Survey of image quality metrics from the perspective of detection and classification performance

Detection and classification of targets is a process that we perform every day, often without even realizing it; the ability to locate and recognize a specific face in a crowd, to be able to identify a bird by its song, or to know what is for dinner simply by the aroma. Much effort has been spent in trying to replicate these uncanny abilities of our senses through purpose-built sensors. For many of these sensing modalities, automatic target recognition algorithms have been developed to automate the process of locating and recognizing the occurrence of a specific state from within the sensed data. This survey is an overview of the metrics used to assess and quantify the quality or 'goodness' of the data, mainly for imaging sensors, from the perspective of automatic target recognition performance. Digital image data go through several transformations as they are moved from the recording sensor to personal electronic devices, resulting in distortions. There has been considerable research in the area of quality assessment of digital image and video media. Often, the image quality assessment approaches rely on having the original image or at least information about the original image available for the evaluation. These approaches may also rely on knowing the source of distortion. The accuracy is typically gauged using the image quality assessment made by the human visual system. As a consequence, the metrics measure aspects of the image that are tied closely to the human perception of quality. Evaluating image quality from an automatic target recognition perspective offers many challenges, as the original data is not likely to be available. Additionally, there may be multiple unknown sources of image quality degradation forcing the need for a general solution. Finally, the human perception of 'good quality' may not necessarily correlate to optimal image quality for automatic target recognition performance, in which case metrics modeled after the human visual system may not be the best choice for image quality assessment. The goal of this survey is to provide a high-level background and overview of how image quality assessment is currently performed for different sensing modalities. Additionally, the challenges that are being addressed within the various disciplines will be discussed and commonalities will be highlighted.