An Effective Lossy Color Image Compression using Multi Transforms

I. Murali Krishna *, Challa Narsimham **, A. S. N. Chakravarthy ***
* Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.
** Vignan's Institute of Information Technology, Vishakhapatnam, Andhra Pradesh, India.
*** Department of Computer Science and Engineering, University College of Engineering, Vizayanagaram, Andhra Pradesh, India.
Periodicity:January - March'2021
DOI : https://doi.org/10.26634/jip.8.1.17875

Abstract

Rapidly growing use of multimedia products in diagnosis is affected by insufficient network and computer storage, as most of these applications use images of large data size. Compression is also important for reducing the size of images, particularly at lower bit rates, and it helps to avoid the use of additional memory and bandwidth in cloud storage. Image compression is classified into two types: lossy compression and lossless compression. The lossy compression technique can be used for medical image diagnosis while maintaining decoded image quality and achieving a high compression ratio, thus increasing device efficiency and reducing network bandwidth for transmission. In this paper, we propose a novel lossy colour image compression approach based on multi transforms such as DWT, SWT, and curvelets. The proposed work focused on an image compression technique based on DWT interpolation of high frequency sub-bands, correction of high frequency sub-band estimation using SWT high frequency sub-band and curvelets and comparison of the resulting images to current lossy compression techniques. Multi transforms compressed images have a high compression rate while maintaining good image quality.

Keywords

DWT, SWT, Curvelets, Cycle Spinning, Sieving, Selective Threshold.

How to Cite this Article?

Krishna, I. M., Narsimham, C., and Chakravarthy, A. S. N. (2021). An Effective Lossy Color Image Compression using Multi Transforms. i-manager's Journal on Image Processing, 8(1), 12-19. https://doi.org/10.26634/jip.8.1.17875

References

[1]. Bruylants, T., Munteanu, A., & Schelkens, P. (2015). Wavelet based volumetric medical image compression. Signal Processing: Image Communication, 31, 112-133. https://doi.org/10.1016/j.image.2014.12.0070923-5965/ 2014
[2]. Cabeen, K., & Gent, K. (n.d.). Image compression and the discrete cosine transform (Report), College of the Redwoods. Retrieved from https://www.math.cuhk.edu. hk/~lmlui/dct.pdf
[3]. Chen, L. A., Ding, J. J., & Lee, Y. C. (2016, December). Shape-adaptive image compression using lossy shape coding, SA-prediction, and SA-deblocking. In 2016, Asia- Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) (pp. 1-10). IEEE.
[4]. Chen, Y. T., & Tseng, D. C. (2007). Wavelet-based medical image compression with adaptive prediction. Computerized Medical Imaging and Graphics, 31(1), 1-8.
[5]. Douak, F., Benzid, R., & Benoudjit, N. (2011). Color image compression algorithm based on the DCT transform combined to an adaptive block scanning. AEUInternational Journal of Electronics and Communications, 65(1), 16-26. https://doi.org/10.1016/j.aeue.2010.03.003
[6]. Gajanan, S. (2011). Comparative study of DCT, DWT and hybrid techniques for image compression. In 1st International Conference on Emerging Trends in Computer and Image Processing.
[7]. Grandhe, P., Reddy, S, E., & Vasumathi, D. (2017). Fusion based integrated advance magnetic visualization of MRI 3D images using advance matlab tools. i-manager's Journal on Digital Signal Processing, 5(3), 27-39. https://doi.org/10.26 634/jdp.5.3.13932
[8]. Halder, A., Kundu, A., Sarkar, A., & Palodhi, K. (2019). A memory-efficient image compression method using DWT applied to histogram-based block optimization. In Emerging Technologies in Data Mining and Information Security (pp. 287-295). Singapore: Springer. https://doi.org/ 10.1007/978-981-13-1501-5_25
[9]. Kadri, O., & Baarir, Z. E. (2016). Still image compression using curvelets and logarithmic scalar quantization technique. In 2016, 5th International Conference on Multimedia Computing and Systems (ICMCS) (pp. 645- 649). IEEE. https://doi.org/10.1109/ICMCS.2016.7905667
[10]. Kaur, N., & Verma, D. (2013). Image compression using digital curvelet transform and HWT as MCA. International Journal of Computer Applications, 79(12), 46-50.
[11]. Li, S., Yin, H., Fang, X., & Lu, H. (2017). Lossless image compression algorithm and hardware architecture for bandwidth reduction of external memory. IET Image Processing, 11(6), 379-388. https://doi.org/10.1049/iet-ipr. 2016.0636
[12]. Liu, F., Hernandez-Cabronero, M., Sanchez, V., Marcellin, M. W., & Bilgin, A. (2017). The current role of image compression standards in medical imaging. Information, 8(4), 131-157. https://doi.org/10.3390/info80 40131
[13]. Liu, H., Wang, Z., & Narayan, R. (2017). Perceptual quality assessment of medical images. Encyclopedia of Biomedical Engineering. The Netherlands, Amsterdam: Elsevier.
[14]. Logeswaran, R. (2008). Compression of medical images for tele-radiology. In Teleradiology (pp. 21-31). Heidelberg, Berlin: Springer. https://doi.org/10.1007/978-3- 540-78871-3_3
[15]. Rebelo, M. S., Furuie, S. S., Munhoz, A. C., Moura, L., & Melo, C. P. (1993). Lossy compression in nuclear medicine images. In Proceedings of the Annual Symposium on Computer Application in Medical Care (p. 824). American Medical Informatics Association.
[16]. Wang, J., & Huang, K. (1996). Medical image compression by using three-dimensional wavelet transformation. IEEE Transactions on Medical Imaging, 15(4), 547-554. https://doi.org/10.1109/42.511757
[17]. Weinberger, M. J., Seroussi, G., & Sapiro, G. (2000). The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS. IEEE Transactions on Image Processing, 9(8), 1309-1324. https://doi.org/10.1109/83.855427
[18]. Wu, X., &Memon, N. (1997). Context-based, adaptive, lossless image coding. IEEE Transactions on Communications, 45(4), 437-444. https://doi.org/10.1109/ 26.585919
[19]. Zhou, D., Zhou, J., He, X., Zhu, J., Kong, J., Liu, P., & Goto, S. (2011). A 530 Mpixels/s 4096x2160@60fps high profile video decoder chip. IEEE Journal of Solid-State Circuits, 46(4), 777-788. https://doi.org/10.1109/JSSC. 2011.2109550
[20]. Zukoski, M. J., Boult, T., & Iyriboz, T. (2006). A novel approach to medical image compression. International Journal of Bioinformatics Research and Applications, 2(1), 89-103. https://doi.org/10.1504/IJBRA.2006.009195
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.