Predicting Visible Image Differences Under Varying Display Brightness and Viewing Distance

Ye, Nanyang; Wolski, Krzysztof; Mantiuk, Rafał

doi:10.1109/cvpr.2019.00558

Cited by 15 publications

(27 citation statements)

References 22 publications

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“…Unlike the visual difference maps produced by other metrics [Mantiuk et al 2011;Wolski et al 2018;Ye et al 2019], which saturate when the distortions are well visible, our maps represent both nearthreshold and supra-threshold distortions, scaled in JOD units. We…”

Section: Jod Regressionmentioning

confidence: 66%

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FovVideoVDP

et al. 2021

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FovVideoVDP is a video difference metric that models the spatial, temporal, and peripheral aspects of perception. While many other metrics are available, our work provides the first practical treatment of these three central aspects of vision simultaneously. The complex interplay between spatial and temporal sensitivity across retinal locations is especially important for displays that cover a large field-of-view, such as Virtual and Augmented Reality displays, and associated methods, such as foveated rendering. Our metric is derived from psychophysical studies of the early visual system, which model spatio-temporal contrast sensitivity, cortical magnification and contrast masking. It accounts for physical specification of the display (luminance, size, resolution) and viewing distance. To validate the metric, we collected a novel foveated rendering dataset which captures quality degradation due to sampling and reconstruction. To demonstrate our algorithm's generality, we test it on 3 independent foveated video datasets, and on a large image quality dataset, achieving the best performance across all datasets when compared to the state-of-the-art.

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Section: Jod Regressionmentioning

confidence: 66%

“…As we focus on methods that could be used as optimization criteria, we do not analyze the metrics intended to produce visual difference maps [Aydin et al 2010;Daly 1993;Wolski et al 2018;Ye et al 2019] but that do not offer single-valued quality predictions.…”

Section: Quality Metricsmentioning

confidence: 99%

FovVideoVDP

et al. 2021

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“…As flicker is often perceived in multiple parts of the image, and location information is crucial for P det (x, y), we designed a marking experiment. Such experiments have been utilized to calibrate similar metrics for image difference predictors [18,19].…”

Section: Flicker Marking Experimentsmentioning

confidence: 99%

Predicting visible flicker in temporally changing images

Dénes¹,

Mantiuk²

2020

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“…However, most image quality assessment (IQA) metrics do not account for display characteristics such as the dynamic range and brightness of the display, influencing the perceived image quality. For example, compression artifacts are more visible on a bright HDR display, than on a dimmed mobile phone [6]. The plethora of display types motivates the need for a new, photometric IQA metric that accounts for absolute image luminance and can operate on both HDR and SDR images.…”

Section: Introductionmentioning

confidence: 99%

Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality

Mikhailiuk

Pérez-Ortiz

Yue

et al. 2022

IEEE Trans. Multimedia

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Increasing popularity of high-dynamic-range (HDR) image and video content brings the need for metrics that could predict the severity of image impairments as seen on displays of different brightness levels and dynamic range. Such metrics should be trained and validated on a sufficiently large subjective image quality dataset to ensure robust performance. As the existing HDR quality datasets are limited in size, we created a Unified Photometric Image Quality dataset (UPIQ) with over 4,000 images by realigning and merging existing HDR and standard-dynamic-range (SDR) datasets. The realigned quality scores share the same unified quality scale across all datasets. Such realignment was achieved by collecting additional cross-dataset quality comparisons and re-scaling data with a psychometric scaling method. Images in the proposed dataset are represented in absolute photometric and colorimetric units, corresponding to light emitted from a display. We use the new dataset to retrain existing HDR metrics and show that the dataset is sufficiently large for training deep architectures. We show the utility of the dataset on brightness aware image compression.

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Predicting Visible Image Differences Under Varying Display Brightness and Viewing Distance

Cited by 15 publications

References 22 publications

FovVideoVDP

FovVideoVDP

Predicting visible flicker in temporally changing images

Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality

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