{'fraction_non_alphanumeric': 0.018628281117696866, 'fraction_numerical': 0.004233700254022015, 'mean_word_length': 4.765853658536585, '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}}
Pan, zoom, scan — Time-coherent, trained automatic video cropping

We present a method to fully automatically fit videos in 16:9 format on 4:3 screens and vice versa. It can be applied to arbitrary aspect ratios and can be used to make videos suitable for mobile viewing devices with small and possibly uncommonly sized displays. The cropping sequence is optimised over time to create smooth transitions and thus leads to an excellent viewing experience. Current televisions have simple and often disturbing methods which either show the centre region of the image, distort the image, or pad it with black borders. The technique presented here can fully automatically find the ldquorightrdquo viewing area for each image in a video sequence. It works in real-time with only very little time-shift. We employ different low-level features and a log-linear model to learn how to find the right area. The method is able to automatically decide whether padding with black borders is necessary or whether all relevant image areas fit on screen by cropping the image. Evaluation is done on ten videos from five different types of content and the baseline methods are clearly outperformed.