Arbitrary neural style transfer is a vital topic with great research value and wide industrial application, which strives to render the structure of one image using the style of another. Recent researches have devoted great efforts on the task of arbitrary style transfer (AST) for improving the stylization quality. However, there are very few explorations about the quality evaluation of AST images, even it can potentially guide the design of different algorithms. In this paper, we first construct a new AST images quality assessment database (AST-IQAD), which consists 150 content-style image pairs and the corresponding 1200 stylized images produced by eight typical AST algorithms. Then, a subjective study is conducted on our AST-IQAD database, which obtains the subjective rating scores of all stylized images on the three subjective evaluations, i.e., content preservation (CP), style resemblance (SR), and overall vision (OV). To quantitatively measure the quality of AST image, we propose a new sparse representation-based method, which computes the quality according to the sparse feature similarity. Experimental results on our AST-IQAD have demonstrated the superiority of the proposed method. The dataset and source code will be released at https://github.com/Hangwei-Chen/AST-IQAD-SRQE

翻译：任意神经风格迁移是一个具有重要研究价值和广泛应用前景的关键课题，旨在利用一种图像的风格渲染另一种图像的结构。近年来的研究在任意风格迁移任务上投入了大量精力，以提升风格化质量。然而，针对AST图像的质量评估却鲜有探索，尽管它可能潜在指导不同算法的设计。本文中，我们首先构建了一个新的AST图像质量评估数据库（AST-IQAD），包含150对内容-风格图像以及由八种典型AST算法生成的1200张风格化图像。随后，在AST-IQAD数据库上开展主观研究，获取所有风格化图像在三个主观评价维度（即内容保留、风格相似度和整体视觉）上的主观评分。为了定量衡量AST图像质量，我们提出了一种基于稀疏表示的新方法，根据稀疏特征相似度计算质量。在AST-IQAD上的实验结果表明了所提方法的优越性。数据集和源代码将在 https://github.com/Hangwei-Chen/AST-IQAD-SRQE 公开发布。