Perceptually Motivated Method for Image Inpainting Comparison
Ivan Molodetskikh, Mikhail Erofeev, Dmitry Vatolin

TL;DR
This paper conducts a subjective comparison of nine image inpainting algorithms and proposes new objective quality metrics that better align with human perception of realism.
Contribution
It introduces a standardized evaluation method for image inpainting algorithms based on human perception and proposes metrics that correlate well with subjective assessments.
Findings
Proposed objective metrics align closely with human judgments
Subjective comparison reveals differences in algorithm realism
Standardized evaluation framework for future research
Abstract
The field of automatic image inpainting has progressed rapidly in recent years, but no one has yet proposed a standard method of evaluating algorithms. This absence is due to the problem's challenging nature: image-inpainting algorithms strive for realism in the resulting images, but realism is a subjective concept intrinsic to human perception. Existing objective image-quality metrics provide a poor approximation of what humans consider more or less realistic. To improve the situation and to better organize both prior and future research in this field, we conducted a subjective comparison of nine state-of-the-art inpainting algorithms and propose objective quality metrics that exhibit high correlation with the results of our comparison.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Image Processing Techniques · Generative Adversarial Networks and Image Synthesis · Image and Video Quality Assessment
