How to Train No Reference Video Quality Measures for New Coding Standards using Existing Annotated Datasets?

Subjective experiments are important for developing objective Video Quality Measures (VQMs). However, they are time-consuming and resource-demanding. In this context, being able to reuse existing subjective data on previous video coding standards to train models capable of predicting the perceptual quality of video content processed with newer codecs acquires significant importance. This paper investigates the possibility of generating an HEVC encoded Processed Video Sequence (PVS) in such a way that its perceptual quality is as similar as possible to that of an AVC encoded PVS whose quality has already been assessed by human subjects. In this way, the perceptual quality of the newly generated HEVC encoded PVS may be annotated approximately with the Mean Opinion Score (MOS) of the related AVC encoded PVS. To show the effectiveness of our approach, we compared the performance of a simple and low complexity but yet effective no reference hybrid model trained on the data generated with our approach with the same model trained on data collected in the context of a pristine subjective experiment. In addition, we merged seven subjective experiments such that they can be used as one aligned dataset containing either original HEVC bitstreams or the newly generated data explained in our proposed approach. The merging process accounts for the differences in terms of quality scale, chosen assessment method and context influence factors. This yields a large annotated dataset of HEVC sequences that is made publicly available for the design and training of no reference hybrid VQMs for HEVC encoded content.