JND - basierte perzeptuelle Videoqualitätsanalyse und -modellierung
- AG Saupe (Multimedia Signalverarbeitung)
|(2023): Crowdsourced Estimation of Collective Just Noticeable Difference for Compressed Video with Flicker Test and QUEST+ TechRxiv. Available under: doi: 10.36227/techrxiv.24119709.v1||
Crowdsourced Estimation of Collective Just Noticeable Difference for Compressed Video with Flicker Test and QUEST+
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 database.mmsp-kn.de/flickervidset-database.html.
|(2023): Relaxed forced choice improves performance of visual quality assessment methods||
In image quality assessment, a collective visual quality score for an image or video is obtained from the individual ratings of many subjects. One commonly used format for these experiments is the two-alternative forced choice method. Two stimuli with the same content but differing visual quality are presented sequentially or side-by-side. Subjects are asked to select the one of better quality, and when uncertain, they are required to guess. The relaxed alternative forced choice format aims to reduce the cognitive load and the noise in the responses due to the guessing by providing a third response option, namely, "not sure". This work presents a large and comprehensive crowdsourcing experiment to compare these two response formats: the one with the ``not sure'' option and the one without it. To provide unambiguous ground truth for quality evaluation, subjects were shown pairs of images with differing numbers of dots and asked each time to choose the one with more dots. Our crowdsourcing study involved 254 participants and was conducted using a within-subject design. Each participant was asked to respond to 40 pair comparisons with and without the "not sure" response option and completed a questionnaire to evaluate their cognitive load for each testing condition. The experimental results show that the inclusion of the "not sure" response option in the forced choice method reduced mental load and led to models with better data fit and correspondence to ground truth. We also tested for the equivalence of the models and found that they were different. The dataset is available at database.mmsp-kn.de/cogvqa-database.html.
|Laufzeit:||21.12.2021 – 20.12.2024|