Modelfest: year one results and plans for future years.

被引:29
作者
Carney, T [1 ]
Tyler, CW [1 ]
Watson, AB [1 ]
Makous, W [1 ]
Beutter, B [1 ]
Chen, CC [1 ]
Norcia, AM [1 ]
Klein, SA [1 ]
机构
[1] Neurometr Inst, Berkeley, CA 94704 USA
来源
HUMAN VISION AND ELECTRONIC IMAGING V | 2000年 / 3959卷
关键词
human vision modeling; threshold database; Modelfest; psychophysics; HVS; image compression;
D O I
10.1117/12.387150
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
A robust model of the human visual system (HVS) would have a major practical impact on the difficult technological problems of transmitting and storing digital images. Although most HVS models exhibit similarities, they may have significant differences in predicting performance. Different HVS models are rarely compared using the same set of psychophysical measurements, so their relative efficacy is unclear. The Modelfest organization was formed to solve this problem and accelerate the development of robust new models of human vision. Members of Modelfest have gathered psychophysical threshold data on the year one stimuli described at last year's SPIE meeting(1). Modelfest is an exciting new approach to modeling involving the sharing of resources, learning from each other's modeling successes and providing a method to cross-validate proposed HVS models. The purpose of this presentation is to invite the Electronic Imaging community to participate in this effort and inform them of the developing database, which is available to all researchers interested in modeling human vision. In future years, the database will be extended to other domains such as visual masking, and temporal processing. This Modelfest progress report summarizes the stimulus definitions and data collection methods used, but focuses on the results of the phase one data collection effort. Each of the authors has provided at least one dataset from their respective laboratories. These data and data collected subsequent to the submission of this paper are posted on the WWW for further analysis and future modeling efforts.
引用
收藏
页码:140 / 151
页数:12
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