Image quality assessment for inpainted images via learning to rank

Mariko Isogawa, Dan Mikami, Kosuke Takahashi, Hideaki Kimata
2018 Multimedia tools and applications  
This paper proposes an image quality assessment (IQA) method for image inpainting, aiming at selecting the best one from a plurality of results. It is known that inpainting results vary largely with the method used for inpainting and the parameters set. Thus, in a typical use case, users need to manually select the inpainting method and the parameters that yield the best result. This manual selection takes a great deal of time and thus there is a great need for a way to automatically estimate
more » ... e best result. Unlike existing IQA methods for inpainting, our method solves this problem as a learning-based ordering task between inpainted images. This approach makes it possible to introduce auto-generated training sets for more effective learning, which has been difficult for existing methods because judging inpainting quality is quite subjective. Our method focuses on the following three points: (1) the problem can be divided into a set of "pairwise preference order estimation" elemental problems, (2) this pairwise ordering approach enables a training set to be generated automatically, and (3) effective feature design is enabled by investigating actually measured human gazes for order estimation.
doi:10.1007/s11042-018-6186-z fatcat:u3ojrgnjhzem5dvvvtcg5ylkv4