SSVD: Structural SVD-based image quality assessment
作者：
Highlights：
• Both the structural degradation and the luminance changes are captured.
• Luminance distortions are regulated based on the structural changes.
• Distorted image reflection on original singular vector provides structural changes.
• Best performance on a large dataset (TID2013) including diverse distortion types.
• Acceptable performance using different databases including multiply distorted one.
摘要
•Both the structural degradation and the luminance changes are captured.•Luminance distortions are regulated based on the structural changes.•Distorted image reflection on original singular vector provides structural changes.•Best performance on a large dataset (TID2013) including diverse distortion types.•Acceptable performance using different databases including multiply distorted one.
论文关键词：Human visual system,Image quality assessment,Singular value decomposition(SVD),SSVD
论文评审过程：Received 31 May 2018, Revised 17 January 2019, Accepted 17 January 2019, Available online 23 January 2019, Version of Record 8 February 2019.
论文官网地址：https://doi.org/10.1016/j.image.2019.01.007
