A local model of eye adaptation for high dynamic range images

Ledda, Patrick; Santos, Luís Paulo; Chalmers, Alan

doi:10.1145/1029949.1029978

Cited by 88 publications

(60 citation statements)

References 33 publications

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“…Edge-stopping filters limit such spatial processing to the regions with homogeneous pixel intensity, which greatly reduces the halo artifacts, but also ignores glare due to bright pixels in the proximity of high-contrast edges. The spatial support of such filters is typically fixed [Ledda et al 2004;Kuang et al 2007] (e.g., to 2% of the image size) or adaptively expanded as a function of the local variability in pixel intensities [Reinhard et al 2002].…”

Section: Related Workmentioning

confidence: 99%

“…For example, the Naka-Rushton response was originally derived as a function of adaptation state for single receptors [Naka and Rushton 1966;Valeton 1983], but its variants are commonly used for larger adaptation areas, sometimes the entire image, without any in-depth justification [Tumblin et al 1999;Pattanaik et al 2000;Irawan et al 2005;Ledda et al 2004;Reinhard et al 2002;Kim et al 2009;Reinhard and Devlin 2005]. In this work, we propose a perceptually grounded model of local adaptation that accounts for the spatial configuration of HDR pixels, and we show that its use can be beneficial in many different applications (Section 7).…”

Section: Related Workmentioning

confidence: 99%

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A model of local adaptation

2015

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A model of local adaptation

2015

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“…11 According to Knoblauch et al, a large contrast in luminance increases visual recognition, 12 whereas the continuity of the contrast has contributed to visual discomfort. 5 Therefore, human vision provides a time-dependent adaptation 11,13,14 to reduce visual fatigue. Some studies have also revealed that the maximum luminance contrast between text and background is inadequate for reading a display because an excessively high contrast leads to visual stress.…”

Section: Introductionmentioning

confidence: 99%

Adaptive luminance contrast for enhancing reading performance and visual comfort on smartphone displays

Suk

2014

Opt. Eng

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Abstract. This study developed a model for setting the adaptive luminance contrast between text and background for enhancing reading performance and visual comfort on smartphone displays. The study was carried out in two experiments. In Experiment I, a user test was conducted to identify the optimal luminance contrast with regard to subjects' reading performance, measured by lines of text reading and visual comfort, assessed by selfreport after the reading. Based on the empirical results of the test, an ideal adaptive model which decreases the luminance contrast gradually with passage of time was developed. In Experiment II, a validation test involving reading performance, visual comfort, and physiological stress measured by a brainwave analysis using an electroencephalogram confirmed that the proposed adaptive luminance contrast is adequate for prolonged text reading on smartphone displays. The developed model enhances both reading performance and visual comfort as well as reduces the energy consumption of a smartphone; hence, it is expected that this study will be applied to diverse kinds of visual display terminals.

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“…The global tone mapping method using only one mapping function has a relatively simple computation, though it is insufficient to address wide dynamic range, whereas the local tone mapping method has an adaptive function that may vary depending on spatially adjacent pixels. Furthermore, certain local methods adopt human visual properties for local contrast enhancement, such as image color appearance model (iCAM)-based methods, 3,4 logarithmic mapping, 5 local eye adaptation, 6 and histogram adjustment. 7 These make images similar to real scenes that an observer would perceive.…”

Section: Introductionmentioning

confidence: 99%

Visual sensitivity correlated tone reproduction for low dynamic range images in the compression field

Lee

Kwon

et al. 2014

Opt. Eng

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Abstract. An image toning method for low dynamic range image compression is presented. The proposed method inserts tone mapping into JPEG baseline instead of postprocessing. First, an image is decomposed into detail, base, and surrounding components in terms of the discrete cosine transform coefficients. Subsequently, a luminance-adaptive tone mapping based on the human visual sensitivity properties is applied. In addition, compensation modules are added to enhance the visually sensitive factors, such as saturation, sharpness, and gamma. A comparative study confirms that the transmitted compression images have good image quality. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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A local model of eye adaptation for high dynamic range images

Cited by 88 publications

References 33 publications

A model of local adaptation

A model of local adaptation

Adaptive luminance contrast for enhancing reading performance and visual comfort on smartphone displays

Visual sensitivity correlated tone reproduction for low dynamic range images in the compression field

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