Medical image compression using vector quantization and system error compression

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dc.contributor.authorPhanprasit, Tanasak
dc.contributor.authorHamamoto, Kazuhiko
dc.contributor.authorSangworasil, Manas
dc.contributor.authorPintavirooj, Chuchart
dc.date.accessioned2026-08-06T10:12:06Z
dc.date.available2026-08-06T10:12:06Z
dc.date.issued2015-09-01
dc.description.abstractA novel medical image compression scheme based on vector quantization (VQ) is proposed in this paper. The advantages of the technique are not only that it yields high compression ratio but also that it maintains a peak signal-to-noise ratio (PSNR). This new method involves three steps. First, we present a codebook design using discrete wavelet transform (DWT), fuzzy C-means (FCM), and support vector machine (SVM) algorithms. Second, we improve the bit rate using the Huffman coding theme as a method of eliminating the redundant index. Finally, we supplement the system with error compensation to improve the PSNR. With the proposed method, we are able to achieve a bit rate improvement of 24.00% and a PSNR of 10.96% over the conventional method.
dc.identifier.citationIeej Transactions on Electrical and Electronic Engineering, 10(5), 554-566, 2015
dc.identifier.doi10.1002/tee.22119
dc.identifier.issn19314973
dc.identifier.other2-s2.0-84937737476
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/6391
dc.sourceIeej Transactions on Electrical and Electronic Engineering
dc.subjectDiscrete wavelet transform
dc.subjectFuzzy C-means
dc.subjectHuffman coding
dc.subjectSupport vector machine
dc.subjectVector quantization
dc.titleMedical image compression using vector quantization and system error compression
dc.typeArticle

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