<title>Perceptually optimized compression of mammograms</title>

Abstract

The Sarnoff JNDmetrix visual discrimination model (VDM) was applied to predict the visibility of compression artifacts in mammographic images. Sections of digitized mammograms were subjected to irreversible (lossy) JPEG and JPEG 2000 compression. The detectability of compressed images was measured experimentally and compared with VDM metrics and PSNR for the same images. Artifacts produced by JPEG 2000 compression were generally easier for observers to detect than those produced by JPEG encoding at the same compression ratio. Detection thresholds occurred at JPEG 2000 compression ratios from 6:1 to 10:1, significantly higher than the average 2:1 ratio obtained for reversible (lossless) compression. VDM predictions of artifact visibility were highly correlated with observer performance for both encoding techniques. Performance was less correlated with encoder bit rate and PSNR, which was a relatively poor predictor of threshold bit rate across images. Our results indicate that the VDM can be used to predict the visibility of compression artifacts and guide the selection of encoder bit rate for individual images to maintain artifact visibility below a specified threshold.