Compression induced image quality degradation in terms of NIIRS

Abstract

Image compression is an important component in modern imaging systems as the size of the raw data collected is getting larger. For example, a commercial Wide Area Motion Imagery (WAMI) image data size is over 144 Megapixels (12,000 × 12,000 pixels), and the next generation WAMI image data size will be at the level of 1.6 Giga-pixels (40,000 × 40,000 pixels). The large file size transmission is limiting performance without resorting to some compression. Lossless compression is able to preserve all the information, but has limited reduction power. On the other hand, lossy compression, which may result in very high compression ratios, suffers from information loss. In this paper, we model the compression induced information loss in terms of the National Imagery Interpretability Rating Scale or NIIRS. NIIRS is a subjective quantification of image collections widely adopted by the Geographic Information System (GIS) community. Specifically, we present the Compression Degradation Image Function Index (CoDIFI) framework that predicts the NIIRS degradation, (i.e., a decrease of NIIRS rating scale) for a given compression setting. The CoDIFI-NIIRS framework enables a user to broker the maximum compression setting while maintaining a specified NIIRS rating.