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

Xi Zhang; Gady Agam

doi:10.1117/12.911964

24 January 2012 A learning-based approach for automated quality assessment of computer-rendered images

Xi Zhang, Gady Agam

Proceedings Volume 8293, Image Quality and System Performance IX; 82930Y (2012) https://doi.org/10.1117/12.911964
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

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.

Citation Download Citation

Xi Zhang and Gady Agam "A learning-based approach for automated quality assessment of computer-rendered images", Proc. SPIE 8293, Image Quality and System Performance IX, 82930Y (24 January 2012); https://doi.org/10.1117/12.911964

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available