Visual realism is defined as the extent to which an image appears to people as a photo rather than computer generated. Assessing visual realism is important in applications like computer graphics rendering and photo retouching. However, current realism evaluation approaches use either labor-intensive human judgments or automated algorithms largely dependent on comparing renderings to reference images. We develop a reference-free computational framework for visual realism prediction to overcome these constraints. First, we construct a benchmark dataset of 2,520 images with comprehensive human annotated attributes. From statistical modeling on this data, we identify image attributes most relevant for visual realism. We propose both empirically-based (guided by our statistical modeling of human data) and deep convolutional neural network models to predict visual realism of images. Our framework has the following advantages: (1) it creates an interpretable and concise empirical model that characterizes human perception of visual realism; (2) it links computational features to latent factors of human image perception.

中文翻译：

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图像视觉真实感：从人类感知到机器计算


视觉真实感被定义为图像在人们看来是照片而不是计算机生成的程度。评估视觉真实感在计算机图形渲染和照片修饰等应用中非常重要。然而，当前的真实感评估方法要么使用劳动密集型的人类判断，要么使用很大程度上依赖于将渲染与参考图像进行比较的自动化算法。我们开发了一个用于视觉真实感预测的无参考计算框架，以克服这些限制。首先，我们构建了一个包含 2,520 张具有全面人类注释属性的图像的基准数据集。通过对这些数据的统计建模，我们确定了与视觉真实感最相关的图像属性。我们提出基于经验的（以我们的人类数据统计模型为指导）和深度卷积神经网络模型来预测图像的视觉真实感。我们的框架具有以下优点：（1）它创建了一个可解释且简洁的经验模型，表征了人类对视觉现实主义的感知； (2)它将计算特征与人类图像感知的潜在因素联系起来。