Learning to Predict Localized Distortions in Rendered Images

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F13%3APU106440" target="_blank" >RIV/00216305:26230/13:PU106440 - isvavai.cz</a>
Result on the web
<a href="http://cadik.posvete.cz" target="_blank" >http://cadik.posvete.cz</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/cgf.12248" target="_blank" >10.1111/cgf.12248</a>

Result language
angličtina
Original language name
Learning to Predict Localized Distortions in Rendered Images
Original language description
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.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/LD12027" target="_blank" >LD12027: Acquisition and processing of HDR images - Pořizování a zpracování HDR obrazů</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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