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 ha...
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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.
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