A Luminance-aware Model of Judder Perception

Chapiro, Alexandre; Atkins, Robin; Daly, Scott

doi:10.1145/3338696

Cited by 13 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study, Kuroki et al [2007] measured motion quality up to refresh rate of 480 Hz and found that the quality improves rapidly with increasing refresh rate but saturates after 240 Hz for natural images. Similar trends were also reported by [Chapiro et al 2019;Denes et al 2020;Wilcox et al 2015]. Similar to temporal aliasing, the visibility of flicker decreases with increasing refresh rate.…”

Section: Factors Affecting Perception Of Motion Artifactssupporting

confidence: 87%

“…Primarily, there are two main approaches of modeling quality: blackbox metrics and white-box metrics. Black-box metrics [Chapiro et al 2019;Debattista et al 2018] explain the data by fitting an arbitrary function, and typically perform well within the domain of the dataset, but are unable to extrapolate beyond it. White-box metrics on the other hand rely on psychophysical models and are better at extrapolating the predictions.…”

Section: Models Of Motion Qualitymentioning

confidence: 99%

See 1 more Smart Citation

Perceptual model for adaptive local shading and refresh rate

2021

View full text Add to dashboard Cite

Intel 2019]. All these works propose a dynamic quality control mechanism that allocates the rendering budget to those aspects of an image or animation that have the highest impact on the overall quality. In this work, we propose to control both the VRS state map and the refresh rate, based on all major factors affecting image quality: texture content, on-screen velocities, luminance, effective resolution, and display persistence. We build on the work of Denes et al. [2020], and extend the visual quality model to account for the important effect of texture content and VRS resolution. In contrast to that work, we offer local, rather than global, control of the resolution via VRS without the need for eye tracking.The key component of our Adaptive Local Shading and Refresh Rate (ALSaRR) method is a new Content-adaptive Metric of Judder, Aliasing and Blur (CaMoJAB) (Section 4). The metric is based on psychophysical models of contrast sensitivity with only a few parameters fitted to the data. We calibrate and validate our metric on various existing datasets as well as our new dataset collected by conducting a subjective quality experiment. The experiment measures the perceived loss of quality due to shading rate reduction under a large range of display refresh rates, resolutions, display persistence, luminance, contrast, and content velocity (Section 4.3). Our ALSaRR method uses the new metric to create per-texture quality functions, which are used for an approximate solution of the knapsack problem: maximize perceived quality for a given rendering budget (Section 5). Unlike the method of Yang et al. [2019], which controls VRS to avoid any visual loss regardless of the per-frame rendering cost, our goal is to find the best trade-off of spatio-temporal resolution under a limited rendering budget.The main contributions of our work 1 are:• Content-adaptive Metric of Judder, Aliasing and Blur (CaMo-JAB), derived from psychophysical models and calibrated on several datasets. • A dataset of motion quality for animations rendered with different shading rates, motion velocities, textures, refresh rates and display angular resolution (in pixels-per-degree), and persistence. • Adaptive Local Shading and Refresh Rate (ALSaRR) method for control of real-time rendering, which maximizes the quality of animation under a limited budget.

show abstract

Section: Factors Affecting Perception Of Motion Artifactssupporting

confidence: 87%

Section: Models Of Motion Qualitymentioning

confidence: 99%

Perceptual model for adaptive local shading and refresh rate

2021

View full text Add to dashboard Cite

show abstract

“…Perceived motion quality of panning (horizontal movement on the screen) was investigated recently by Chapiro et al [2019]. The authors measured subjective motion artifact scores for refresh rates typical of modern televisions (30, 60, and 120 Hz) across a range of luminance (2.5-40 cd/m 2 ) and camera panning speeds (2-6.6 deg /s).…”

Section: Motion Quality In Filmsmentioning

confidence: 99%

“…We follow their approach in our target application and also optimize rendering quality under a constrained budget using a model. However, our model accounts for the velocity of motion, which was shown to be a major factor affecting motion quality [Chapiro et al 2019]. Furthermore, our intention is to build an explainable model, accounting for the underlying mechanisms of the visual system, which can generalize across a wide range of input parameters.…”

Section: Refresh Rate Vs Resolutionmentioning

confidence: 99%

A perceptual model of motion quality for rendering with adaptive refresh-rate and resolution

et al. 2020

View full text Add to dashboard Cite

Limited GPU performance budgets and transmission bandwidths mean that real-time rendering often has to compromise on the spatial resolution or temporal resolution (refresh rate). A common practice is to keep either the resolution or the refresh rate constant and dynamically control the other variable. But this strategy is non-optimal when the velocity of displayed content varies. To find the best trade-off between the spatial resolution and refresh rate, we propose a perceptual visual model that predicts the quality of motion given an object velocity and predictability of motion. The model considers two motion artifacts to establish an overall quality score: non-smooth (juddery) motion, and blur. Blur is modeled as a combined effect of eye motion, finite refresh rate and display resolution. To fit the free parameters of the proposed visual model, we measured eye movement for predictable and unpredictable motion, and conducted psychophysical experiments to measure the quality of motion from 50 Hz to 165 Hz. We demonstrate the utility of the model with our on-the-fly motion-adaptive rendering algorithm that adjusts the refresh rate of a G-Sync-capable monitor based on a given rendering budget and observed object motion. Our psychophysical validation experiments demonstrate that the proposed algorithm performs better than constant-refresh-rate solutions, showing that motion-adaptive rendering is an attractive technique for driving variable-refresh-rate displays.

show abstract

“…An accurate presentation of stimuli is a prerequisite for accurate results of visual perception experiments. However, most modern monitors present motion objects at discrete locations, showing an approximate continuous motion process (Chapiro et al, 2019). Motion blur will occur when the motion speed or motion frequency is too high; it violates the assumption of smooth motion and causes stimulus distortion and false experimental results (Tourancheau et al, 2009;Watson and Ahumada, 2011).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effect of the Refresh Rate of a Device on Various Motion Stimulation Frequencies Based on Steady-State Motion Visual Evoked Potentials

Han

Zheng

et al. 2022

Front. Neurosci.

View full text Add to dashboard Cite

The refresh rate is one of the important parameters of visual presentation devices, and assessing the effect of the refresh rate of a device on motion perception has always been an important direction in the field of visual research. This study examined the effect of the refresh rate of a device on the motion perception response at different stimulation frequencies and provided an objective visual electrophysiological assessment method for the correct selection of display parameters in a visual perception experiment. In this study, a flicker-free steady-state motion visual stimulation with continuous scanning frequency and different forms (sinusoidal or triangular) was presented on a low-latency LCD monitor at different refresh rates. Seventeen participants were asked to observe the visual stimulation without head movement or eye movement, and the effect of the refresh rate was assessed by analyzing the changes in the intensity of their visual evoked potentials. The results demonstrated that an increased refresh rate significantly improved the intensity of motion visual evoked potentials at stimulation frequency ranges of 7–28 Hz, and there was a significant interaction between the refresh rate and motion frequency. Furthermore, the increased refresh rate also had the potential to enhance the ability to perceive similar motion. Therefore, we recommended using a refresh rate of at least 120 Hz in motion visual perception experiments to ensure a better stimulation effect. If the motion frequency or velocity is high, a refresh rate of≥240 Hz is also recommended.

show abstract

A Luminance-aware Model of Judder Perception

Cited by 13 publications

References 35 publications

Perceptual model for adaptive local shading and refresh rate

Perceptual model for adaptive local shading and refresh rate

A perceptual model of motion quality for rendering with adaptive refresh-rate and resolution

Assessing the Effect of the Refresh Rate of a Device on Various Motion Stimulation Frequencies Based on Steady-State Motion Visual Evoked Potentials

Contact Info

Product

Resources

About