A learning-based approach for automated quality assessment of computer-rendered images

摘要

Computer generated images are common in numerous computer graphics applications such as games, modeling, and simulation. There is normally a tradeoff between the time allocated to the generation of each image frame and and the quality of the image, where better quality images require more processing time. Specifically, in the rendering of 3D objects, the surfaces of objects may be manipulated by subdividing them into smaller triangular patches and/or smoothing them so as to produce better looking renderings. Since unnecessary subdivision results in increased rendering time and unnecessary smoothing results in reduced details, there is a need to automatically determine the amount of necessary processing for producing good quality rendered images. In this paper we propose a novel supervised learning based methodology for automatically predicting the quality of rendered images of 3D objects. To perform the prediction we train on a data set which is labeled by human observers for quality. We are then able to predict the quality of renderings (not used in the training) with an average prediction error of roughly 20%. The proposed approach is compared to known techniques and is shown to produce better results.