How to reuse existing annotated image quality datasets to enlarge available training data with new distortion types

There is a continuing demand for objective measures that predict perceived media quality. Researchers are developing new methods for mapping technical parameters of digital media to the perceived quality. It is quite common to use machine learning algorithms for these purposes especially deep learning algorithms, which need large amounts of data for training. In this paper, we aim towards getting more training data with recent types of distortions. Instead of doing expensive subjective experiments, we evaluate the reuse of previously published, well-known image datasets with subjective annotation. In this contribution, the procedure of mapping Mean Opinion Scores (MOS) from an already published subjectively annotated dataset with older codecs to new codecs is presented. In particular, we map from Joint Photographic Experts Group (JPEG) distortions to newer High Efficiency Video Coding (HEVC) distortions. We have used values of three different objective methods as a connection between these two different distortion types. In order to investigate the significance of our approach, subjective verification tests were designed and conducted. The design goals led to two types of experiments, i.e. Pair Comparison (PC) test and Absolute Category Rating (ACR) test, in which 40 participants provided their opinion. Results of the subjective experiments indicate that it may be possible to use information gained from older datasets to describe the perceived quality of more recent compression algorithms.