Blind Image Quality Estimation via Distortion Aggravation

By Xiongkuo Min, Guangtao Zhai, Ke Gu, Yutao Liu, Xiaokang Yang. IEEE transactions on broadcasting, 2018

475 citations recorded by the source corpus.

Traditional blind image quality assessment (IQA) measures generally predict quality from a sole distorted image directly. In this paper, we first introduce multiple pseudo reference images (MPRIs) by further degrading the distorted image in several ways and to certain degrees, and then compare the similarities between the distorted image and the MPRIs. Via such distortion aggravation, we can have some references to compare with, i.e., the MPRIs, and utilize the full-reference IQA framework to compute the quality. Specifically, we apply four types and five levels of distortion aggravation to deal with the commonly encountered distortions. Local binary pattern features are extracted to describe the similarities between the distorted image and the MPRIs. The similarity scores are then utilized to estimate the overall quality. More similar to a specific pseudo reference image (PRI) indicates closer quality to this PRI. Owning to the availability of the created multiple PRIs, we can reduce the influence of image content, and infer the image quality more accurately and consistently. Validation is conducted on four mainstream natural scene image and screen content image quality assessment databases, and the proposed method is comparable to or outperforms the state-of-the-art blind IQA measures. The MATLAB source code of the proposed measure will be publicly available.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2539a903a89ec0c6894590ff2c0fc17377bf9b31

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. Haoning Wu
2. Qiong Yan
3. Weisi Lin
4. Antonio Liotta
5. Jingwen Hou
6. Wei Song
7. Liang Liao
8. Jiadi Mo

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.”
• Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map (2019, background): “can be easily deployed in practical visual communication systems [7], [8], [9].”