Tone mapping for high dynamic range displays

Meylan, Laurence; Daly, Scott; Süsstrunk, Sabine

doi:10.1117/12.706472

Cited by 84 publications

(57 citation statements)

References 8 publications

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“…A further extension of the method allows handling video by ensuring temporal coherence of the expansion map between frames [7]. Meylan and colleagues [20,21] opt for a similar yet simpler approach, applying a steep linear expansion to saturated areas, and a less steep linear expansion function to the rest. Rempel et al [24] define a brightness enhancement function (BEF), similar to an expansion map, computed by blurring a mask of all saturated pixels.…”

Section: Previous Workmentioning

confidence: 99%

Dynamic range expansion based on image statistics

Masiá

Serrano

Gutiérrez

2015

Multimed Tools Appl

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As the dynamic range of displays keeps increasing, there is a need for reverse tone mapping methods, which aim at expanding the dynamic range of legacy low dynamic range images for viewing on higher dynamic range displays. While a number of strategies have been proposed, most of them are designed for well-exposed input images and are not optimal when dealing with ill-exposed (under-or over-exposed) content. Further, this type of content is more prone to artifacts which may arise when using local methods. In this work, we build on an existing, automatic, global reverse tone mapping operator based on a gamma expansion. We improve this method by providing a new way for automatic parameter calculation from the image statistics. We show that this method yields better results across the whole range of exposures.

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Section: Previous Workmentioning

confidence: 99%

Dynamic range expansion based on image statistics

Masiá

Serrano

Gutiérrez

2015

Multimed Tools Appl

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“…On the other hand, the cones (photoreceptors) adapt their sensitivity to the average luminance of the scene. Finally, local adaptation modulates contrasts [38].…”

Section: Tone Mappingmentioning

confidence: 99%

“…Taking into account the fact that common image representation devices, such as Liquid Crystal Displays (LCD), have a limited dynamic range, in order to directly expose HDR images on these conventional screens, it is necessary to apply luminance compression methods such as tone or gamut mapping [17,[36][37][38]. With tone mapping, the dynamic range of the radiance maps is compressed to match the dynamic range of the display device [23].…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…Biologically speaking, the radiance is captured by rods and cones in the retina and then passed into the visual system. Subsequently those signals are nonlinearly processed by several layers of neurons, which form an image called percept that, in fact, does not correspond to the physical radiance of the scene [38].…”

Section: Tone Mappingmentioning

confidence: 99%

“…One of the tone mapping functions is to simulate the processing of the human visual system in order to make the images perceptually significant [38]. Thus, the task underlying a tone mapping is therefore to strike a balance between the emphasis of all features on an image and presenting a good contrast in the production of the LDR image [20].…”

Section: Tone Mappingmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

et al. 2017

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Precision agriculture is gaining an increasing interest in the current farming paradigm. This new production concept relies on the use of information technology (IT) to provide a control and supervising structure that can lead to better management policies. In this framework, imaging techniques that provide visual information over the farming area play an important role in production status monitoring. As such, accurate representation of the gathered production images is a major concern, especially if those images are used in detection and classification tasks. Real scenes, observed in natural environment, present high dynamic ranges that cannot be represented by the common LDR (Low Dynamic Range) devices. However, this issue can be handled by High Dynamic Range (HDR) images since they have the ability to store luminance information similarly to the human visual system. In order to prove their advantage in image processing, a comparative analysis between LDR and HDR images, for fruits detection and counting, was carried out. The obtained results show that the use of HDR images improves the detection performance to more than 30% when compared to LDR.

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Rendering of HDR images to improve brightness discrimination

Hong

Shin

Song

et al. 2016

Jnl Soc Info Display

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The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.

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Tone mapping for high dynamic range displays

Cited by 84 publications

References 8 publications

Dynamic range expansion based on image statistics

Dynamic range expansion based on image statistics

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

Rendering of HDR images to improve brightness discrimination

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