Time-dependent visual adaptation for fast realistic image display

Pattanaik, Sumanta; Tumblin, Jack; Yee, Hector; Greenberg, Donald P.

doi:10.1145/344779.344810

Cited by 242 publications

(181 citation statements)

References 19 publications

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“…The first five LDR images generated by the TMOs developed by Reinhard et al [15], Drago et al [16], Durand & Dorsey [17], Mantiuk et al [18] and Pattanaik et al [19] are computed using the publicly available software Qtpfsgui [20]. In addition, the last three LDR images were created using the built-in TMOs in Adobe Photoshop, namely "Exposure and Gamma," "Equalize Histogram," and "Local Adaptation," respectively.…”

Section: Test Protocolmentioning

confidence: 99%

Integrating Visual Saliency Information into Objective Quality Assessment of Tone-Mapped Images

Liu

Zhang

et al. 2014

Intelligent Computing Theory

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Abstract. Tone-mapped images are the low dynamic range (LDR) images converted from high dynamic range (HDR) images. Recently, the objective quality assessment of tone-mapped images is becoming a challenging problem. However, there is no mature algorithm to deal with this issue until the tone-mapped image quality index (TMQI) was proposed recently, which is tone-mapped image quality index (TMQI). Unfortunately, the pooling method of the structural fidelity map in TMQI is the simple "mean", which makes the result unsatisfying. On the other hand, recent studies have found that different locations of an image may have different contributions to the quality perception of the human visual system. The significance of a local image region can be well characterized by a visual saliency (VS) model. Inspired by this insight, in this paper, we propose a VS-based pooling strategy for the objective quality assessment of tone-mapped images. The experimental results clearly demonstrate the efficacy of our proposed method.Keywords: Tone-mapped images, high dynamic range images, objective quality assessment, visual saliency. IntroductionRecently, researchers have shown a growing interest in high dynamic range (HDR) images. Compared to the low dynamic range (LDR) images, the range of intensity levels of HDR images is largely wider, thus enjoying advantages. Specifically, the range of intensity level of HDR images could be on the order of 10000 to 1, which allows for accurate representations of luminance variations in real scenes, ranging from direct sunlight to faint starlight [1], while the LDR images only have 256 intensity levels. However, General displays are designed for displaying LDR images, so we must use tone-mapping operators (TMOs) to create the corresponding LDR images for the visualization of HDR images. As the reduction in dynamic range, the LDR images converted from HDR images cannot preserve all the information. With an increasing number of TMOs being developed, a question appears, that is which TMO has a better performance? This question drives us to develop an index to compare them, and then utilize the index for optimizing parameters in TMOs.

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Section: Test Protocolmentioning

confidence: 99%

Integrating Visual Saliency Information into Objective Quality Assessment of Tone-Mapped Images

Liu

Zhang

et al. 2014

Intelligent Computing Theory

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“…[10] performed this process through quantization, [16] through histogram adjustment, and [30] through a form of anisotropic diffusion. [21] modeled the effect of visual adaptation in viewing high-dynamic range images, and later work [22] modeled global time-dependent effects. [30] discussed a foveal display program in which the exposure of a high-dynamic range image was adjusted according to user mouse input, but was implemented in software only.…”

Section: Related Workmentioning

confidence: 99%

“…In auto-exposure mode, the system samples pixels near the center of the visible portion of the panorama. The system then adjusts the exposure level to correctly expose the image for these pixel values, similar to [28,22]. To keep the exposure from fluctuating in an unpleasant manner, we do not adjust exposure directly, but model it as a high friction spring system, where the rest state is set to the desired exposure.…”

Section: Visualizing Hdr Scenerymentioning

confidence: 99%

“…To keep the exposure from fluctuating in an unpleasant manner, we do not adjust exposure directly, but model it as a high friction spring system, where the rest state is set to the desired exposure. More sophisticated models of adaptation as in [28,22] could also be investigated.…”

Section: Visualizing Hdr Scenerymentioning

confidence: 99%

“…The exposure level can be dynamically adjusted to map a particular section of the full dynamic range of the scene to the displayable dynamic range of the output device. Our technique is similar in motivation to the dynamic visual adaptation work recently presented by Pattanaik et al [22], but focuses on how such visual adaptation can be accomplished in real time using current graphics hardware. Central to our technique is the use of high- .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time High-Dynamic Range Texture Mapping

Cohen¹,

Tchou²,

Hawkins³

et al. 2001

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Abstract. This paper presents a technique for representing and displaying high dynamic-range texture maps (HDRTMs) using current graphics hardware. Dynamic range in real-world environments often far exceeds the range representable in 8-bit per-channel texture maps. The increased realism afforded by a highdynamic range representation provides improved fidelity and expressiveness for interactive visualization of image-based models. Our technique allows for realtime rendering of scenes with arbitrary dynamic range, limited only by available texture memory. In our technique, high-dynamic range textures are decomposed into sets of 8-bit textures. These 8-bit textures are dynamically reassembled by the graphics hardware's programmable multitexturing system or using multipass techniques and framebuffer image processing. These operations allow the exposure level of the texture to be adjusted continuously and arbitrarily at the time of rendering, correctly accounting for the gamma curve and dynamic range restrictions of the display device. Further, for any given exposure only two 8-bit textures must be resident in texture memory simultaneously. We present implementation details of this technique on various 3D graphics hardware architectures. We demonstrate several applications, including high-dynamic range panoramic viewing with simulated auto-exposure, real-time radiance environment mapping, and simulated Fresnel reflection.

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2017

Computational Color Science

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Time-dependent visual adaptation for fast realistic image display

Cited by 242 publications

References 19 publications

Integrating Visual Saliency Information into Objective Quality Assessment of Tone-Mapped Images

Integrating Visual Saliency Information into Objective Quality Assessment of Tone-Mapped Images

Real-Time High-Dynamic Range Texture Mapping

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