论文标题
deepfl-iqa：深度IQA功能学习的弱监督
DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning
论文作者
论文摘要
多层次的深度功能一直在推动美学和图像质量评估（IQA）的最新方法。但是，大多数IQA基准测试由人为变形的图像组成，这些图像源自表现不佳的ImageNet。我们提出了一个新的IQA数据集和一种弱监督的功能学习方法，以训练更适合人为扭曲图像的IQA的功能。数据集Kadis-700K比类似作品要广泛得多，其中包括140,000张原始图像，25种扭曲类型，总计700K扭曲版本。我们弱监督的功能学习被设计为一种多任务学习类型培训，使用11个现有的全参考IQA指标作为差异平均意见分数的代理。我们还介绍了人工降低的图像的基准数据库Kadid-10k，每个图像都由30名群众的主观注释。我们通过培训和测试该数据库上的浅回归网络和其他五个基准IQA数据库来利用（无参考）图像质量评估的派生图像矢量。我们的方法称为DEEPFL-IQA，其性能比其他基于功能的NO-NO-REFERPERIT IQA方法更好，并且比在KADID-10K上测试过的全参考IQA方法更好。对于其他五个基准IQA数据库，DEEPFL-IQA平均匹配了最佳现有端到端深度学习方法的性能。
Multi-level deep-features have been driving state-of-the-art methods for aesthetics and image quality assessment (IQA). However, most IQA benchmarks are comprised of artificially distorted images, for which features derived from ImageNet under-perform. We propose a new IQA dataset and a weakly supervised feature learning approach to train features more suitable for IQA of artificially distorted images. The dataset, KADIS-700k, is far more extensive than similar works, consisting of 140,000 pristine images, 25 distortions types, totaling 700k distorted versions. Our weakly supervised feature learning is designed as a multi-task learning type training, using eleven existing full-reference IQA metrics as proxies for differential mean opinion scores. We also introduce a benchmark database, KADID-10k, of artificially degraded images, each subjectively annotated by 30 crowd workers. We make use of our derived image feature vectors for (no-reference) image quality assessment by training and testing a shallow regression network on this database and five other benchmark IQA databases. Our method, termed DeepFL-IQA, performs better than other feature-based no-reference IQA methods and also better than all tested full-reference IQA methods on KADID-10k. For the other five benchmark IQA databases, DeepFL-IQA matches the performance of the best existing end-to-end deep learning-based methods on average.
