Abstract
Example-based texture synthesis plays a significant role in many fields, including computer graphics, computer vision, multimedia, and image and video editing and processing. However, it is not easy for all textures to synthesize high-quality outputs of any size from a small input example. Hence, the assessment of the synthesizability of the example textures deserves more attention. Inspired by the broad studies in image quality assessment, we propose a texture synthesizability assessment approach based on a deep Siamese-type network. To our best knowledge, this is the first attempt to evaluate the synthesizability of sample textures through end-to-end training. We first train a Siamese-type network to compare the example texture and the synthesized texture in terms of their similarity and then transfer the experience knowledge obtained in the Siamese-type network to a traditional CNN by fine-tuning, so that to give an absolute score to a single example texture, representing its synthesizability. Not relying on laborious human selection and annotation, these synthesized textures can be generated automatically by example-based synthesis algorithms. We demonstrate that our approach is completely data-driven without hand-crafted features and/or prior knowledge in the field of expertise. Experiments show that our approach improves the accuracy of texture synthesizability assessment qualitatively and quantitatively and outperforms the manual feature-based method.
Highlights
Textures can be obtained by various methods such as manual drawing, photography, modeling, and simulation, texture synthesis, especially, example-based texture synthesis, remains one of the most powerful approaches due to its advantages on universality, efficiency, and quality
One kind focuses on the quality assessment for the synthesized textures, that is, the output texture after synthesis [1, 2]. Another one attempts to evaluate whether an example texture/input texture is suitable for example-based synthesis methods before synthesis [3, 4], called texture synthesizability prediction in [3]
Our main contributions are as follows: (1) we propose a deep Siamese-type network-based approach for texture synthesizability assessment, which allows for end-to-end optimization for feature extraction, fusion, and regression; (2) we offer a recommendation mechanism for suggesting the best-matched synthesis algorithm; (3) such a methodology could be considered for other texture analysis, such as the synthesizability of nonstationary textures
Summary
Textures can be obtained by various methods such as manual drawing, photography, modeling, and simulation, texture synthesis, especially, example-based texture synthesis, remains one of the most powerful approaches due to its advantages on universality, efficiency, and quality. As far as we know, some work has been done to explore this issue though not much. One kind focuses on the quality assessment for the synthesized textures, that is, the output texture after synthesis [1, 2]. Another one attempts to evaluate whether an example texture/input texture is suitable for example-based synthesis methods before synthesis [3, 4], called texture synthesizability prediction in [3]. Our work falls into the latter category
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