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Visual Pattern Degradation based Image Quality Assessment

机译:基于视觉模式退化的图像质量评估

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摘要

In this paper, we introduce a visual pattern degradation based full-reference (FR) image quality assessment (IQA) method. Researches on visual recognition indicate that the human visual system (HVS) is highly adaptive to extract visual structures for scene understanding. Existing structure degradation based IQA methods mainly take local luminance contrast to represent structure, and measure quality as degradation on luminance contrast. In this paper, we suggest that structure includes not only luminance contrast but also orientation information. Therefore, we analyze the orientation characteristic for structure description. Inspired by the orientation selectivity mechanism in the primary visual cortex, we introduce a novel visual pattern to represent the structure of a local region. Then, the quality is measured as the degradations on both luminance contrast and visual pattern. Experimental results on Five benchmark databases demonstrate that the proposed visual pattern can effectively represent visual structure and the proposed IQA method performs better than the existing IQA metrics.
机译:在本文中,我们介绍了一种基于视觉模式退化的全参考(FR)图像质量评估(IQA)方法。关于视觉识别的研究表明,人类视觉系统(HVS)高度适应于提取视觉结构以进行场景理解。现有的基于结构退化的IQA方法主要采用局部亮度对比度来表示结构,并根据亮度对比度的下降来衡量质量。在本文中,我们建议结构不仅包括亮度对比度,还包括方向信息。因此,我们分析取向特征以进行结构描述。受到主要视觉皮层中方向选择性机制的启发,我们引入了一种新颖的视觉模式来表示局部区域的结构。然后,测量质量作为亮度对比度和视觉图案上的劣化。在五个基准数据库上的实验结果表明,所提出的视觉模式可以有效地表示视觉结构,并且所提出的IQA方法的性能要优于现有的IQA指标。

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