Towards Joint Frame-Level and MOS Quality Predictions with Low-Complexity Objective Models

The evaluation of the quality of gaming content, with low-complexity and low-delay approaches is a major challenge raised by the emerging gaming video streaming and cloud-gaming services. Considering two existing and a newly created gaming databases this paper confirms that some low-complexity metrics match well with subjective scores when considering usual correlation indicators. It is however argued such a result is insufficient: gaming content suffers from sudden large quality drops that these indicators do not capture. In addition to proposing three new low-complexity models based on various machine learning techniques, this paper introduces a new indicator to capture sudden quality variations and reports poor results for most of the models when applying this indicator. Consequently, an original way to train the models, using jointly the subjective scores and the frame level scores of a full-reference metric, is proposed. The high correlation through traditional indicators is preserved, while the efficiency on the new indicator is drastically improved.