Is it time to upgrade your gaming monitor? – An application of a visual model
Thinking of upgrading your 2K 120Hz display to a 4K 144Hz model but not sure if you’ll notice any difference in quality while playing your favorite game? Or perhaps you’re shopping for a new monitor online but can’t decide which model to pick? It’s hard to make a decision about the visual quality of two displays without comparing them side-by-side in person. When this isn’t possible, objective quality metrics can serve as a reliable proxy for human perception.
Objective quality metrics are computational models of human perception that aim to mimic human behavior in different tasks. They can be used to compare the quality of two images, determine if an animation looks realistic, check if something is visible to the human eye, or compare different display configurations. Below, you’ll find an app that uses a visual model called MARRR to estimate what percentage of the population would prefer one display over another for a given type of content. Try inputting your current display configuration and the one you’re considering upgrading to, and see what the model predicts.
Enter your current and target display configuration below and click on Calculate. The app will tell you if you will notice a difference in the quality of displays.
Display spec |
Current display |
Target display |
|---|---|---|
Refresh rate: |
Hz |
Hz |
Screen width: |
cm |
cm |
Viewing distance: |
cm |
cm |
Horizontal resolution: |
px |
px |
FoV: |
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Content velocity: |
deg/sec |
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Results: |
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How MARRR Works
When an image moves on a display, it is susceptible to certain artifacts due to the nature of display technology and human perception. The two primary artifacts are judder and blur:
Judder occurs when smooth, continuous motion is replaced by non-continuous, shaky motion. (Refer to the previous blog for an example.) This artifact becomes more noticeable when fast motion is displayed on a screen with a low refresh rate.
Blur happens when either the screen resolution is low or when our eyes track a moving image. On LCD displays, an image is held for the entire duration of a frame. As our gaze follows a moving object on the screen, it pans across stationary pixels, causing the image to smear on the retina. This results in the perception of blur. The degree of blur increases with object velocity and decreases with higher refresh rates.
MARRR models judder and blur artifacts by analyzing them in the frequency domain to estimate the degradation in visual quality (in terms of smoothness and sharpness). The model is calibrated using psychophysical studies. You can find more details about this model here.
The Broader Context of Visual Models
Visual models like MARRR are widely used across industries, from calibrating display colors to saving GPU bandwidth. However, they should be applied with caution and within the appropriate context. For example, MARRR is designed to evaluate whether an average observer can perceive a difference in the quality between two displays. It does not predict whether your performance in tasks such as gaming will improve with a better display. For performance-related predictions, other models like gaze-timing are more suitable.