Blind Quality Assessment Based on Pseudo-Reference Image

By Xiongkuo Min, Ke Gu, Guangtao Zhai, Jing Liu, Xiaokang Yang, Chang Wen Chen. IEEE transactions on multimedia, 2018

410 citations recorded by the source corpus.

Traditional full-reference image quality assessment (IQA) metrics generally predict the quality of the distorted image by measuring its deviation from a perfect quality image called reference image. When the reference image is not fully available, the reduced-reference and no-reference IQA metrics may still be able to derive some characteristics of the perfect quality images, and then measure the distorted image's deviation from these characteristics. In this paper, contrary to the conventional IQA metrics, we utilize a new “reference” called pseudo-reference image (PRI) and a PRI-based blind IQA (BIQA) framework. Different from a traditional reference image, which is assumed to have a perfect quality, PRI is generated from the distorted image and is assumed to suffer from the severest distortion for a given application. Based on the PRI-based BIQA framework, we develop distortion-specific metrics to estimate blockiness, sharpness, and noisiness. The PRI-based metrics calculate the similarity between the distorted image's and the PRI's structures. An image suffering from severer distortion has a higher degree of similarity with the corresponding PRI. Through a two-stage quality regression after a distortion identification framework, we then integrate the PRI-based distortion-specific metrics into a general-purpose BIQA method named blind PRI-based (BPRI) metric. The BPRI metric is opinion-unaware (OU) and almost training-free except for the distortion identification process. Comparative studies on five large IQA databases show that the proposed BPRI model is comparable to the state-of-the-art opinion-aware- and OU-BIQA models. Furthermore, BPRI not only performs well on natural scene images, but also is applicable to screen content images. The MATLAB source code of BPRI and other PRI-based distortion-specific metrics will be publicly available.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/499c4bab0170649239bd35c4829a3a5a4ea34c06

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. Ke Gu
2. Weisi Lin
3. Guangtao Zhai
4. Xiaokang Yang
5. Xiongkuo Min
6. Qiong Yan
7. Wenxiu Sun
8. Chaofeng Chen

In Media Technology

1. Ke Gu
2. Weisi Lin
3. Guangtao Zhai
4. Xiaokang Yang
5. Xiongkuo Min
6. Haoning Wu
7. Qiong Yan
8. Jingwen Hou

Citations in the corpus

• TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment (2023, methodology): “Min et al. [34], [35] proposed new features based on pseudo-reference image and significantly outperforms NSS based methods.”
• Blind Image Quality Estimation via Distortion Aggravation (2018, methodology): “, NFERM [18], LPSI [33], and BPRI [20].”