CPA -G&V: Collaborative Research: Visual Equivalence: a New Foundation for Perceptually-Based Rendering of Complex Scenes

CPA-G

• 批准号: 0811680
• 负责人: Kavita Bala
• 金额: $ 27.5万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811680&HistoricalAwards=false
• 关键词: CPA; G& Collaborative; Research; Visual

AbstractPI: Kavita Bala (0811680)The goal of realistic imaging is to produce images that faithfully represent the appearance of the real world. Assessing image fidelity is an important aspect of realistic imaging that has broad implications in both computer graphics and digital photography. Recently perceptual metrics based on computational models of human vision have been used to characterize image fidelity; but these metrics typically characterize pixel-wise image differences. When we look at images we don?t see pixels, we see objects with distinct shapes, sizes, materials, and motions. Therefore, there is a need for image fidelity metrics that characterize the appearance properties of objects. This project introduces a new standard for image fidelity called visual equivalence. Images are visually equivalent if they convey the same information about object appearance (shape, material, lighting), even if they are different pixel-by-pixel. This new standard fundamentally expands the scope of how to assess image fidelity, providing a metric that is based on higher-level properties of visual coding.This project seeks to understand and leverage the phenomenon of visual equivalence by: a) conducting a series of psychophysical experiments that develop a foundation for visual equivalence by exploring interactions between the geometry, material and illumination properties of objects in complex scenes; b) developing visual equivalence predictors that apply to a wide range of scenes; and c) demonstrating the practical utility of the concept of visual equivalence in a range of applications including high-fidelity environment map compression and scalable rendering of complex scenes. The products of this research provide valuable new tools for addressing one of the grand challenges in computer graphics: the high performance, high fidelity image synthesis of complex scenes. Further, this new approach to image fidelity should have a significant impact beyond graphics in areas including 2d and 3d digital image acquisition, coding, compression, transmission, storage, and display.

Abstractpi：Kavita Bala（0811680）现实成像的目标是产生忠实地代表现实世界外观的图像。 评估图像保真度是现实成像的重要方面，在计算机图形和数字摄影中都具有广泛的影响。 最近，基于人类视觉计算模型的感知指标已被用来表征图像保真度。但是这些指标通常表征像素图像差异。当我们看图像时，我们没有看到像素，我们会看到具有不同形状，尺寸，材料和运动的对象。 因此，需要表征对象的外观特性的图像保真度指标。该项目为图像保真度引入了称为视觉对等的新标准。如果图像传达有关对象外观（形状，材料，照明）的相同信息，即使它们是不同的像素，它们在视觉上是等效的。这个新的标准从根本上扩大了如何评估图像保真度的范围，提供了一个基于视觉编码的高级特性的度量标准。本项目试图通过以下方面来理解和利用视觉对等的现象： b）开发适用于各种场景的视觉对等预测因素； c）在一系列应用程序中展示视觉对等概念的实际实用性，包括高保真环境图压缩和复杂场景的可扩展渲染。这项研究的产品为解决计算机图形方面的巨大挑战提供了宝贵的新工具：高性能，高保真图像的复杂场景综合。 此外，这种新的图像保真度的方法应该在2D和3D数字图像采集，编码，压缩，传输，存储和显示等领域中产生重大影响。