KonIQ-10k: Towards an ecologically valid and large-scale IQA database

Lin, Hanhe; Hosu, Vlad; Saupe, Dietmar

KonIQ-10k: Towards an ecologically valid and large-scale IQA database

Files

Lin_2-1bwsuooctmuyh0.pdf(995.92 KB)

Date

2018

Publication type

Preprint

Publication status

Published

Abstract

The main challenge in applying state-of-the-art deep learning methods to predict image quality in-the-wild is the relatively small size of existing quality scored datasets. The reason for the lack of larger datasets is the massive resources required in generating diverse and publishable content. We present a new systematic and scalable approach to create large-scale, authentic and diverse image datasets for Image Quality Assessment (IQA). We show how we built an IQA database, KonIQ-10k, consisting of 10,073 images, on which we performed very large scale crowdsourcing experiments in order to obtain reliable quality ratings from 1,467 crowd workers (1.2 million ratings). We argue for its ecological validity by analyzing the diversity of the dataset, by comparing it to state-of-the-art IQA databases, and by checking the reliability of our user studies.

Subject (DDC)

004 Computer Science

Keywords

Image database, image quality assessment, diversity sampling, crowdsourcing

Cite This

ISO 690

LIN, Hanhe, Vlad HOSU, Dietmar SAUPE, 2018. KonIQ-10k: Towards an ecologically valid and large-scale IQA database

BibTex

@unpublished{Lin2018-03-22T17:50:05ZKonIQ-42293,
  year={2018},
  title={KonIQ-10k: Towards an ecologically valid and large-scale IQA database},
  author={Lin, Hanhe and Hosu, Vlad and Saupe, Dietmar}
}

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Bibliography of Konstanz

Yes