{'fraction_non_alphanumeric': 0.017661900756938603, 'fraction_numerical': 0.0, 'mean_word_length': 4.483870967741935, '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}}
Predicting Video-frames Using Encoder-convlstm Combination

Video generation is an active field of research. With the rise in the amount of available data and economically available processing power in the form of GPUs, deep Learning has been a go-to solution for many real life problems and similarly it is often attempted to solve the problem of video generation using deep learning. Predicting the next set of frames for a given set of frames in a video has seldom been taken up. Each video is composed of a consecutive closely related frames of images. If we consider these frames, the frame in each time-step seems to be related to the frames in the preceding time-steps. Therefore, we have both spatial and temporal data available from any set of consecutive frames in a video. Learning some sort of representation of the images that encodes the spatial data of the images (frames) can be combined with learning how these representations of a particular time-step is related with the next few time-steps is made possible, then prediction of the next few frames for a given set of frames is made possible. Our aim is to propose a simple yet effective model that can achieve this goal.