Large-scale crowdsourced subjective assessment of picturewise just noticeable difference.

Lin, Hanhe; Chen, Guangan; Jenadeleh, Mohsen; Hosu, Vlad; Reips, Ulf-Dietrich; Hamzaoui, Raouf; Saupe, Dietmar

Abstract

The picturewise just noticeable difference (PJND) for a given image, compression scheme, and subject is the smallest distortion level that the subject can perceive when the image is compressed with this compression scheme. The PJND can be used to determine the compression level at which a given proportion of the population does not notice any distortion in the compressed image. To obtain accurate and diverse results, the PJND must be determined for a large number of subjects and images. This is particularly important when experimental PJND data are used to train deep learning models that can predict a probability distribution model of the PJND for a new image. To date, such subjective studies have been carried out in laboratory environments. However, the number of participants and images in all existing PJND studies is very small because of the challenges involved in setting up laboratory experiments. To address this limitation, we develop a framework to conduct PJND assessments via crowdsourcing. We use a new technique based on slider adjustment and a flicker test to determine the PJND. A pilot study demonstrated that our technique could decrease the study duration by 50% and double the perceptual sensitivity compared to the standard binary search approach that successively compares a test image side by side with its reference image. Our framework includes a robust and systematic scheme to ensure the reliability of the crowdsourced results. Using 1,008 source images and distorted versions obtained with JPEG and BPG compression, we apply our crowdsourcing framework to build the largest PJND dataset, KonJND-1k (Konstanz just noticeable difference 1k dataset). A total of 503 workers participated in the study, yielding 61,030 PJND samples that resulted in an average of 42 samples per source image. The KonJND-1k dataset is available at http://database.mmsp-kn.de/konjnd-1k-database.html.

Citation

LIN, H., CHEN, G., JENADELEH, M., HOSU, V., REIPS, U.-D., HAMZAOUI, R. and SAUPE, D. 2022. Large-scale crowdsourced subjective assessment of picturewise just noticeable difference. IEEE transactions on circuits and systems for video technology [online], 32(9), pages 5859-5873. Available from: https://doi.org/10.1109/TCSVT.2022.3163860

• Journal Article Type: Article
• Acceptance Date: Mar 23, 2022
• Online Publication Date: Mar 31, 2022
• Publication Date: Sep 30, 2022
• Deposit Date: Apr 8, 2022
• Publicly Available Date: Apr 1, 2023
• Journal: IEEE transactions on circuits and systems for video technology
• Print ISSN: 1051-8215
• Electronic ISSN: 1558-2205
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 9
• Pages: 5859-5873
• DOI: https://doi.org/10.1109/TCSVT.2022.3163860
• Keywords: Picturewise just noticeable difference (PJND); Image compression; Video compression; Image coding; Distortion; Transform coding; Crowdsourcing; Observers; Image resolution; Visualization; Just noticeable difference (JND); Satisfied user ratio (SUR); Crowdsourcing; Flicker test; JPEG; BPG; Dataset
• Public URL: https://rgu-repository.worktribe.com/output/1635440