学习预测计算机游戏中的染恶化的感知可见性
Rafał Piórkowski1, Radosław Mantiuk2, Marek Wernikowski3
1Independent scientist and designer, Szczecin, Poland.
Scientific reports
|November 13, 2024
概括
这项研究引入了一个卷积神经网络 (CNN) 来优化计算机游戏染设置. 人工智能识别和分类视觉工件,帮助玩家选择最佳配置以获得更好的性能和视觉质量.
科学领域:
- 计算机图形 计算机图形
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 电脑游戏玩家手动调整染设置 (纹理分辨率,影子地图,反别名) 以平衡性能和视觉保真.
- 这些设置的复杂相互作用使手动优化具有挑战性,特别是在低端硬件上.
- 需要自动化方法来简化选择最佳染配置.
研究的目的:
- 开发和验证一个卷积神经网络 (CNN) 用于游戏染设置的自动优化.
- 为了能够有效地识别和分类由于染质量降低而导致的视觉工件.
- 帮助玩家选择最小化文物可见性和最大化性能的配置.
主要方法:
- 训练一家专有CNN公司来比较高质量的参考图像和质量降低的染图像.
- 美国有线电视新闻网 (CNN) 识别和分类视觉艺术品,并评估它们的感知影响.
- 该网络接受了各种游戏引擎场景的培训,手动注释了质量下降的地区.
主要成果:
- 开发的CNN有效地识别和分类染图像中的视觉工件.
- 在一个原型OpenGL染器中的实现允许对反别名设置进行评估.
- 用户研究证实了CNN在现有指标上识别文物可见性的卓越能力.
结论:
- CNN提供了一种有效的自动化解决方案,用于优化游戏染设置.
- 这种方法简化了实现最佳视觉质量和性能的过程.
- 该方法在文物检测效率方面超过了传统的图像质量指标.
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