Quality Prediction on Deep Generative Images
- Title
- Quality Prediction on Deep Generative Images
- Author
- 고현석
- Issue Date
- 2020-04
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Citation
- IEEE TRANSACTIONS ON IMAGE PROCESSING, v. 29, Page. 5964-5979
- Abstract
- In recent years, deep neural networks have been utilized in a wide variety of applications including image generation. In particular, generative adversarial networks (GANs) are able to produce highly realistic pictures as part of tasks such as image compression. As with standard compression, it is desirable to be able to automatically assess the perceptual quality of generative images to monitor and control the encode process. However, existing image quality algorithms are ineffective on GAN generated content, especially on textured regions and at high compressions. Here we propose a new "naturalness"-based image quality predictor for generative images. Our new GAN picture quality predictor is built using a multi-stage parallel boosting system based on structural similarity features and measurements of statistical similarity. To enable model development and testing, we also constructed a subjective GAN image quality database containing (distorted) GAN images and collected human opinions of them. Our experimental results indicate that our proposed GAN IQA model delivers superior quality predictions on the generative image datasets, as well as on traditional image quality datasets.
- URI
- https://ieeexplore.ieee.org/abstract/document/9069418https://repository.hanyang.ac.kr/handle/20.500.11754/164509
- ISSN
- 1057-7149
- DOI
- 10.1109/TIP.2020.2987180
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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