Title
An Automated Estimator of Image Visual Realism Based on Human Cognition
Abstract
Assessing the visual realism of images is increasingly becoming an essential aspect of fields ranging from computer graphics (CG) rendering to photo manipulation. In this paper we systematically evaluate factors underlying human perception of visual realism and use that information to create an automated assessment of visual realism. We make the following unique contributions. First, we established a benchmark dataset of images with empirically determined visual realism scores. Second, we identified attributes potentially related to image realism, and used correlational techniques to determine that realism was most related to image naturalness, familiarity, aesthetics, and semantics. Third, we created an attributes-motivated, automated computational model that estimated image visual realism quantitatively. Using human assessment as a benchmark, the model was below human performance, but outperformed other state-of-the-art algorithms.
Year
DOI
Venue
2014
10.1109/CVPR.2014.535
CVPR
Keywords
Field
DocType
cognition,human perception,image semantics,image naturalness,visual realism scores,correlational techniques,statistical analysis,learning (artificial intelligence),computer graphics rendering,image aesthetics,perception,visual realism,cg rendering,human assessment,visual realism assessment,human cognition,computer vision,image visual realism estimation,image familiarity,visual realism, human cognition, perception,lighting,semantics,computational modeling,correlation,learning artificial intelligence,layout,visualization
Computer vision,Computer science,Naturalness,Artificial intelligence,Cognition,Rendering (computer graphics),Perception,Computer graphics,Realism,Semantics,Estimator
Conference
ISSN
Citations 
PageRank 
1063-6919
3
0.40
References 
Authors
16
6
Name
Order
Citations
PageRank
Shaojing Fan1225.63
Tian-Tsong Ng269443.29
Jonathan S. Herberg3142.55
Bryan L. Koenig4212.94
Cheston Tan515515.27
Rangding Wang625931.01