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Proceedings Paper

Optimization of display viewing distance for human observers in the noise-limited case
Author(s): Kimberly Kolb; Bradley Preece; Jeffrey Olson; Joseph Reynolds
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Paper Abstract

In the pursuit of fully-automated display optimization, the US Army RDECOM CERDEC Night Vision and Electronic Sensors Directorate (NVESD) is evaluating a variety of approaches, including the effects of viewing distance and magnification on target acquisition performance. Two such approaches are the Targeting Task Performance (TTP) metric, which NVESD has developed to model target acquisition performance in a wide range of conditions, and a newer Detectivity metric, based on matched-filter analysis by the observer. While NVESD has previously evaluated the TTP metric for predicting the peak-performance viewing distance as a function of blur, no such study has been done for noise-limited conditions. In this paper, the authors present a study of human task performance for images with noise versus viewing distance using both metrics. Experimental results are compared to predictions using the Night Vision Integrated Performance Model (NV-IPM). The potential impact of the results on the development of automated display optimization are discussed, as well as assumptions that must be made about the targets being displayed.

Paper Details

Date Published: 5 May 2017
PDF: 15 pages
Proc. SPIE 10197, Degraded Environments: Sensing, Processing, and Display 2017, 101970T (5 May 2017); doi: 10.1117/12.2263322
Show Author Affiliations
Kimberly Kolb, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Bradley Preece, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Jeffrey Olson, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Joseph Reynolds, U.S. Army Night Vision & Electronic Sensors Directorate (United States)


Published in SPIE Proceedings Vol. 10197:
Degraded Environments: Sensing, Processing, and Display 2017
John (Jack) N. Sanders-Reed; Jarvis (Trey) J. Arthur, Editor(s)

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