Information-theoretic analysis of Blinn-Phong lighting with applicationto mobile cloud gaming

In mobile cloud gaming, images are rendered, compressed, and delivered to mobile devices from cloud servers. Joint optimization of rendering and coding aims to reduce data rate of the delivery. Among the tasks in a rendering pipeline, Blinn-Phong lighting is the most widely adopted lighting model and also has the most impact on the visual information of the rendered images. In this paper, we analyze and model the information content of an image rendered using the Blinn-Phong lighting model. Our analytic model estimates the entropy generated by the Blinn-Phong reflections on the rendered images. In the context of cloud gaming, we derive the solution for the joint rendering and coding optimization, and demonstrate how the entropy estimators can facilitate fast and on-the-fly solution to the optimization. Experiments results validate our information-theoretic analyses, and show substantial bitrate reduction by the joint optimization of rendering and coding.