Domain Fingerprints for No-Reference Image Quality Assessment

By Weihao Xia, Yujiu Yang, Jing-Hao Xue, Jing Xiao. 2019

14 citations recorded by the source corpus.

Human fingerprints are detailed and nearly unique markers of human identity. Such a unique and stable fingerprint is also left on each acquired image. It can reveal how an image was degraded during the image acquisition procedure and thus is closely related to the quality of an image. In this work, we propose a new no-reference image quality assessment (NR-IQA) approach called domain-aware IQA (DA-IQA), which for the first time introduces the concept of domain fingerprint to the NR-IQA field. The domain fingerprint of an image is learned from image collections of different degradations and then used as the unique characteristics to identify the degradation sources and assess the quality of the image. To this end, we design a new domain-aware architecture, which enables simultaneous determination of both the distortion sources and the quality of an image. With the distortion in an image better characterized, the image quality can be more accurately assessed, as verified by extensive experiments, which show that the proposed DA-IQA performs better than almost all the compared state-of-the-art NR-IQA methods.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yujiu Yang
2. Ming-Hsuan Yang
3. Weihao Xia
4. Yulun Zhang
5. Jing-Hao Xue
6. Luc Van Gool
7. Bolei Zhou
8. Lei Zhang

In Media Technology

1. Yulun Zhang
2. Ming-Hsuan Yang
3. Yujiu Yang
4. Weihao Xia
5. Bolei Zhou
6. S. Gong
7. Jing-Hao Xue
8. Ming Cao

Citations in the corpus

• GAN Inversion: A Survey (2021, methodology): “The latent features of well-disentangled features of different classes can be visualized [75], [76] by using the t-SNE [77] method.”
• MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment (2022, methodology): “With the success of deep learning and Convolution Neural Network (CNN) in many computer vision tasks, CNN-based methods have significantly outperformed previous approaches in terms of real-world distortions by directly extracting discriminative features from LQ images [1,3,14,20,26,37,39,45,59,61].”