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

摘要PI：Kavita Bala (0811680)逼真成像的目标是产生忠实代表现实世界外观的图像。 评估图像保真度是真实成像的一个重要方面，在计算机图形和数字摄影中具有广泛的影响。 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；但这些指标通常表征像素级图像差异。当我们查看图像时，我们看不到像素，我们看到的是具有不同形状、大小、材质和运动的物体。 因此，需要表征对象外观属性的图像保真度度量。该项目引入了一种新的图像保真度标准，称为视觉等效性。如果图像传达有关对象外观（形状、材质、照明）的相同信息，则它们在视觉上是等效的，即使它们逐个像素不同。这一新标准从根本上扩展了如何评估图像保真度的范围，提供了基于视觉编码的高级属性的度量。该项目旨在通过以下方式理解和利用视觉等效现象：a）进行一系列心理物理学实验，通过探索复杂场景中物体的几何形状、材料和照明属性之间的相互作用，为视觉等效性奠定基础； b) 开发适用于各种场景的视觉等效预测器； c) 展示视觉等效概念在一系列应用中的实用性，包括高保真环境地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学的重大挑战之一提供了有价值的新工具：复杂场景的高性能、高保真图像合成。 此外，这种新的图像保真度方法应该对图形以外的领域产生重大影响，包括 2D 和 3D 数字图像采集、编码、压缩、传输、存储和显示。

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.