Premium
Medical image compression using vector quantization and system error compression
Author(s) -
Phanprasit Tanasak,
Hamamoto Kazuhiko,
Sangworasil Manas,
Pintavirooj Chuchart
Publication year - 2015
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22119
Subject(s) - codebook , vector quantization , huffman coding , peak signal to noise ratio , image compression , computer science , linde–buzo–gray algorithm , compression ratio , quantization (signal processing) , artificial intelligence , data compression ratio , data compression , algorithm , discrete wavelet transform , wavelet transform , pattern recognition (psychology) , wavelet , image (mathematics) , image processing , engineering , internal combustion engine , automotive engineering
A 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. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.