The International Journal of Cardiac Imaging

, Volume 11, Issue 3, pp 151–162 | Cite as

Objective methods for optimizing JPEG compression of coronary angiographic images

  • Dietrich G. W. Onnasch
  • Guido P. M. Prause
  • Andreas Plöger


Digital angiographic images contain a significant amount of redundancy as well as some irrelevant information and noise. Therefore, it is possible to reduce the number of bits required to represent an image considerably. The lossy JPEG standard may be used provided that no significant diagnostic information is lost. As implemented in presently available hard- and software in most cases the luminance quantization table (LQT) is applied for gray level images, which may only be scaled by a so-called quality factor. The questions arise whether it is possible and worthwhile to specify quantization tables for the particular characteristics of angiograms.

To assess the quality performance quantitatively, global numerical quality measures and evaluations based on Hosaka-plots were performed. Those diagrams compare the errors introduced into areas of different local activity. By the newly introduced weighting of these errors with the relative occupancy of the respective classes of activity the results got more reproducible. The blocking and blurring effects introduced by lossy JPEG compression could be compared objectively.

Two new quantization tables were derived from the transfer function of the angiographic X-ray system, the modulation transfer quantization table (MTQT) and the star pattern quantization table (SPQT). Both tables guarantee that the blurring of sharp edges is minimized so that no deterioration around a coronary lesion occurs. Based on the signal-to-noise ratio, the overall quality performance is the same as for theLQT. A general relation between the bit rate of the compressed image and the quality factor has been determined for images of high local activity and normally scaled coronary angiographic images (512 × 512).

Key words

digital imaging image compression image processing coronary angiograms modulation transfer function JPEG compression image quality measures quantization table design Hosaka-plot 


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  1. 1.
    MacMahon H, Doi K, Sanada S et al. Data compression: Effects on diagnostic accuracy in digital chest radiography. Radiology 1991; 176: 175–9.Google Scholar
  2. 2.
    Fritsch JP, Brennecke R, Lang M, Renneisen U, Haude M, Koch L, Erbel R, Meyer J. New techniques for visualization of losses due to image compression in grayscale medical still images. IEEE Comp Soc Press, Los Alamitos: Computers in Cardiology 1992: 271–3.Google Scholar
  3. 3.
    ISO/IEC/JTC 1/SC2. Digital compression and coding of continuous-tone still images, 1991.Google Scholar
  4. 4.
    Rabbani M, Jones PW. Digital image compression techniques. Bellingham, Washington: SPIE Optical Engineering Press, 1991.Google Scholar
  5. 5.
    Lohscheller H. A subjectively adapted image communication system. IEEE Trans Comm: Com 32(12), 1984: 1316–22.Google Scholar
  6. 6.
    Lane TG, Gladstone P, Ortiz L et al. IndependentJPEG group's free JPEG software. Release 4, 1992.Google Scholar
  7. 7.
    Krause S. Untersuchung der Einsatzmöglichkeiten des JPEG-Kompressionsverfahrens für angiokardiographische Bilder [Diplomarbeit]. Kiel (Germany): Inst Informatik, University of Kiel, 1993.Google Scholar
  8. 8.
    Farelle PM. Recursive block coding for image data compression. New York, Berlin: Springer-Verlag, 1990.Google Scholar
  9. 9.
    Hosaka K. A new picture quality evaluation method. Proc Intern Picture Coding Symposium, Tokyo, April 1986. PCS 1986; 86: 17–8.Google Scholar
  10. 10.
    Onnasch DGW, Prause GPM, Plöger A. Quantization table design for JPEG compression of angiocardiographic images. Press, Los Alamitos: Computers in Cardiology 1994; 265–8.Google Scholar
  11. 11.
    Wintz PA. Transform picture coding. Proceedings of the IEEE 60(7), 1972: 814–5.Google Scholar
  12. 12.
    Onnasch DGW, Zhou X, Prause GPM. JPEG quantization table design for angiographic image compression. 5th Intern Sym Coronary arteriography, Rotterdam June 28–30, 1993.Google Scholar
  13. 13.
    Sperlbaum U, Prause GPM, Onnasch DGW. Qualitäts-sicherung in der digitalen Angiographie: Modulationsübertragungsfunktion und Wiener Spektrum. Biomedizinische Technik, Hanover, Poster P4, 1992, Biomedical Engineering 37 (Supplement) 1992; 27.Google Scholar
  14. 14.
    Riedel CH, Onnasch DGW, Prause GPM. Bestimmungen von Modulationstransferfunktionen der Komponenten digitaler Röntgensysteme durch Subsamplingmethoden. Biomedical Engineering 39 (Supplement) 1994; 75–6.Google Scholar
  15. 15.
    Fritsch JP, Negwer F, Renneisen U, Brennecke R, Meyer J. Visual and quantitative analysis of coronary angiograms after irreversible data compression. Europ Heart J (Abstract Supplement) 1994; 443.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Dietrich G. W. Onnasch
    • 1
  • Guido P. M. Prause
    • 1
  • Andreas Plöger
    • 1
  1. 1.Clinic for Pediatric Cardiology, Biomedical EngineeringUniversity of KielGermany

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