation,” in Proc. 4th Int. Conf. Image Graph., Aug. 22–24, 2007, pp. 933–937.
  2. Y. Renner, J. Wei, and C. Ken, “Down sample-based multiple description coding and post-processing of decoding,” in Proc. 27th Chinese Control Conf., Jul. 16–18, 2008, pp. 253–256.
  3. H. Demirel, G. Anbarjafari, and S. Izadpanahi, “Improved motion based localized super-resolution technique using discrete wavelet transform for low-resolution video enhancement,” in Proc. 17th Eur. Signal Process. Conf., Glasgow, Scotland, Aug. 2009, pp. 1097–1101.
  4. Y. Piao, I. Shin, and H. W. Park, “Image resolution enhancement using inter-subband correlation in the wavelet domain,” in Proc. Int. Conf. Image Process., 2007, vol. 1, pp. I-445–448.
  5. H. Demirel and G. Anbarjafari, “Satellite image resolution enhancement using complex wavelet transform,” IEEE Geoscience and Remote Sensing Letter, vol. 7, no. 1, pp. 123–126, Jan. 2010.
  6. C. B. Atkins, C. A. Bouman, and J. P. Allebach, “Optimal image scaling using pixel classification,” in Proc. Int. Conf. Image Process., Oct. 7–10, 2001, vol. 3, pp. 864–867.
  7. W. K. Carey, D. B. Chuang, and S. S. Hemami, “Regularity-preserving image interpolation,” IEEE Trans. Image Process., vol. 8, no. 9, pp. 1295–1297, Sep. 1999.
  8. S. Mallat, A Wavelet Tour of Signal Processing, 2nd ed. New York: Academic, 1999.
  9. J. E. Fowler, “The redundant discrete wavelet transforms and additive noise,” Mississippi State ERC, Mississippi State University, Tech. Rep. MSSU-COE-ERC-04-04, Mar. 2004.
  10. X. Li and M. T. Orchard, “New edge-directed interpolation,” IEEE Trans. Image Process., vol. 10, no. 10, pp. 1521–1527, Oct. 2001.

Downloads

Published

2022-10-30

Issue

Section

Research Articles

How to Cite

[1]
Ch. Sree Sai Sumanth, S. Narasimhulu "Image Compression using Hybrid Wavelet Transform" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 5, pp.444-450, September-October-2022.