Characterisation of image fusion quality metrics for surveillance applications over bandlimited channels

Image fusion is finding increasing application in areas such as medical imaging, remote sensing or military surveillance using sensor networks. Many of these applications demand highly compressed data combined with error resilient coding due to the characteristics of the communication channel. In this respect, JPEG2000 has many advantages over previous image coding standards. This paper evaluates and compares quality metrics for lossy compression using JPEG2000. Three representative image fusion algorithms: simple averaging, contrast pyramid and dual-tree complex wavelet transform based fusion have been considered. Numerous infrared and visible test images have been used. We compare these results with a psychophysical study where participants were asked to perform specific tasks and assess image fusion quality. The results show that there is a correlation between most of the metrics and the psychophysical evaluation. They also indicate that selection of the correct fusion method has more impact on performance than the presence of compression.