.png){kind=icon}
.png){kind=icon}
Return
LANBIQUE: LANguage-based Blind Image QUality Evaluation
.png){kind=icon}
OA
AI
DOI：10.1145/3538649.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Image quality assessment is often performed with deep networks that are fine-tuned to regress a human provided quality score of a given image. Usually, this approach may lack generalization capabilities and, while being highly precise on similar image distribution, it may yield lower correlation on unseen distortions. In particular, they show poor performances, whereas images corrupted by noise, blur, or compression have been restored by generative models. As a matter of fact, evaluation of these generative models is often performed providing anecdotal results to the reader. In the case of image enhancement and restoration, reference images are usually available. Nevertheless, using signal based metrics often leads to counterintuitive results: Highly natural crisp images may obtain worse scores than blurry ones. However, blind reference image assessment may rank images reconstructed with GANs higher than the original undistorted images. To avoid time-consuming human-based image assessment, semantic computer vision tasks may be exploited instead. In this article, we advocate the use of language generation tasks to evaluate the quality of restored images. We refer to our assessment approach as LANguage-based Blind Image QUality Evaluation (LANBIQUE). We show experimentally that image captioning, used as a downstream task, may serve as a method to score image quality, independently of the distortion process that affects the data. Captioning scores are better aligned with human rankings with respect to classic signal based or No-reference image quality metrics. We show insights on how the corruption, by artefacts, of local image structure may steer image captions in the wrong direction.
Keywords:
Image quality enhancement
image captioning
image quality evaluation
GAN
generative models evaluation
Journal
IF:
6
Papers:
2.0K
Citations:
5.4K
Organization
No organization information available