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Crowdsourced Estimation of Collective Just Noticeable Difference for Compressed Video with Flicker Test and QUEST+
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  • Mohsen Jenadeleh ,
  • Raouf Hamzaoui ,
  • Ulf-Dietrich Reips ,
  • Dietmar Saupe
Mohsen Jenadeleh
University of Konstanz

Corresponding Author:[email protected]

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Raouf Hamzaoui
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Ulf-Dietrich Reips
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Dietmar Saupe
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Abstract

The concept of video-wise just noticeable difference (JND) was recently proposed to determine the lowest bitrate at which a source video can be compressed without perceptible quality loss with a given probability.
This bitrate is usually obtained from an estimate of the satisfied used ratio (SUR) at each bitrate, respectively encoding quality parameter. The SUR is the probability that the distortion corresponding to this bitrate is not noticeable. Commonly, the SUR is computed experimentally by estimating the subjective JND threshold of each subject using binary search, fitting a distribution model to the collected data, and creating the complementary cumulative distribution function of the distribution. The subjective tests consist of paired comparisons between the source video and compressed versions. However, we show that this approach typically over- or underestimates the SUR.
To address this shortcoming, we directly estimate the SUR function by considering the entire population as a collective observer. Our method randomly chooses the subject for each paired comparison and uses a state-of-the-art Bayesian adaptive psychometric method (QUEST+) to select the compressed video in the paired comparison.
Our simulations show that this collective method yields more accurate SUR results with fewer comparisons.
We also provide a subjective experiment to assess the JND and SUR for compressed video. In the paired comparisons, we apply a flicker test that compares a video that interleaves the source video and its compressed version with the source video. Analysis of the subjective data revealed that the flicker test provides on average higher sensitivity and precision in the assessment of the JND threshold than the usual test that compares compressed versions with the source video.
Using crowdsourcing and the proposed approach, we build a JND dataset for 45 source video sequences that are encoded with both  advanced video coding (AVC) and versatile video coding (VVC) at all available quantization parameters.  Our dataset is available at http://database.mmsp-kn.de/flickervidset-database.html.