A comparison of wavelet and Joint Photographic Experts Group lossy compression methods applied to medical images

J Digit Imaging. 1999 May;12(2 Suppl 1):14-7. doi: 10.1007/BF03168745.


This presentation focuses on the quantitative comparison of three lossy compression methods applied to a variety of 12-bit medical images. One Joint Photographic Exports Group (JPEG) and two wavelet algorithms were used on a population of 60 images. The medical images were obtained in Digital Imaging and Communications in Medicine (DICOM) file format and ranged in matrix size from 256 x 256 (magnetic resonance [MR]) to 2,560 x 2,048 (computed radiography [CR], digital radiography [DR], etc). The algorithms were applied to each image at multiple levels of compression such that comparable compressed file sizes were obtained at each level. Each compressed image was then decompressed and quantitative analysis was performed to compare each compressed-then-decompressed image with its corresponding original image. The statistical measures computed were sum of absolute differences, sum of squared differences, and peak signal-to-noise ratio (PSNR). Our results verify other research studies which show that wavelet compression yields better compression quality at constant compressed file sizes compared with JPEG. The DICOM standard does not yet include wavelet as a recognized lossy compression standard. For implementers and users to adopt wavelet technology as part of their image management and communication installations, there has to be significant differences in quality and compressibility compared with JPEG to justify expensive software licenses and the introduction of proprietary elements in the standard. Our study shows that different wavelet implementations vary in their capacity to differentiate themselves from the old, established lossy JPEG.

Publication types

  • Comparative Study

MeSH terms

  • Algorithms
  • Diagnostic Imaging*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging
  • Radiographic Image Enhancement
  • Signal Processing, Computer-Assisted
  • Software
  • Tomography, X-Ray Computed