Title: A near-lossless approach for medical image compression using visual quantisation and block-based DPCM

Authors: Marykutty Cyriac; C. Chellamuthu

Addresses: Department of ECE, Jerusalem College of Engineering, Pallikaranai, Chennai 600100, India ' Department of EEE, RMK Engineering College, R.S.M. Nagar, Kavaraipettai, Gummidipoondi Taluk, Tiruvallur Dt, Tamil Nadu 601 206, India

Abstract: For the efficient transmission of medical image datasets over the internet, compression techniques which offer high compression ratio and less computational cost are required. Since the existing lossless techniques have low compression ratios, the alternative approach is to employ near-lossless methods. The existing near-lossless methods like JPEG-LS are context based and computationally intensive. In this paper, a new approach for near-lossless compression of the medical images is proposed. Pre-processing techniques are applied to the input image to generate a visually quantised image. The visually quantised image is encoded using a low complexity block-based lossless differential pulse code modulation coder, followed by the Huffman entropy encoder. The compression performance is compared with the state-of-the-art technique context-based adaptive lossless image coding, by using the objective analysis parameter peak signal to noise ratio and the image fidelity measurement parameter visual signal to noise ratio. Results show the superiority of the proposed technique in terms of the bit rate and visual quality.

Keywords: differential pulse code modulation; block-based DPCM; near-lossless method; visual quantisation; medical image compression; context-based adaptive lossless image coding; histogram; CALIC; Huffman coding; JPEG-LS; predictor; image quality; visual SNR; signal to noise ratio; VSNR; medical imaging; bit rate.

DOI: 10.1504/IJBET.2013.057711

International Journal of Biomedical Engineering and Technology, 2013 Vol.13 No.1, pp.17 - 29

Received: 10 Apr 2013
Accepted: 10 Sep 2013
Published online: 27 Sep 2014 *

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