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No-reference image sharpness assessment based on discrepancy measures of structural degradation

机译：基于结构退化的差异衡量标准的无参考图像清晰度评估

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摘要

The discrepancy between an image and its "reblurred" version indicates the extent of blur in the image. This paper presents a novel no-reference image sharpness evaluator leveraging the discrepancy measures of structural degradation in both the spatial and wavelet domains. Specifically, local structural degradation of an input image is characterized by the discrepancy measures of orientation selectivity-based visual patterns and log-Gabor filter responses between the image and its corresponding reblurred version respectively. Considering the influence of viewing distance on image quality, the global sharpness discrepancy is measured through inter-resolution self-similarities. Finally, the computed discrepancies are utilized as sharpness-aware features and then a support vector regressor is employed to map the feature vectors into quality scores. The performance of the proposed method is evaluated on six public image quality databases, including two real blurred image databases. Experimental results demonstrate that our proposed method achieves state-of-the-art performances across all these databases. (C) 2020 Elsevier Inc. All rights reserved.
机译：图像和“重新碰撞”版本之间的差异表示图像中的模糊程度。本文介绍了一种新的无参考图像清晰度评估器，利用空间和小波域的结构降解的差异衡量。具体地，输入图像的局部结构劣化的特征在于分别取向选择性的视觉模式的差异测量和图像与其对应的重新碰撞版本之间的逻辑 - Gabor滤波器响应。考虑到观察距离对图像质量的影响，通过互相分辨率自相似度来衡量全局锐度差异。最后，计算的差异被用作清晰度感知的特征，然后采用支持向量回归传送器将特征向量映射到质量分数。在六个公共图像质量数据库中评估所提出的方法的性能，包括两个真实模糊的图像数据库。实验结果表明，我们的提出方法在所有这些数据库中实现了最先进的表演。 （c）2020 Elsevier Inc.保留所有权利。

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