{'fraction_non_alphanumeric': 0.021660649819494584, 'fraction_numerical': 0.01444043321299639, 'mean_word_length': 5.117647058823529, '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}}
An efficient method to reduce computational cost of improved super-resolution method

In this paper, we propose a method to efficiently reduce computational cost of the improved super-resolution method that achieves dense motion estimation using overlapped block matching. We focus on statistics of motion vectors allocated to a small cell, and conjoin the calculation for same motion vectors in the optimization process. This method can reduce the entire computational cost up to 29.9 ∼ 49.1% depending on a given video sequence in the case that maintains the original performance of the improved super-resolution method completely. If we relax the constraint that preserves the original performance, we can further reduce computational cost up to 20.7 ∼ 27.8% without serious deterioration of the quality of reconstructed images.