An efficient implementation of the channelized Hotelling observer for task-based assessment of lossy compressed images

An efficient subband implementation of the channelized Hotelling observer is presented, which can be used to assess the image quality of images compressed with wavelet-based techniques. The channelized Hotelling model observer has been shown to predict human performance in detecting signals in noise-limited images. The model observer can also predict 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 such as mean square error. The subband implementation shown is unique in that it operates on channel responses of the wavelet subbands rather than on the entire image itself. The technique is extendable to operate on the channel response of the subband bitmaps, which would permit bit ordering optimized for human performance.