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elaTCSF: A Temporal Contrast Sensitivity Function for Flicker Detection and Modeling Variable Refresh Rate Flicker

Graphics · Submitted 21 Mar 2025

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We demonstrate how elaTCSF can be used to predict flicker due to low-persistence in VR headsets, identify flicker-free VRR operational ranges, and determine flicker sensitivity in lighting design.

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arXiv ID2503.16759 (version 1)
Pith IDWIWR6WX3, Pith's permanent record of this paper and its review
CitationAli Bozorgian, Maliha Ashraf, Rafa{\l} K. Mantiuk, Robert Wanat, Yancheng Cai. "elaTCSF: A Temporal Contrast Sensitivity Function for Flicker Detection and Modeling Variable Refresh Rate Flicker." arXiv:2503.16759 (2025). https://pith.science/paper/WIWR6WX3
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,974 input tokens.

What the abstract itself states, as probabilities: claims first 0.85 · states limitation 0.11 · human participants 0.74 · controlled comparison 0.12 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.65 · mentions code or data availability 0.12.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

The perception of flicker has been a prominent concern in illumination and electronic display fields for over a century. Traditional approaches often rely on Critical Flicker Frequency (CFF), primarily suited for high-contrast (full-on, full-off) flicker. To tackle varying contrast flicker, the International Committee for Display Metrology (ICDM) introduced a Temporal Contrast Sensitivity Function TCSF$_{IDMS}$ within the Information Display Measurements Standard (IDMS). Nevertheless, this standard overlooks crucial parameters: luminance, eccentricity, and area. Existing models incorporating these parameters are inadequate for flicker detection, especially at low spatial frequencies. To address these limitations, we extend the TCSF$_{IDMS}$ and combine it with a new spatial probability summation model to incorporate the effects of luminance, eccentricity, and area (elaTCSF). We train the elaTCSF on various flicker detection datasets and establish the first variable refresh rate flicker detection dataset for further verification. Additionally, we contribute to resolving a longstanding debate on whether the flicker is more visible in peripheral vision. We demonstrate how elaTCSF can be used to predict flicker due to low-persistence in VR headsets, identify flicker-free VRR operational ranges, and determine flicker sensitivity in lighting design.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The perception of flicker has been a prominent concern in illumination and electronic display fields for over a century. background 1.00 0.03 0.02 asserted 1.00
s1 Traditional approaches often rely on Critical Flicker Frequency (CFF), primarily suited for high-contrast (full-on, full-off) flicker. background 0.97 0.03 0.02 asserted 0.96
s2 To tackle varying contrast flicker, the International Committee for Display Metrology (ICDM) introduced a Temporal Contrast Sensitivity Function TCSF$_{IDMS}$ within the Information Display Measurements Standard (IDMS). background 1.00 0.03 0.04 asserted 1.00
s3 Nevertheless, this standard overlooks crucial parameters: luminance, eccentricity, and area. gap 0.98 0.12 0.03 asserted 1.00
s4 Existing models incorporating these parameters are inadequate for flicker detection, especially at low spatial frequencies. gap 0.88 0.08 0.04 asserted 1.00
s5 To address these limitations, we extend the TCSF$_{IDMS}$ and combine it with a new spatial probability summation model to incorporate the effects of luminance, eccentricity, and area (elaTCSF). contribution 0.59 0.95 0.16 asserted 0.98
s6 We train the elaTCSF on various flicker detection datasets and establish the first variable refresh rate flicker detection dataset for further verification. contribution 0.70 0.97 0.09 asserted 1.00
s7 Additionally, we contribute to resolving a longstanding debate on whether the flicker is more visible in peripheral vision. contribution 0.90 0.74 0.08 asserted 0.81
s8 We demonstrate how elaTCSF can be used to predict flicker due to low-persistence in VR headsets, identify flicker-free VRR operational ranges, and determine flicker sensitivity in lighting design. contribution 0.65 0.94 0.46 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.92.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The perception of flicker has been a prominent concern in illumination and electronic display fields for over a century. Traditional approaches often rely on Critical Flicker Frequency (CFF), primarily suited for high-contrast (full-on, full-off) flicker. To tackle varying contrast flicker, the International Committee for Display Metrology (ICDM) introduced a Temporal Contrast Sensitivity Function TCSF$_{IDMS}$ within the Information Display Measurements Standard (IDMS). Nevertheless, this standard overlooks crucial parameters: luminance, eccentricity, and area. Existing models incorporating these parameters are inadequate for flicker detection, especially at low spatial frequencies. To address these limitations, we extend the TCSF$_{IDMS}$ and combine it with a new spatial probability summation model to incorporate the effects of luminance, eccentricity, and area (elaTCSF). We train the elaTCSF on various flicker detection datasets and establish the first variable refresh rate flicker detection dataset for further verification. Additionally, we contribute to resolving a longstanding debate on whether the flicker is more visible in peripheral vision. We demonstrate how elaTCSF can be used to predict flicker due to low-persistence in VR headsets, identify flicker-free VRR operational ranges, and determine flicker sensitivity in lighting design.

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