Gaze-Dependent Tone Mapping

Mantiuk, Radosław; Markowski, Mateusz

doi:10.1007/978-3-642-39094-4_48

Cited by 11 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the appearance of a color can be moved along the CIELAB axes, colors at the edge of the display gamut can be pushed outside of the physical gamut of the screen, essentially increasing its perceivable gamut size. This could be used to display images with higher perceived luminance ranges (as suggested by [3,24]), but also higher perceived chromaticity, allowing the display to render more vivid photographs or display medical images (e.g., MRI, X-ray) with more observable detail.…”

Section: Discussionmentioning

confidence: 99%

“…Yamauchi, Mikami and Ouda [42] investigated global tone mapping operators driven by parameters derived from the focal area and compared them in a subjective task, but their comparison had no baseline nor showed statistically-reliable effects. Similar gaze-contingent tone mapping approaches have been proposed (e.g., [24,30]), but without empirical evaluation. As a result, there is no evidence that gaze-contingent tone mapping provides any benefit over static tone mapping.…”

Section: Gaze-contingent Displays (Gcds)mentioning

confidence: 99%

See 1 more Smart Citation

Gaze-Contingent Manipulation of Color Perception

Mauderer

Flatla

Nacenta

2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Using real time eye tracking, gaze-contingent displays can modify their content to represent depth (e.g., through additional depth cues) or to increase rendering performance (e.g., by omitting peripheral detail). However, there has been no research to date exploring how gaze-contingent displays can be leveraged for manipulating perceived color. To address this, we conducted two experiments (color matching and sorting) that manipulated peripheral background and object colors to influence the user's color perception. Findings from our color matching experiment suggest that we can use gaze-contingent simultaneous contrast to affect color appearance and that existing color appearance models might not fully predict perceived colors with gaze-contingent presentation. Through our color sorting experiment we demonstrate how gaze-contingent adjustments can be used to enhance color discrimination. Gazecontingent color holds the promise of expanding the perceived color gamut of existing display technology and enabling people to discriminate color with greater precision.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Gaze-contingent Displays (Gcds)mentioning

confidence: 99%

Gaze-Contingent Manipulation of Color Perception

Mauderer

Flatla

Nacenta

2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…When viewing images on a regular (LDR) display, the adaptation changes to a lesser degree as the luminance range reproduced on the display is much smaller. However, if real-time information about gaze position is available from an eye-tracker, the real-world adaptation process can be simulated on a regular display [Mantiuk and Markowski 2013].…”

Section: Simulation Of Afterimagesmentioning

confidence: 99%

“…We reproduced a gaze-dependent tone mapping system similar to the one presented in [Mantiuk and Markowski 2013]. Given an HDR image as input, our model predicts the spatial map of adaptation luminance levels that the eye would arrive at in the real-world scene.…”

Section: Simulation Of Afterimagesmentioning

confidence: 99%

A model of local adaptation

2015

View full text Add to dashboard Cite

Figure 1: Processing steps of our spatial adaptation model. First, optical glare is simulated to produce a retinal image. Then, the local luminance adaptation map is computed using our novel adaptation model. The plots below show the luminance profile for the pixels marked with the dashed-orange line. Note that the eye cannot adapt to small highlights as shown by the flattened blue curve in the "adaptation luminance" plot. As one of the applications, the adaptation map can be used to estimate the smallest visible contrast in complex images (detection map) and therefore represents a visibility tolerance for each pixel. AbstractThe visual system constantly adapts to different luminance levels when viewing natural scenes. The state of visual adaptation is the key parameter in many visual models. While the time-course of such adaptation is well understood, there is little known about the spatial pooling that drives the adaptation signal. In this work we propose a new empirical model of local adaptation, that predicts how the adaptation signal is integrated in the retina. The model is based on psychophysical measurements on a high dynamic range (HDR) display. We employ a novel approach to model discovery, in which the experimental stimuli are optimized to find the most predictive model. The model can be used to predict the steady state of adaptation, but also conservative estimates of the visibility (detection) thresholds in complex images. We demonstrate the utility of the model in several applications, such as perceptual error bounds for physically based rendering, determining the backlight resolution for HDR displays, measuring the maximum visible dynamic range in natural scenes, simulation of afterimages, and gaze-dependent tone mapping.

show abstract

“…GDOT treats distinct objects in a scene as potential fixation targets and with high probability computes the fixation directions towards the target observed by the viewer. It was demonstrated that this technique can be successfully applied to the depthof-field effect simulation [10], in tone mapping [6], and as an auxiliary controller in a computer game [2].…”

Section: Introductionmentioning

confidence: 99%

Gaze-Driven Object Tracking Based on Optical Flow Estimation

Bazyluk

Mantiuk

2014

Computer Vision and Graphics

Self Cite

View full text Add to dashboard Cite

Abstract. To efficiently deploy eye tracking within gaze-dependent image analysis tasks, we present an optical flow-aided extension of the gaze-driven object tracking technique (GDOT). GDOT assumes that objects in a 3-dimensional space are fixation targets and with high probability computes the fixation directions towards the target observed by the user. We research whether this technique proves its efficiency for video footage in 2-dimensional space in which the targets are tracked by optical flow tracking technique with inaccuracies characteristic for this method. In the conducted perceptual experiments, we assess efficiency of the gazedriven object identification by comparing results with the reference data where attended objects are known. The GDOT extension reveals higher errors in comparison to 3D graphics tasks but still outperforms typical fixation techniques.

show abstract

Gaze-Dependent Tone Mapping

Cited by 11 publications

References 19 publications

Gaze-Contingent Manipulation of Color Perception

Gaze-Contingent Manipulation of Color Perception

A model of local adaptation

Gaze-Driven Object Tracking Based on Optical Flow Estimation

Contact Info

Product

Resources

About