KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment.

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ID: 88073
2020
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Ranked #1 of 51 articles by views in ieee transactions on image processing : a publication of the ieee signal processing society

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Abstract
Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0:921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0:825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512 × 384). Correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image.
Reference Key
hosu2020koniq10kieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hosu, Vlad;Lin, Hanhe;Sziranyi, Tamas;Saupe, Dietmar;
Journal ieee transactions on image processing : a publication of the ieee signal processing society
Year 2020
DOI
10.1109/TIP.2020.2967829
URL
Keywords
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