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Using the Natural Scenes' Edges for Assessing Image Quality Blindly and Efficiently

机译：使用自然场景的边缘进行盲目有效的图像质量评估

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摘要

Two real blindo-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separate NR IQA algorithms. The presented algorithms do not need training on databases of human judgments or even prior knowledge about expected distortions, so they are real NR IQA algorithms. In contrast to the most general no-reference IQA, the model used for this study is generic and has been created in such a way that it is not specified to any particular distortion type. When testing the proposed algorithms on LIVE database, experiments show that they correlate well with subjective opinion scores. They also show that the introduced methods significantly outperform the popular full-reference peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) methods. Besides they outperform the recently developed NR natural image quality evaluator (NIQE) model.
机译：在空间域中，开发了两种实际的盲/无参考（NR）图像质量评估（IQA）算法。为了测量图像质量，引入的方法使用了空前的概念来基于自然场景的边缘收集一组新颖的特征。人眼对图像边缘和轮廓所携带的信息的敏感性增强，支持了这一主张。已经研究了所提出的技术在量化图像质量方面的有效性。使用Weibull分布统计信息和两个清晰度函数来设计两个单独的NR IQA算法，从而形成收集的特征。提出的算法不需要在人工判断数据库上训练，甚至不需要有关预期失真的先验知识，因此它们是真正的NR IQA算法。与最一般的无参考IQA相比，本研究使用的模型是通用模型，其创建方式未指定任何特定的失真类型。当在LIVE数据库上测试提出的算法时，实验表明它们与主观意见得分具有很好的相关性。他们还表明，引入的方法明显优于流行的全参考峰信噪比（PSNR）和结构相似性（SSIM）方法。此外，它们的性能优于最近开发的NR自然图像质量评估器（NIQE）模型。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第2期|389504.1-389504.9|共9页
  • 作者

    Abdalmajeed Saifeldeen; Jiao Shuhong;

  • 作者单位

    Harbin Engn Univ, Dept Informat & Commun, Harbin 150001, Peoples R China.;

    Harbin Engn Univ, Dept Informat & Commun, Harbin 150001, Peoples R China.;

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