An Efficient Observer Model for Assessing Signal Detection Performance of Lossy-Compressed Images

Abstract

A technique for assessing the impact of lossy wavelet-based image compression on signal detection tasks is presented. A medical image's value is based on its ability to support clinical decisions, including detecting and diagnosing abnormalities. However, image quality of compressed images is often stated in terms of mathematical metrics such as mean square error. The presented technique provides a more suitable measure of image degradation by building on the channelized Hotelling observer model, which has been shown to predict human performance of signal detection tasks in noise-limited images. The technique first decomposes an image into its constituent wavelet subbands. Channel responses for the individual subbands are computed, combined, and processed with a Hotelling observer model to provide a measure of signal detectability versus compression ratio. This allows a user to determine how much compression can be tolerated before image detectability drops below a certain threshold.