A Comprehensive VRR Dataset of Luminance Signals and Their Perceived Flicker Levels: Insights for Display and GPU Manufacturers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The adoption of variable refresh rate (VRR) technology in displays—aimed at reducing input lag, minimizing video stuttering, and improving power efficiency—has introduced an unforeseen challenge: flicker caused by minor changes in luminance due to the varying duration of each frame. Existing industry flicker measuring metrics are inadequate, often overly restrictive or reliant on impractical subjective evaluations. This highlights the need for an accurate, objective flicker metric specifically designed for VRR displays. Developing such a metric requires a comprehensive dataset that captures a wide range of flicker intensities across different display technologies and luminance conditions. To facilitate this, we compiled a unique VRR dataset consisting of 160 signals, ranging from 2 to 40 cd/m 2 , along with perceived flicker levels obtained through extensive subjective testing, following a standard protocol defined in ITU‐R BT.500‐15. This dataset serves as a critical resource for flicker assessment, providing valuable insights for display manufacturers, and it is instrumental in advancing VRR technology. Our analysis revealed that JEITA, the most widely used flicker metric for VRR displays, correlates with subjective flicker perception at only 71.43%. This finding underscores the limitations of current metrics and the pressing need for a more reliable standard tailored to VRR technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it