{'fraction_non_alphanumeric': 0.013223140495867768, 'fraction_numerical': 0.0, 'mean_word_length': 5.307291666666667, '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}}
Towards Large-Scale Super Resolution Datasets via Learned Downsampling of Ray-Traced Renderings

Delivering high resolution content is a challenge in the film and games industries due to the cost of photorealistic ray-traced rendering. Image upscaling techniques are commonly used to obtain a high resolution result from a low resolution render. Recently, deep learned upscaling has started to make an impact in production settings, synthesizing sharper and more detailed imagery than previous methods. The quality of a super resolution model depends on the size of its dataset, which can be expensive to generate at scale due to the large number of ray-traced pairs of renders required. In this report, we discuss our experiments training an additional neural network to learn the degradation operator, which can be used to rapidly generate low resolution images from existing high resolution renders. Our testing on production scenes shows that super resolution networks trained with a large synthetic dataset produce fewer artifacts and better reconstruction quality than networks trained on a smaller rendered dataset alone, and compare favorably to recent state of the art blind synthetic data techniques.