Publication Details
Learning to Predict Localized Distortions in Rendered Images
Herzog Robert (MPII)
Mantiuk Rafał (BANGOR)
Mantiuk Radosław (WPU)
Myszkowski Karol (MPII)
Seidel Hans-Peter (MPII)
image quality assessment, feature vectors, machine learning, rendering
In this work, we present an analysis of feature descriptors for objective image quality assessment. We explore a large space of possible features including components of existing image quality metrics as well as many traditional computer vision and statistical features. Additionally, we propose new features motivated by human perception and we analyze visual saliency maps acquired using an eye tracker in our user experiments. The discriminative power of the features is assessed by means of a machine learning framework revealing the importance of each feature for image quality assessment task. Furthermore, we propose a new data-driven full-reference image quality metric which outperforms current state-of-the-art metrics. The metric was trained on subjective ground truth data combining two publicly available datasets. For the sake of completeness we create a new testing synthetic dataset including experimentally measured subjective distortion maps. Finally, using the same machine-learning framework we optimize the parameters of popular existing metrics.
@ARTICLE{FITPUB10478,
author = "Martin \v{C}ad\'{i}k and Robert Herzog and Rafa\l Mantiuk and Rados\law Mantiuk and Karol Myszkowski and Hans-Peter Seidel",
title = "Learning to Predict Localized Distortions in Rendered Images",
pages = "401--410",
journal = "Computer Graphics Forum",
volume = 2013,
number = 7,
year = 2013,
ISSN = "0167-7055",
doi = "10.1111/cgf.12248",
language = "english",
url = "https://www.fit.vut.cz/research/publication/10478"
}