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Multi-level deep-features have been driving state-of-the-art methods foraesthetics and image quality assessment (IQA). However, most IQA benchmarks arecomprised of artificially distorted images, for which features derived fromImageNet under-perform. We propose a new IQA dataset and a weakly supervisedfeature learning approach to train features more suitable for IQA ofartificially distorted images. The dataset, KADIS-700k, is far more extensivethan similar works, consisting of 140,000 pristine images, 25 distortionstypes, totaling 700k distorted versions. Our weakly supervised feature learningis designed as a multi-task learning type training, using eleven existingfull-reference IQA metrics as proxies for differential mean opinion scores. Wealso introduce a benchmark database, KADID-10k, of artificially degradedimages, each subjectively annotated by 30 crowd workers. We make use of ourderived image feature vectors for (no-reference) image quality assessment bytraining and testing a shallow regression network on this database and fiveother benchmark IQA databases. Our method, termed DeepFL-IQA, performs betterthan other feature-based no-reference IQA methods and also better than alltested full-reference IQA methods on KADID-10k. For the other five benchmarkIQA databases, DeepFL-IQA matches the performance of the best existingend-to-end deep learning-based methods on average.

DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning