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Author, Editor(s)

Author(s):

Čadík, Martin
Herzog, Robert
Mantiuk, Rafal
Mantiuk, Radosław
Myszkowski, Karol
Seidel, Hans-Peter

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Not MPG Author(s):

Mantiuk, Rafal
Mantiuk, Radosław

BibTeX cite key*:

cadik2013learning

Title

Title*:

Learning to Predict Localized Distortions in Rendered Images


cadik13learning.pdf (5146.51 KB)

Journal

Journal Title*:

Computer Graphics Forum (Proceedings Pacific Graphics 2013)

Journal's URL:

http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-8659

Download URL
for the article:

http://www.mpi-inf.mpg.de/resources/hdr/metric/

Language:

English

Publisher

Publisher's
Name:

The Eurographics Association and John Wiley & Sons Ltd.

Publisher's URL:


Publisher's
Address:


ISSN:

0167-7055

Vol, No, pp, Date

Volume*:

32

Number:

7

Publishing Date:

2013

Pages*:

10

Number of
VG Pages:


Page Start:


Page End:


Sequence Number:


DOI:


Note, Abstract, ©

Note:


(LaTeX) Abstract:

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.

URL for the Abstract:

http://www.mpi-inf.mpg.de/resources/hdr/metric/

Categories,
Keywords:

image quality assessment, machine learning, rendering artifacts

HyperLinks / References / URLs:


Copyright Message:


Personal Comments:


Download
Access Level:

Public

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computer Graphics Group

Audience:

experts only

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort


BibTeX Entry:

@ARTICLE{cadik2013learning,
AUTHOR = {Čad{\'i}k, Martin and Herzog, Robert and Mantiuk, Rafal and Mantiuk, Radosław and Myszkowski, Karol and Seidel, Hans-Peter},
TITLE = {Learning to Predict Localized Distortions in Rendered Images},
JOURNAL = {Computer Graphics Forum (Proceedings Pacific Graphics 2013)},
PUBLISHER = {The Eurographics Association and John Wiley & Sons Ltd.},
YEAR = {2013},
NUMBER = {7},
VOLUME = {32},
PAGES = {10},
ISBN = {0167-7055},
}


Entry last modified by Karol Myszkowski, 01/30/2014
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Editor(s)
[Library]
Created
10/07/2013 11:27:58 PM
Revisions
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2.
Editor(s)
Karol Myszkowski
Karol Myszkowski
Karol Myszkowski
Karol Myszkowski
Karol Myszkowski
Edit Dates
10/08/2013 10:39:28 AM
10/08/2013 10:37:52 AM
10/08/2013 10:33:35 AM
10/08/2013 10:32:22 AM
10/08/2013 10:29:37 AM
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