Arbitrary neural style transfer is a vital topic with research value and industrial application prospect, 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) that 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 visual (OV). To quantitatively measure the quality of AST image, we proposed a new sparse representation-based image quality evaluation metric (SRQE), which computes the quality using the sparse feature similarity. Experimental results on the 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:任意神经风格传输是一个至关重要的主题,具有研究价值和工业应用前景,它努力利用另一种风格使一个图像的结构成为一个图像的结构。最近的研究在任意风格传输(AST)的任务上投入了大量努力,以提高结构化质量。然而,很少有关于AST图像质量评估的探索,即使它可以指导不同算法的设计。在本文件中,我们首先建立一个新的AST图像质量评估数据库(AST-IQAD),由150个内容式图像配对和由8种典型的AST算法制作的1200个相配图像组成。然后,在我们的AST-IQAD数据库上进行一项主观研究,该数据库在三种主观评价(即内容保护(CP)、风格相似(SR)和总体视觉(OV)上获得所有图像的主观评级分数。为了量化AST-IQ图像图像质量评估(SRQ)的质量,我们建议采用隐性特征(AST-IAST-Q) 实验性结果将显示AST-SI/释放的数据源。