Learning to Predict Localized Distortions in Rendered Images

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning to Predict Localized Distortions in Rendered Images

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Learning to Predict Localized Distortions in Rendered Images

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LD12027" target="_blank" >LD12027: Pořizování a zpracování HDR obrazů ? Acquisition and processing of HDR images</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMPUTER GRAPHICS FORUM

ISSN

0167-7055

e-ISSN

1467-8659

Svazek periodika

2013

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

401-410

Kód UT WoS článku

000327310800042

EID výsledku v databázi Scopus

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