{'fraction_non_alphanumeric': 0.014398848092152628, 'fraction_numerical': 0.0007199424046076314, 'mean_word_length': 5.525821596244131, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Latent and apparent image quality metrics

Measures of image quality are presented here that have been developed to assess both the immediate quality of an image and the potential at intermediate points in an imaging chain for enhanced image quality. The original intent of the metric(s) was to provide an optimand for interpolator design, and the metrics have subsequently been used for a number of differential image quality analyses and imaging system component designs. The metrics presented are of the same general form as the National Imagery Interpretability Rating Scale (NIIRS), representing quality as the base-2 logarithm of linear resolution, so that one unit of differential quality represents a doubling or halving of the resolution of imagery. Analysis of a simple imaging chain is presented in terms of the metrics, with conclusions regarding interpolator design, consistency of the latent and apparent image quality metrics, and the relationship between interpolator and convolution kernel design in a system where both are present. Among the principal results are an optimized division of labor between interpolators and Modulation Transfer Function Correction (MTFC) filters, consistency of the analytical latent and apparent image quality metrics with each other and with visually optimized aim curves, and an introduction to sharpening interpolator design methodology.