Is there a relationship between Mean Opinion Score (MOS) and Just Noticeable Difference (JND)?
Jingwen Zhu, Hadi Amirpour, Wei Zhou, Patrick Le Callet

TL;DR
This study investigates the relationship between Mean Opinion Score (MOS) and Just Noticeable Difference (JND) in video quality assessment, revealing that while MOS aligns with JND expectations, reverse mapping is ambiguous and limited participant studies may lack sensitivity.
Contribution
The paper provides an empirical analysis of how MOS relates to JND in high-quality video scenarios, highlighting the challenges in mapping between these perceptual metrics.
Findings
MOS at JND points generally matches theoretical expectations
Reverse mapping from MOS to JND is ambiguous due to overlapping confidence intervals
Limited participant studies may not detect significant differences in perceptual quality
Abstract
Evaluating perceived video quality is essential for ensuring high Quality of Experience (QoE) in modern streaming applications. While existing subjective datasets and Video Quality Metrics (VQMs) cover a broad quality range, many practical use cases especially for premium users focus on high quality scenarios requiring finer granularity. Just Noticeable Difference (JND) has emerged as a key concept for modeling perceptual thresholds in these high end regions and plays an important role in perceptual bitrate ladder construction. However, the relationship between JND and the more widely used Mean Opinion Score (MOS) remains unclear. In this paper, we conduct a Degradation Category Rating (DCR) subjective study based on an existing JND dataset to examine how MOS corresponds to the 75% Satisfied User Ratio (SUR) points of the 1st and 2nd JNDs. We find that while MOS values at JND points…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage and Video Quality Assessment · Video Coding and Compression Technologies · Visual Attention and Saliency Detection
