Quantum Dot Display Burn-In Risk Calculator

How This Quantum Dot Burn-In Risk Calculator Works

This calculator estimates how likely a quantum dot (QD) display is to show noticeable burn-in or permanent image retention over a multi‑year period. It uses your inputs for daily usage, average brightness, blue subpixel stress, operating temperature, and a baseline lifetime rating to approximate how quickly the panel ages under your conditions.

The model is deliberately simplified. It is designed to give you a comparative risk gauge, not a guarantee. The output is most useful for questions such as “Is my current setup closer to low, moderate, or high burn-in risk?” or “How much would lowering brightness or temperature help?”

Key Concepts and Inputs

The calculator focuses on the stress experienced by the blue subpixels that excite the quantum dots. These age faster than red and green elements and often set the practical lifetime of the display.

• Daily Usage (hours) – How many hours per day the screen is on. More hours per day mean the total lifetime is consumed faster.
• Average Brightness (nits) – Typical brightness when you use the display. Standard SDR content often sits around 200–400 nits; HDR highlights can be much brighter. Higher brightness increases wear.
• Blue Subpixel Stress Factor (0–1) – A user-estimated factor for how hard the blue subpixels are driven relative to a mixed, varied content baseline. Values near 1 correspond to static logos, HUDs, or bright UI elements that stay in the same place. Lower values (for example 0.3–0.5) represent more varied, full-screen, or darker content.
• Operating Temperature (°C) – Approximate panel surface temperature during use. Higher temperatures accelerate chemical degradation; cooler operation slows it down.
• Baseline Lifetime (hours) – A manufacturer or reviewer quoted lifetime figure, often defined as the number of hours until luminance drops to 50% (L50) under reference conditions. The calculator scales this value up or down based on your settings.

Approximate Lifetime and Burn-In Probability Formulas

Internally, the tool forms a rough estimate of an effective lifetime that accounts for brightness, stress, and temperature. It then converts that effective lifetime into a probability of burn-in within a given time horizon (such as five years of use).

A simplified version of the effective lifetime idea can be written as:

where:

• is the effective lifetime under your conditions.
• is the baseline lifetime input.
• represents average brightness, represents the stress factor, and represents temperature.

The calculator scales lifetime down when brightness, stress, or temperature rise above their nominal values. For example, if temperature is above a reference 25 °C, a simple multiplier reduces lifetime by a small percentage per degree above that point. The exact coefficients are chosen to give realistic, but still approximate, behavior across everyday use cases.

Once an effective lifetime is computed, the tool converts that number into a probability using a smooth S‑shaped curve (a logistic function). In concept, the probability of noticeable burn-in by a target time (for example, five years) can be written in shorthand as:

Here, is a dimensionless burn-in risk index derived from the ratio of your usage time to the effective lifetime, and , are constants that set how quickly probability rises. You do not need to work with these equations directly; they are shown to make the assumptions transparent.

Interpreting Your Burn-In Risk Result

The calculator typically outputs a probability of noticeable burn-in within a specific time frame, along with a qualitative label. You can use these broad bands as a guide:

• 0–20% (Low) – Under typical consumer use, burn-in is unlikely within the modeled time horizon. Some gradual uniform wear is still normal.
• 20–50% (Moderate) – There is a meaningful chance of mild image retention, especially if you use static UI elements, news tickers, or game HUDs for many hours.
• 50–80% (High) – Your settings place the panel under substantial stress. Permanent retention of static elements becomes increasingly probable.
• 80–100% (Very High) – The model suggests that burn-in is very likely if usage continues unchanged over the modeled period.

Remember that these ranges are approximate. Two displays with the same inputs could age differently due to panel-to-panel variation, firmware differences, or manufacturing improvements.

Worked Example

Consider a gaming monitor with the following characteristics:

• Daily Usage: 6 hours
• Average Brightness: 400 nits
• Blue Subpixel Stress Factor: 0.8 (bright HUD and static UI elements)
• Operating Temperature: 30 °C (slightly warm room, tower PC radiating heat)
• Baseline Lifetime: 10,000 hours (reference L50 figure)

First, the calculator estimates an effective lifetime by scaling down 10,000 hours for the higher-than-nominal brightness, high stress factor, and 5 °C above the 25 °C reference. Suppose this yields an effective lifetime on the order of 6,000–7,000 hours.

Next, daily usage of 6 hours corresponds to roughly 2,190 hours per year. Over five years, that is about 10,950 hours. Your total usage therefore exceeds the effective lifetime, which pushes the risk index into the higher part of the curve.

In this kind of scenario, the output might land in the “High” or “Very High” probability band. The model is essentially telling you that, under these conditions, there is a substantial chance that static HUD elements or channel logos become permanently visible over time.

If you rerun the calculator with reduced brightness (for example 250 nits), a lower stress factor (0.4, representing more varied content), and cooler operation (27 °C), the effective lifetime increases. The resulting probability may drop into the “Moderate” range, illustrating how small changes in usage can materially affect risk.

What Most Strongly Affects Quantum Dot Lifespan?

Several practical factors dominate the model and align with real-world behavior:

• Brightness level – Running close to maximum brightness for long periods increases current through the blue subpixels and heats the panel. This has a compounding effect on wear.
• Content pattern – Static layouts localize stress. News tickers, broadcast logos, scoreboard graphics, and game HUDs can all leave imprints if they stay in place for thousands of hours.
• Temperature – A higher panel temperature accelerates the chemical reactions that age phosphors, transport layers, and encapsulation materials.
• Total on-time – Even at reasonable brightness, very high usage (for example, signage running 16 hours per day) quickly accumulates wear.

Comparison: Different Usage Profiles

The table below illustrates how different usage styles affect qualitative burn-in risk, assuming similar modern quantum dot panels. The numbers are illustrative rather than exact predictions.

How to Reduce Burn-In Risk

Regardless of your specific result, several straightforward adjustments can reduce risk:

• Lower average brightness – Even a modest drop (for example, from 400 to 300 nits) can measurably extend effective lifetime in the model.
• Enable automatic brightness – Dynamic adjustment often keeps brightness lower for dim rooms while preserving comfort.
• Vary content – Avoid leaving static images, dashboards, or game menus visible for long periods. Use screen savers where appropriate.
• Use built-in protections – Many TVs and monitors offer pixel-shift, logo dimming, or panel refresh features. Keeping these enabled spreads or reverses wear.
• Manage temperature – Ensure good ventilation, keep the display away from direct sunlight or heat sources, and avoid enclosing it in tight cabinets.

Assumptions and Limitations of the Model

This calculator is for educational and planning purposes only. It is not a diagnostic tool and does not represent a manufacturer’s warranty, reliability guarantee, or official specification. Several important assumptions and limitations apply:

• Simplified physics – The model uses basic scaling relationships for brightness, temperature, and stress rather than full physical or statistical reliability models. Real devices may follow more complex aging behavior.
• Approximate coefficients – Parameters such as the temperature acceleration per degree Celsius or the way brightness scales wear are approximate and tuned for plausibility, not calibrated to a specific product line.
• Baseline lifetime context – The baseline lifetime you enter may be defined under controlled lab conditions (for example, fixed brightness, uniform patterns, and specific failure criteria). Actual field conditions can diverge significantly.
• User-estimated stress factor – The blue subpixel stress factor is a proxy for your mix of content and static elements. Because it is user-estimated, two people with similar usage might choose different values and see different results.
• Panel and firmware variation – Different generations of quantum dot displays, different backlight designs, and different burn-in mitigation algorithms can lead to very different real-world outcomes even with identical inputs.
• Time horizon – The probability is interpreted over a defined period (for example, five years of use). Using the display for a much shorter or longer time than implied by the model will change the real risk.
• Not a warranty or service guide – Always consult your product documentation for official guidance, recommended operating ranges, and warranty terms.

You should therefore treat the calculated probability as a relative risk index for comparing scenarios (for example, current vs. reduced brightness) rather than as an absolute, device-specific prediction.

How Quantum Dot Burn-In Compares to Other Technologies

Quantum dot LCDs and quantum dot OLEDs behave differently from traditional OLED and conventional LCD panels:

• Quantum dot LCD (QD-LCD) – Uses a blue LED backlight with quantum dot conversion for red and green. Burn-in risk is generally lower than self-emissive OLED panels, but backlight and filter aging can still produce non-uniformity over long periods, especially under static content.
• Quantum dot OLED (QD-OLED) – Self-emissive blue OLED subpixels drive quantum dots. These panels can achieve excellent brightness and color, but the blue OLED layer is still subject to differential aging similar to other OLED technologies.
• Conventional OLED – Each subpixel is organic and emissive, making them more sensitive to static content but also capable of very high contrast.
• Mini-LED / standard LCD – These rely on inorganic LEDs and LCD shutters. They can still show image persistence, but classic burn-in is typically less severe than in emissive technologies.

The same general principles apply across all of these technologies: moderate brightness, varied content, and reasonable temperatures are your best defenses against long-term image retention.

Using the Calculator Effectively

To get the most value from the tool:

• Start with your current settings and usage habits to establish a baseline risk.
• Experiment with lower brightness, different stress factors, and improved cooling to see how the probability changes.
• Use the comparison to inform choices like whether to enable logo dimming or how bright to set HDR modes.

Because the model is approximate, focus on the direction and relative size of changes rather than the exact percentage numbers.

Disclaimer

This burn-in risk calculator is an informational tool only. It does not predict the behavior of any specific device, cannot account for all real-world conditions, and should not be used as the sole basis for purchase decisions, warranty claims, or safety judgments. Always follow the recommendations of your device manufacturer.