目的 现有的全参考图像质量评价方法使用“完美”的源信号作为参考,但是增强图像的参考图像通常不是“完美”的.因此,现有的全参考质量评价方法不能用于增强图像的评价,提出了一种新的面向彩色增强图像的质量评价算法.方法 利用图像的梯度、颜色和亮度特征,提出了增强图像的梯度增强图、颜色增强图和亮度增强因子的计算方法,计算增强图像相对于参考图像在梯度、颜色和亮度方面的增强程度;并建立了亮度增强因子和梯度增强图、颜色增强图之间的关系模型;另外,原图像的梯度和颜色特征也被提取用于增强图像的质量评价.结果 使用公开数据库进行的实验结果表明,该算法和现有最优算法相比,皮尔逊线性相关系数(PLCC)和斯皮尔曼相关系数(SROCC)分别提高了2.9%和2.5%,而均方根误差(RMSE)则降低了12.3%,获得了比现有算法更优越的性能.结论 本文算法解决了目前已有的评价算法需要参考图像为“完美”图像,而且增强图像质量无法采用相似性程度进行计算的问题,适用于为了获得更好视觉质量的不含噪增强图像的质量评价.

Objective Image enhancement is very important in various visual signal processing applications. In many applications, such as photo retouching, visual inspection, and machine analysis, image enhancement methods are proposed to obtain images with better visual quality. In these cases, original images are usually not “perfect.” However, existing full-reference image quality assessment methods use “perfect” original images as reference signals to assess image quality. Therefore, existing full-reference image quality assessment methods cannot be used to evaluate the visual quality of enhanced images. In this paper, a novel visual quality assessment metric based on features of gradient, colorfulness, and luminance is proposed for enhanced chromatic images.Method The human visual system (HVS) is highly sensitive to gradient information, which can effectively capture both contrast and structural/texture information. Thus, in the proposed metric, a gradient enhancement map is calculated by estimating the enhancement degree of the enhanced image compared with its reference image. In addition, colorfulness is the attribute of the perceived color in certain regions appearing to be more or less chromatic. In the proposed metric, the colorfulness of an enhanced image is estimated by two factors, namely, one is the average distance from different colors to the center gray and the distance between individual colors in the image. Consequently, a colorfulness enhancement map is computed by calculating the enhancement extent of color saturation and its standard deviation. Meanwhile, luminance enhancement factor is integrated together based on the analysis that the luminance change would influence the appearance of gradient and colorfulness information. Moreover, the gradient and colorfulness features of the reference images are extracted to build the objective quality assessment metric for enhanced images. Finally, the model of the relationship between the luminance enhancement factor and the gradient/colorfulness enhancement map is built.Result The proposed metric is compared with the existing image quality assessment metrics, including the peak signal to noise ratio (PSNR), structural similarity (SSIM), visual information fidelity (VIF), most apparent distortion (MAD), appearance-based MAD (MADa), and augmented MADa (dxMADa). Three evaluation criteria are used for performance evaluation, namely, (1) Pearson linear correlation coefficient (PLCC), (2) Spearman's rank-order correlation coefficient (SROCC), and (3) root-mean-squared error (RMSE). Generally, a good image quality assessment (IQA) metric has high PLCC and SROCC values and a low RMSE value. In our proposed metric, compared with the best available metric for enhanced images, PLCC and SROCC improved 2.9% and 2.5%, respectively. Moreover, RMSE reduced 12.3%. In sum, the proposed metric is an obvious improvement than existing metrics when assessing enhanced images.Conclusion In the proposed metric, gradient and colorfulness enhancement maps can accurately calculate the enhancement extent. By integrating the luminance enhancement estimation and features in the reference image, the proposed metric can perform better than other existing metrics. The proposed metric provides an objective score for enhanced images and solves two problems, namely, the reference images of enhanced images are not “perfect” images and similarity measure algorithms cannot be used well for enhanced images. We have conducted experiments to demonstrate the performance of the proposed metric for enhanced images. We have conducted the experiments to demons trate the performance of the proposed metric for enhanced images.