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Visualizing and Communicating Errors in Rendered Images

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Bibliographic Details
Title: Visualizing and Communicating Errors in Rendered Images
Authors: Andersson, Pontus, Akenine-Möller, Tomas, Nilsson, Jim
Contributors: Lund University, Faculty of Science, Centre for Mathematical Sciences, Mathematics (Faculty of Engineering), Computer Vision and Machine Learning, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Matematik LTH, Datorseende och maskininlärning, Originator, Lund University, Faculty of Science, Centre for Mathematical Sciences, Mathematics (Faculty of Engineering), Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Matematik LTH, Originator
Source: Ray Tracing Gems II Evaluating and Improving Rendered Visual Experiences. :301-320
Subject Terms: Natural Sciences, Computer and Information Sciences, Computer graphics and computer vision, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Datorgrafik och datorseende, Computer Sciences, Datavetenskap (Datalogi)
Description: In rendering research and development, it is important to have a formalized way of visualizing and communicating how and where errors occur when rendering with a given algorithm. Such evaluation is often done by comparing the test image to a ground-truth reference image. We present a tool for doing this for both low and high dynamic range images. Our tool is based on a perception-motivated error metric, which computes an error map image. For high dynamic range images, it also computes a visualization of the exposures that may generate large errors.
Access URL: https://link.springer.com/book/10.1007/978-1-4842-7185-8
Database: SwePub
Description
Abstract:In rendering research and development, it is important to have a formalized way of visualizing and communicating how and where errors occur when rendering with a given algorithm. Such evaluation is often done by comparing the test image to a ground-truth reference image. We present a tool for doing this for both low and high dynamic range images. Our tool is based on a perception-motivated error metric, which computes an error map image. For high dynamic range images, it also computes a visualization of the exposures that may generate large errors.