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首页> 外文期刊>IEEE transactions on multimedia >Blind Image Quality Assessment Using Statistical Structural and Luminance Features
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Blind Image Quality Assessment Using Statistical Structural and Luminance Features

机译：使用统计结构和亮度特征进行盲图像质量评估

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摘要

Blind image quality assessment (BIQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce a novel BIQA metric by structural and luminance information, based on the characteristics of human visual perception for distorted image. We extract the perceptual structural features of distorted image by the local binary pattern distribution. Besides, the distribution of normalized luminance magnitudes is extracted to represent the luminance changes in distorted image. After extracting the features for structures and luminance, support vector regression is adopted to model the complex nonlinear relationship from feature space to quality measure. The proposed BIQA model is called no-reference quality assessment using statistical structural and luminance features (NRSL). Extensive experiments conducted on four synthetically distorted image databases and three naturally distorted image databases have demonstrated that the proposed NRSL metric compares favorably with the relevant state-of-the-art BIQA models in terms of high correlation with human subjective ratings. The MATLAB source code and validation results of NRSL are publicly online at http://www.ntu.edu.sg/home/wslin/Publications.htm.
机译：盲图像质量评估（BIQA）旨在开发定量措施，以自动，准确地估计感知图像质量，而无需有关参考图像的任何先验信息。在本文中，我们根据人对扭曲图像的视觉感知特性，通过结构和亮度信息介绍了一种新颖的BIQA度量。我们通过局部二值模式分布提取失真图像的感知结构特征。此外，提取归一化亮度幅度的分布以表示失真图像中的亮度变化。在提取出结构和亮度的特征之后，采用支持向量回归对从特征空间到质量度量的复杂非线性关系进行建模。所提出的BIQA模型称为使用统计结构和亮度特征（NRSL）的无参考质量评估。在四个合成失真的图像数据库和三个自然失真的图像数据库上进行的大量实验表明，在与人类主观评分的高度相关性方面，建议的NRSL度量与相关的最新BIQA模型相比具有优势。 NRSL的MATLAB源代码和验证结果在http://www.ntu.edu.sg/home/wslin/Publications.htm上公开在线。

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