Blind Image Quality Assessment Based on Natural Scene Statistics

Authors

Haiyang Li, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China;;2. University of Chinese Academy of Sciences, Beijing, 100049, China;
Weiguo Cao, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China;;2. University of Chinese Academy of Sciences, Beijing, 100049, China;
Shirui Li, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China;;2. University of Chinese Academy of Sciences, Beijing, 100049, China;
Kelu Tao, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China;;2. University of Chinese Academy of Sciences, Beijing, 100049, China;
Li Hua, 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China;;2. University of Chinese Academy of Sciences, Beijing, 100049, China;

Abstract

Abstract: Nowadays blind/referenceless image spatial quality evaluator (BRISQUE) based on natural scene statistics is one of the state-of-the-art no-reference algorithms. But it only analyzes the original image and ignores the difference of the features constructed. Here an improved algorithm BRISQUEs is proposed and implemented by three steps. First, we apply mean subtracted contrast normalized to the gradient images and construct a new feature vector to assess quality. Second, we weight some key features of BRISQUE to improve assessment. After the two assessments obtained, a further average is made to weaken the bias from different assessments. Through the experiments on the LIVE IQA database, our approach has a remarkable performance than previous no-reference algorithms and is statistically superior to the popular multi-scale structural similarity index.

Recommended Citation

Li, Haiyang; Cao, Weiguo; Li, Shirui; Tao, Kelu; and Hua, Li (2020) "Blind Image Quality Assessment Based on Natural Scene Statistics," Journal of System Simulation: Vol. 28: Iss. 12, Article 4.
DOI: 10.16182/j.issn1004731x.joss.201612004
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss12/4

First Page

2903

Revised Date

2015-05-04

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201612004

Last Page

2911

CLC

TP391.9

Recommended Citation

Li Haiyang, Cao Weiguo, Li Shirui, Tao Kelu, Li Hua. Blind Image Quality Assessment Based on Natural Scene Statistics[J]. Journal of System Simulation, 2016, 28(12): 2903-2911.

DOI

10.16182/j.issn1004731x.joss.201612004