Abstract
Communication of the medical image and videos has now raised as a vital concern for the telediagnosis of critical diseases. Currently, JPEG and JPEG2k codecs are the default compression tool to facilitate their communication over band-limited channels. However, most often, the performance of these existing codecs is found poor particularly at the higher compression levels. Hence, this paper presents a new medical image compression codec to achieve high-quality compression of the medical images, especially at the higher compression levels. The proposed codec utilizes Discrete Orthogonal Cosine Stockwell Transform (DOCST) for the higher pixel decorrelation and the optimal integer bit allocation based quantization strategy for the efficient quantization of the DOCST coefficients. Further, to justify and validate the performance of the proposed codec an extensive performance analysis has been presented for six medical images of two different modalities. It is reported that the proposed codec outperforms the existing JPEG and JPEG2k codecs with significant quality gain for all the compression levels.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Huang, H.K.: PACS and Imaging Informatics: Basic Principles and Applications. Wiley, New York (2010)
Jayaraman, S., Esakkirajan, S., Veerakumar, T.: Digital Image Processing, 1st edn. Tata McGraw Hill Education, New Delhi (2009)
Wallace, G.K.: The JPEG still picture compression standard. Commun. ACM 34, 30–44 (1991)
Thakur, V.S., Thakur, K.: Design and implementation of a highly efficient grey image compression codec using fuzzy based soft hybrid JPEG standard. In: International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC), pp. 484–489, January 2014
Thakur, V.S., Dewangan, N.K., Thakur, K.: A highly efficient grey image compression codec using neuro-fuzzy based soft hybrid JPEG standard. In: 2nd International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA), vol. 1, pp. 625–631 (2014)
Thakur, V.S., Gupta, S., Thakur, K.: Optimum global thresholding based variable block size DCT coding for efficient image compression. Biomed. Pharmacol. J. 8(1), 453–468 (2015)
Christopoulos, C., Skodras, A., Ebrahimi, T.: The JPEG2000 still image coding system: an overview. IEEE Trans. Consum. Electron. 46, 1103–1127 (2000)
Thakur, V.S., Gupta, S., Thakur, K.: Perceptive performance analysis of Discrete Orthogonal Cosine Stockwell Transform for low bit-rate image compression. In: International Conference on Recent Trends in Engineering, Science and Technology (ICRTEST 2016), no. 2, pp. 251–257 (2016)
Ladan, J., Vrscay, E.R.: The Discrete Orthonormal Stockwell Transform and Variations, with applications to image compression. In: Kamel, M., Campilho, A. (eds.) ICIAR 2013. LNCS, vol. 7950, pp. 235–244. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39094-4_27
Sayood, K.: Introduction to Data Compression. Morgan Kaufman, San Francisco (1996)
Fung, H.T., Ranker, K.J.: Design of image-adaptive quantization tables for JPEG. J. Electron. Imaging 4(2), 144–150 (1995)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB, 2nd edn. Prentice-Hall, Upper Saddle River (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Thakur, V.S., Thakur, K., Gupta, S. (2017). High-Quality Medical Image Compression Using Discrete Orthogonal Cosine Stockwell Transform and Optimal Integer Bit Allocated Quantization. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-71928-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71927-6
Online ISBN: 978-3-319-71928-3
eBook Packages: Computer ScienceComputer Science (R0)