Optimization of block size for DCT-based medical image compression

In view of the increasing importance of medical imaging in healthcare and the large amount of image data to be transmitted/stored, the need for development of an efficient medical image compression method, which would preserve the critical diagnostic information at higher compression, is growing. Discrete cosine transform (DCT) is a popular transform used in many practical image/video compression systems because of its high compression performance and good computational efficiency. As the computational burden of full frame DCT would be heavy, the image is usually divided into non-overlapping sub-images, or blocks, for processing. This paper aims to identify the optimum size of the block, in reference to compression of CT, ultrasound and X-ray images. Three conflicting requirements are considered, namely processing time, compression ratio and the quality of the reconstructed image. The quantitative comparison of various block sizes has been carried out on the basis of benefit-to-cost ratio (BCR) and reconstruction quality score (RQS). Experimental results are presented that verify the optimality of the 16 × 16 block size.

[1]  Francisco del Pozo,et al.  Rural telemedicine for primary healthcare in developing countries , 2004, IEEE Technology and Society Magazine.

[2]  Shen-Chuan Tai,et al.  Medical image compression by discrete cosine transform spectral similarity strategy , 2001, IEEE Transactions on Information Technology in Biomedicine.

[3]  Shen-Chuan Tai,et al.  Embedded medical image compression using DCT based subband decomposition and modified SPIHT data organization , 2004, Proceedings. Fourth IEEE Symposium on Bioinformatics and Bioengineering.

[4]  W. B. Mikhael,et al.  A mixed transform approach for efficient compression of medical images , 1996, IEEE Trans. Medical Imaging.

[5]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[6]  Ian H. Witten,et al.  Arithmetic coding for data compression , 1987, CACM.

[7]  Anil K. Jain Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[8]  Bing Zeng,et al.  Reduction of blocking effect in DCT-coded images using zero-masking techniques , 1999, Signal Process..

[9]  D. Pearson,et al.  Transform coding of images using interleaved blocks , 1984 .

[10]  Pamela C. Cosman,et al.  Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy , 1994, Proc. IEEE.

[11]  H. K. Huang,et al.  Radiologic image compression-a review , 1995, Proc. IEEE.

[12]  Anil K. Jain,et al.  Image data compression: A review , 1981, Proceedings of the IEEE.

[13]  Jae S. Lim,et al.  Reduction of blocking effect in image coding , 1983, ICASSP.

[14]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[15]  Shen-Chuan Tai,et al.  An adaptive 3-D discrete cosine transform coder for medical image compression , 2000, IEEE Transactions on Information Technology in Biomedicine.

[16]  J. H. Peters,et al.  Reversible intraframe compression of medical images. , 1988, IEEE transactions on medical imaging.

[17]  Murray H. Loew,et al.  A new approach to reduce the 'blocking effect' of transform coding [image coding] , 1993, IEEE Trans. Commun..

[18]  A. S. Tolba Wavelet Packet Compression of Medical Images , 2002, Digit. Signal Process..

[19]  Yung-Gi Wu,et al.  Medical image compression by sampling DCT coefficients , 2002, IEEE Trans. Inf. Technol. Biomed..

[20]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .