Effective wavelet-based compression method with adaptive quantization threshold and zerotree coding
Artur Przelaskowski , Marian Kazubek , Tomasz Jamrógiewicz
AbstractEfficient image compression technique especially for medical applications is presented. Dyadic wavelet decomposition by use of Antonini and Villasenor bank filters is followed by adaptive space-frequency quantization and zerotree-based entropy coding of wavelet coefficients. Threshold selection and uniform quantization is made an a base of spatial variance estimate built on the lowest frequency subband data set. Threshold value for each coefficient is evaluated as linear function of 9-order binary context After quantization zerotree construction, pruning and arithmetic coding is applied for efficient lossless data coding. Presented compression method is less complex than the most effective EZW-based techniques but allows to achieve comparable compression efficiency. Specifically our method has similar to SPIHT efficiency in MR image compression, slightly better for CT image and significantly better in US image compression. Thus the compression efficiency of presented method is competitive with the best published algorithms in the literature across diverse classes of medical images.
|Publication size in sheets||0.5|
|Book||Kuo Ccj., Chang Sf., Gudivada VN.: MULTIMEDIA STORAGE AND ARCHIVING SYSTEMS, Proceedings of SPIE: The International Society for Optical Engineering, vol. 3229, 1997, SPIE - The International Society for Optics and Photonics, ISBN 0-8194-2662-8|
|Keywords in English||wavelet transform; image compression; medical image archiving; adaptive quantization|
|Publication indicators||= 1; = 2; = 4.0|
|Citation count*||4 (2013-01-30)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.