No-Reference Image Quality Assessment Using Meta-Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020689" target="_blank" >RIV/62690094:18450/23:50020689 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-981-99-2680-0_13" target="_blank" >http://dx.doi.org/10.1007/978-981-99-2680-0_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-99-2680-0_13" target="_blank" >10.1007/978-981-99-2680-0_13</a>

Result language

angličtina

Original language name

No-Reference Image Quality Assessment Using Meta-Learning

Original language description

Deep learning-based no-reference image quality assessment faces problems like dependency on a large amount of experimental data and the generalization ability of the learned model. A deep learning model trained on a specific dataset cannot obtain the desired results for testing on other datasets. Similarly, a deep learning model trained with small experiment data does not provide the best result. This paper addresses these problems of the deep learning model using the meta-learning approach in the field of no-reference Image Quality Assessment. The no-reference image quality assessment is a small sample problem, where the amount of experimental data is very less. Although data augmentation techniques have been used to increase the amount of experimental data, they do not increase the variation of the data. Therefore, traditional deep learning-based techniques are unsuitable for no-reference image quality assessment. Another problem is the lack of generalization ability. A deep learning model trained with image quality datasets containing images distorted synthetically cannot efficiently assess the quality of images distorted naturally. This work proposes a meta-learning model that can be trained with more than one image quality dataset, where one image quality assessment dataset contains synthetic images and the other contains real images. Finally, another image quality assessment dataset has tested the trained model. The test result is better than the state-of-the-art methods, and the results establish the fact that the meta-learning model proposed in this paper tries to resolve the problems of the deep learning model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Czech name

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Czech description

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Type

D - Article in proceedings

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

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Article name in the collection

Lecture Notes in Networks and Systems

ISBN

978-981-9926-79-4

ISSN

2367-3370

e-ISSN

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Number of pages

8

Pages from-to

137-144

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

Singapur

Event location

Ropar

Event date

Dec 19, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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