JPEG AIC-3 Dataset : Towards Defining the High Quality to Nearly Visually Lossless Quality Range

Testolina, Michela; Hosu, Vlad; Jenadeleh, Mohsen; Lazzarotto, Davi; Saupe, Dietmar; Ebrahimi, Touradj

JPEG AIC-3 Dataset : Towards Defining the High Quality to Nearly Visually Lossless Quality Range

Dateien

Testolina_2-slqm50hhwxkh9.pdfGröße: 1.27 MBDownloads: 6

Datum

2023

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

15th International Conference on Quality of Multimedia Experience (QoMEX). Piscataway, NJ: IEEE, 2023. ISBN 979-8-3503-1174-7. Available under: doi: 10.1109/qomex58391.2023.10178554

Zusammenfassung

Visual data play a crucial role in modern society, and the rate at which images and videos are acquired, stored, and exchanged every day is rapidly increasing. Image compression is the key technology that enables storing and sharing of visual content in an efficient and cost-effective manner, by removing redundant and irrelevant information. On the other hand, image compression often introduces undesirable artifacts that reduce the perceived quality of the media. Subjective image quality assessment experiments allow for the collection of information on the visual quality of the media as perceived by human observers, and therefore quantifying the impact of such distortions. Nevertheless, the most commonly used subjective image quality assessment methodologies were designed to evaluate compressed images with visible distortions, and therefore are not accurate and reliable when evaluating images having higher visual qualities. In this paper, we present a dataset of compressed images with quality levels that range from high to nearly visually lossless, with associated quality scores in JND units. The images were subjectively evaluated by expert human observers, and the results were used to define the range from high to nearly visually lossless quality. The dataset is made publicly available to researchers, providing a valuable resource for the development of novel subjective quality assessment methodologies or compression methods that are more effective in this quality range.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

subjective visual quality assessment, dataset, high visual quality, nearly visually lossless quality, JND, JPEG AIC-3

Konferenz

2023 15th International Conference on Quality of Multimedia Experience (QoMEX), 20. Juni 2023 - 22. Juni 2023, Ghent, Belgium

Zitieren

ISO 690

TESTOLINA, Michela, Vlad HOSU, Mohsen JENADELEH, Davi LAZZAROTTO, Dietmar SAUPE, Touradj EBRAHIMI, 2023. JPEG AIC-3 Dataset : Towards Defining the High Quality to Nearly Visually Lossless Quality Range. 2023 15th International Conference on Quality of Multimedia Experience (QoMEX). Ghent, Belgium, 20. Juni 2023 - 22. Juni 2023. In: 15th International Conference on Quality of Multimedia Experience (QoMEX). Piscataway, NJ: IEEE, 2023. ISBN 979-8-3503-1174-7. Available under: doi: 10.1109/qomex58391.2023.10178554

BibTex

@inproceedings{Testolina2023-06-20Datas-67424,
  year={2023},
  doi={10.1109/qomex58391.2023.10178554},
  title={JPEG AIC-3 Dataset : Towards Defining the High Quality to Nearly Visually Lossless Quality Range},
  isbn={979-8-3503-1174-7},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={15th International Conference on Quality of Multimedia Experience (QoMEX)},
  author={Testolina, Michela and Hosu, Vlad and Jenadeleh, Mohsen and Lazzarotto, Davi and Saupe, Dietmar and Ebrahimi, Touradj}
}

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Universitätsbibliographie

Ja