High Dynamic Range for High Fidelity Image Synthesis of Real Scenes

高动态范围，用于真实场景的高保真图像合成

• 批准号: EP/D032008/1
• 负责人: Marina Bloj
• 金额: $ 29.71万
• 依托单位: University of Bradford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD032008%2F1
• 关键词: Dynamic; Range; Fidelity; Image; Synthesis

The computer graphics industry, and in particular those involved with films, games, simulation, virtual reality and military applications, continue to demand more realistic computer-generated images, that is computed images that more accurately match the real scene they are intended to represent. This is particularly challenging when considering images of the natural world, which presents our visual system with a wide range of colours and intensities. In most real scenes, for example, looking from inside a house towards a window, the ratio between the darkest areas (e.g. inside the room) and the brightest area (outside the window), the so-called contrast ratio, could be many thousands to one. A typical computer monitor only has a contrast ratio of about 100:1 and is thus incapable of accurately displaying such scenes.A number of appearance-preserving, or tone-mapping, operators (TMOs) have been developed in order to try to achieve a perceptual match between the real-world scene and what is displayed on the computer monitor. However, it has not yet been possible to validate the fidelity of these TMOs thoroughly against the real scenes they are trying to portray. The recent development of novel, high dynamic range (HDR) displays, capable of 75,000:1 contrast ratio now provide the opportunity to compute and display computer-generated images that are perceptually much closer to the real world.This research proposal will use these novel HDR displays to evaluate existing TMOs to see how well they do preserve the appearance of the real scenes, and will use the insights gained to develop new, more accurate TMOs for existing computer monitors and HDR displays. A framework will also be produced that will provide a straightforward, objective way of comparing real and synthetic images. Two applications, which are critically dependent on the realism of computed images, are virtual archaeology and military simulations. When investigating past environments on a computer, failure to produce images that accurately match what the past environment may have looked like, may in fact lead to the archaeologists misinterpreting the past. Similarly, the incorrect display of a military vehicle attempting to camouflage in a certain terrain may lead to detection of the vehicle in the real battlefield scenario. We will use specific examples from archaeology and camouflage to test the results of our research.

计算机图形行业，尤其是涉及电影、游戏、模拟、虚拟现实和军事应用的行业，继续要求更逼真的计算机生成图像，即与它们打算表示的真实场景更精确地匹配的计算图像。考虑到自然世界的图像，这一点尤其具有挑战性，因为它为我们的视觉系统呈现了广泛的颜色和强度。例如，在大多数真实场景中，从房子里朝窗户看，最暗的区域(如房间内)和最亮的区域(如窗户外)之间的比率，即所谓的对比度，可能是数千比1。典型的计算机监视器只有大约100：1的对比度，因此不能准确地显示这样的场景。为了试图实现真实世界场景和计算机监视器上显示的内容之间的知觉匹配，已经开发了许多外观保持或色调映射操作符(TMO)。然而，目前还不能根据他们试图描绘的真实场景来彻底验证这些TMO的逼真度。新型高动态范围(HDR)显示器的最新发展，能够提供75,000：1的对比度，现在提供了计算和显示计算机生成的图像的机会，这些图像在感知上更接近真实世界。这项研究计划将使用这些新型HDR显示器来评估现有的TMO，看看它们在保存真实场景的外观方面做得如何，并将利用所获得的见解来为现有的计算机显示器和HDR显示器开发新的、更精确的TMO。还将制定一个框架，提供一种直接、客观的方式来比较真实图像和合成图像。虚拟考古学和军事模拟是两种严重依赖于计算机图像真实感的应用。当在计算机上调查过去的环境时，如果不能产生与过去环境可能的样子准确匹配的图像，实际上可能会导致考古学家曲解过去。同样，在特定地形中试图伪装的军用车辆的错误显示可能会导致在真实战场场景中检测到该车辆。我们将用考古学和伪装学中的具体例子来检验我们的研究结果。

项目成果

The Gilchrist room revisited

重温吉尔克里斯特房间

• 作者: Alexa Ruppertsberg (Author)

The role of mutual illumination in gradient formation

相互照明在梯度形成中的作用

• DOI: 10.1167/6.6.246
• 发表时间: 2010
• 期刊: Journal of Vision
• 影响因子: 1.8
• 作者: Bloj M

High Dynamic Range Imaging and LDR Expansion for Generating HDR Content

用于生成 HDR 内容的高动态范围成像和 LDR 扩展

• 发表时间: 2009
• 作者: F Banterle

Exploring eye movements for tone mapped images

探索色调映射图像的眼球运动

• DOI: 10.1117/12.810497
• 发表时间: 2009
• 作者: Bloj M

Mixing tone mapping operators on the GPU by differential zone mapping based on psychophysical experiments

• DOI: 10.1016/j.image.2016.09.004
• 发表时间: 2016-10
• 期刊: Signal Process. Image Commun.
• 作者: F. Banterle;A. Artusi;E. Sikudová;P. Ledda;Thomas Bashford-Rogers;A. Chalmers;Marina Bloj