{'fraction_non_alphanumeric': 0.02445302445302445, 'fraction_numerical': 0.002574002574002574, 'mean_word_length': 4.984615384615385, '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}}
Fast video interpolation/upsampling using linear motion model

Recently, the probabilistic motion field was proposed for super-resolution reconstruction (SRR). In the interpolation step of SRR, a missing pixel can be estimated by the weighted average of neighboring pixels, which are weighted by the errors with the missing pixel. However, the errors are far from true values due to the approximated missing pixel in calculating the errors. Hence, in this paper, we propose a linear motion model to better approximate the errors, which results in a better interpolation quality. Experimental results show that a gain of 0.6 dB in PSNR is achievable using this linear motion model, and only a small number of neighboring pixels have to be used for fast interpolation/upsampling.