.png){kind=icon}
.png){kind=icon}
Return
No-reference image quality assessment using statistical wavelet-packet features
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2016.06.010.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper an efficient no-reference (NR) image quality assessment (IQA) method is presented based on the statistical features of subband coefficients in the wavelet-packet domain. The proposed method is based on the hypothesis that potential distortions may alter the statistical characteristics of natural un-distorted images. Hence, by characterizing the statistical properties of a given distorted image one can identify the distortion and its strength in the distorted image. For this purpose, several statistical features of a given gray-scale image as well as the magnitude of its gradient and its Laplacian are extracted in the wavelet-packet domain. The extracted features are then mapped to quality scores within a two-stage quality assessment framework. The proposed method is general-purpose, and is able to assess the image quality across various distortion categories. Experimental results indicate that the proposed method achieves high accuracy in image quality prediction as compared to several prominent and state-of-the-art full-reference and no-reference IQA methods. Crown Copyright (C) 2016 Published by Elsevier B.V. All rights reserved.
Keywords:
Image quality assessment
Wavelet packets
Support vector machines
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.8K
Citations:
1.6W