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.
An eMclent suhhand implementation of the channelized Hotelling observer is presented, whieh eau be used to assess the image quality of Images eompressed with waveletbased techniques. The channelized Hotelling model observer has heen shown to prediet human performanee lo detecting signals in noise-limited images. The model observer can also prediet degradation of human performance due to lossy compressed images. This provides a more relevant image quality assessment for medical images, where the image's value Is in supporting clinical decisions, than metrics sueh as mean square error. The suhhand implementation shown is unique in that It operates on ehannel responses of the wavelet suhhands rather than on the entire image itself. The technique is extendable to operate on the ehannel response of the subhand hitmaps, whieh would permit hit ordering optimized for human Derformanee.
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