IEEE Trans Cybern. 2016 Jan;46(1):39-50. doi: 10.1109/TCYB.2015.2392129. Epub 2015 Jan 29.
Blur is a key determinant in the perception of image quality. Generally, blur causes spread of edges, which leads to shape changes in images. Discrete orthogonal moments have been widely studied as effective shape descriptors. Intuitively, blur can be represented using discrete moments since noticeable blur affects the magnitudes of moments of an image. With this consideration, this paper presents a blind image blur evaluation algorithm based on discrete Tchebichef moments. The gradient of a blurred image is first computed to account for the shape, which is more effective for blur representation. Then the gradient image is divided into equal-size blocks and the Tchebichef moments are calculated to characterize image shape. The energy of a block is computed as the sum of squared non-DC moment values. Finally, the proposed image blur score is defined as the variance-normalized moment energy, which is computed with the guidance of a visual saliency model to adapt to the characteristic of human visual system. The performance of the proposed method is evaluated on four public image quality databases. The experimental results demonstrate that our method can produce blur scores highly consistent with subjective evaluations. It also outperforms the state-of-the-art image blur metrics and several general-purpose no-reference quality metrics.
模糊是影响图像质量感知的关键因素。一般来说，模糊会导致边缘扩散，从而导致图像形状发生变化。离散正交矩广泛应用于有效的形状描述符。直观地说，可以使用离散矩来表示模糊，因为明显的模糊会影响图像矩的大小。基于这一考虑，本文提出了一种基于离散切比雪夫矩的盲图像模糊评价算法。首先计算模糊图像的梯度以表示形状，这对于模糊表示更有效。然后将梯度图像分成等大小的块，并计算切比雪夫矩来描述图像形状。块的能量定义为非直流矩值平方和。最后，根据视觉显著度模型的指导，定义了所提出的图像模糊评分，以适应人类视觉系统的特点。在四个公共图像质量数据库上评估了所提出方法的性能。实验结果表明，所提出的方法可以产生与主观评价高度一致的模糊评分。它还优于最先进的图像模糊度量标准和几个通用无参考质量度量标准。
