首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >APPROXIMATE IMAGE QUALITY MEASURE IN LOW-DIMENSIONAL DOMAIN BASED ON RANDOM PROJECTION
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APPROXIMATE IMAGE QUALITY MEASURE IN LOW-DIMENSIONAL DOMAIN BASED ON RANDOM PROJECTION

机译:基于随机投影的低维域近似图像质量度量

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摘要

Image Quality Measure (IQM) is used to automatically measure the degree of image artifacts such as blocking, ringing and blurring effects. It is calculated traditionally in the image spatial domain. In this paper, we present a new method of transforming an image into a low-dimensional domain based on random projection, so we can efficiently obtain the compatible IQM. From the transformed domain, we can calculate the Peak Signal-to-Noise Ratio (PSNR) and apply fuzzy logic to generate a Low-Dimensional Quality Index (LDQI). Experimental results show that the LDQI can approximate the IQM in the image spatial domain. We observe that the LDQI is suited for measuring the compression blur due to its relatively low distortion. The relative error is about 0.15 as the compression blur increases.
机译:图像质量测量(IQM)用于自动测量图像伪影的程度,例如阻塞,振铃和模糊效果。传统上,它是在图像空间域中计算的。在本文中,我们提出了一种基于随机投影将图像转换为低维域的新方法,因此我们可以有效地获得兼容的IQM。从转换后的域中,我们可以计算峰值信噪比(PSNR)并应用模糊逻辑来生成低维质量指数(LDQI)。实验结果表明,LDQI可以在图像空间域内近似IQM。我们发现LDQI失真度较低,因此适合测量压缩模糊。随着压缩模糊的增加,相对误差约为0.15。

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