Task-Speci c Image Quality Metrics for Lossy Compression of FLIR Images

In this paper we evaluate two lossy compression methods, JPEG and SPIHT, using a metric computation system which evaluates the distortion eeects of lossy compression as it aaects the task of target segmentation with morphological ltering. Using Forward Looking Infrared Radar (FLIR) images to develop the approach, our goal is to contrast the results of the identical tasks with and without compression using traditional metrics, local/target area traditional metrics, and binary metrics applied to the component representing the selected target mask. Thus, our metrics are speciically targeted to measure the degree of invariance of the processing to the presence of an initial compression-decompression step. The two segmentation methods used are the fuzzy c-means and the median cut. The results indicate that even though SPIHT is better than JPEG, this is not always the case in terms of the binary metrics. In addition, the fuzzy c-means segmentation method is better than the median cut in most cases. Another interesting eeect observed is that small changes in traditional metrics can sometimes lead to a drastic change in the task-speciic metrics.