A Perceptual Image Quality Assessment Metric Using Singular Value Decomposition

作者：Wang, Shuigen^*; Cui, Dongshun; Wang, Baoxian; Zhao, Baojun; Yang, Jinglin

来源：Circuits, Systems, and Signal Processing, 2015, 34(1): 209-229.

DOI：10.1007/s00034-014-9840-3

• Image quality assessment
• Human visual system
• Singular value decomposition
• Extreme learning machine

Image distortion can be categorized into two types: content-dependent degradation and content-independent one. Most of the existing perceptual full-reference image quality assessment (IQA) metrics cannot deal with both these two different impacts well. Singular value decomposition (SVD) as a useful mathematical tool that has been used in various image processing applications (e.g., feature extraction). In this paper, SVD is employed to decompose the images into the structural (content-dependent) and the content-independent components. For each portion, a specific assessment model is designed to tailor for its corresponding distortion properties. All the proposed models are then fused to obtain a final quality score by extreme learning machine (ELM), a machine learning technique. Extensive experimental results on six publicly available databases demonstrate that the proposed metric achieves better performance in comparison with the relevant state-of-the-art quality metrics.

• 出版日期2015-1
• 单位北京理工大学