Generalized Westerink-Roufs Model for Predicting Quality of Scaled Video

Resolution is a fundamental property of encoded video. Understanding the impact of resolution on quality as an independent parameter can help design better, more efficient systems, such as selecting optimum rendition in adaptive video streaming applications. One known quality model that considers resolution for predicting the perceived picture quality is the Westerink and Roufs (WR) model, which establishes the relationship between subjective quality and two parameters of viewing setup: angular resolution and viewing angle. This paper first validates the WR model on recent datasets and shows that it is reasonably accurate. We then propose a generalization of this model, allowing operation in a broader range of parameters and with more graceful saturation in extended regions. We then validate the performance of the proposed Generalized WR model on the new datasets and show that the proposed model achieves even a better fit to the recent datasets. We also demonstrate that the proposed Generalized model can account for the differences in scaling algorithms, including more advanced ML-based methods such as super-resolution. We conclude with a discussion of several possible applications of this model, including its use to guide the rendition selection decisions in streaming players and adapt that decision logic based on the upsampling algorithms used at the player.