{'fraction_non_alphanumeric': 0.019202363367799114, 'fraction_numerical': 0.0014771048744460858, 'mean_word_length': 5.843434343434343, '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}}
Preparing pictures for visual comparison.

To judge the effectiveness of different digital image processing techniques whose results are pictures rather than statistics, one must visually compare the resulting pictures. The perceived differences between two of these pictures may be significant to the technique's evaluation or only cosmetic. Unfortunately the cosmetic differences may dominate the comparison, thereby giving misleading results. We describe here a preprocessing procedure and its generalization that minimizes a cosmetic difference, a point nonlinearity between the two pictures prior to the visual comparison. The procedure consists of calculating and applying an appropriate inverse nonlinearity to one of the two pictures. The calculation of the inverse nonlinearity depends strongly on the spatial distribution of the gray values. This calculation therefore differs in concept and execution from the common practice of standardizing pictures through histogram manipulation, where some selected histogram is used as a reference. The histogram standardization, usually equal probability quantization, ignores the spatial location of occurring gray values. The processing procedure we have used is: (1) choose one picture as the reference; (2) calculate the space-invariant point nonlinearity, which, when applied to the second picture,