中文摘要：

在计算美学研究中,需要用绘画作品的高分辨率图像来进行分析,但实际获得的绘画作品图像多为低分辨率的,为此研究绘画图像质量对视觉艺术分析效果的影响.选取梵高、莫奈等4位画家绘画作品的高低分辨率图像作为研究对象,采用稀疏编码训练出绘画作品的基函数;对基函数在Gabor域和频率域提取Gabor能量、峰值方向、峰值空间频率等7个特征;按不同的绘画组别对这些特征进行归一化互信息计算,比较它们在分析绘画风格时的性能差异;用性能最好的Gabor能量分析高低分辨率绘画图像对绘画风格研究的差异;最后采用从高分辨率绘画图像中提取的特征对绘画风格进行了分类.实验结果表明,低分辨率绘画图像提取的特征在一定程度上仍具有表征绘画风格的能力,可用于绘画风格的分析,其中Gabor能量的性能最好.

英文摘要：

In the study of computational aesthetics, high-resolution paintings always are used to analyze painting style, but actually the paintings we obtain mostly are low-resolution. In order to analyze the effect of image quality for visual art analysis, the contrast experiments are carried out between high and low resolution paintings in this paper. The sparse coding is used to train basis functions, different features are extracted in frequency domain and Gabor domain from the basis function. Then the normalized mutual information（NMI） is figured out to analyze the difference between high and low resolution painting features in analyzing the painting style. At last, the features with better performance are used to classify the painting style. The results show that, to a certain extent, features extracted from low-resolution paintings still have the ability to characterize the painting style, among which the Gabor energy has the best effect in painting style analysis. That is to say features extracted from low-resolution paintings can be used in painting style analysis.