Image Visual Realism: From Human Perception to Machine Computation

Visual realism is defined as the extent to which an image appears to people as a photo rather than computer generated. Assessing visual realism is important in applications like computer graphics rendering and photo retouching. However, current realism evaluation approaches use either labor-intensiv...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2018-09, Vol.40 (9), p.2180-2193
Main Authors: Fan, Shaojing, Ng, Tian-Tsong, Koenig, Bryan Lee, Herberg, Jonathan Samuel, Jiang, Ming, Shen, Zhiqi, Zhao, Qi
Format: Article
Language:English
Subjects:
Artificial neural networks
Benchmark testing
Computation
Computational modeling
Computer Graphics
convolutional neural network
Fabric analysis
Face
Female
human psychophysics
Humans
Judgments
Machine Learning
Male
Mathematical models
Models, Statistical
Neural Networks (Computer)
Psychophysics - methods
Realism
Rendering (computer graphics)
Retouching
Solid modeling
statistical modeling
Statistical models
Video boards
Virtual Reality
Visual Perception - physiology
Visual perception driven algorithms
Visual realism
Visualization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Visual realism is defined as the extent to which an image appears to people as a photo rather than computer generated. Assessing visual realism is important in applications like computer graphics rendering and photo retouching. However, current realism evaluation approaches use either labor-intensive human judgments or automated algorithms largely dependent on comparing renderings to reference images. We develop a reference-free computational framework for visual realism prediction to overcome these constraints. First, we construct a benchmark dataset of 2,520 images with comprehensive human annotated attributes. From statistical modeling on this data, we identify image attributes most relevant for visual realism. We propose both empirically-based (guided by our statistical modeling of human data) and deep convolutional neural network models to predict visual realism of images. Our framework has the following advantages: (1) it creates an interpretable and concise empirical model that characterizes human perception of visual realism; (2) it links computational features to latent factors of human image perception.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2017.2747150