Predicting Interpretability Loss in Thermal IR Imagery due to Compression

Analysis of thermal Infrared (IR) imagery is critical to many law enforcement and military missions, particularly for operations at night or in low-light conditions. Transmitting the imagery data from the sensor to the operator often relies on limited bandwidth channels, leading to information loss. This paper develops a method, known as the Compression Degradation Image Function Index (CoDIFI) framework, that predicts the degradation in interpretability associated with the specific image compression method and level of compression. Quantification of the image interpretability relies on the National Imagery Interpretability Ratings Scale (NIIRS). Building on previously reported development and validation of CoDIFI operating on electro-optical (EO) imagery collected in the visible region, this paper extends CoDIFI to imagery collected in the mid-wave infrared (MWIR) region, approximately 3 to 5 microns. For the infrared imagery application, the IR NIIRS is the standard for quantifying image interpretability and the prediction model rests on the general image quality equation (GIQE). A prediction model using the CoDIFI for IR imagery is established with empirical validation. By leveraging the CoDIFI in operational settings, mission success ensures that the compression selection is achievable in terms of the NIIRS level of imagery data delivered to users, while optimizing the use of scarce data transmission capacity.