Paper
15 April 1996 Evaluation of image compression using plausible nonvisually weighted image fidelity measures
Walter F. Good, Stefanie Lattner, Glenn S. Maitz
Author Affiliations +
Abstract
The current trend of using ROC style observer performance studies to evaluate image compression schemes is inefficient and has greatly limited our ability to apply image compression to radiographic images. Because of this, we are developing more efficient automated techniques for assessing the loss of image quality due to compression. The figure- of-merit (FOM) presented here is based on plausible hypotheses about what factors are important in observer performance and evaluates not only the magnitude of difference images but also their structure. This FOM should avoid the limitations of simpler measures such as mean-square-error as well as the limitations and complexity of measures based on psychovisual theory. We applied our FOM to portions of compressed mammograms that had been previously evaluated in a just-noticeable-difference study, and to CT images that had been degraded by compression at various compression ratios with the JPEG algorithm or through other processes. In general, we found that this FOM offers many advantages over other common FOMs and demonstrates the feasibility of developing efficient measures. Although no one-dimensional measure can be expected to be highly correlated to observer performance, for small levels of image degradation we expect this measure to be useful in placing limits on the loss of observer performance.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Walter F. Good, Stefanie Lattner, and Glenn S. Maitz "Evaluation of image compression using plausible nonvisually weighted image fidelity measures", Proc. SPIE 2707, Medical Imaging 1996: Image Display, (15 April 1996); https://doi.org/10.1117/12.238458
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Image compression

Image quality

Visualization

Image processing

Mammography

Image visualization

Digital imaging

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