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. 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. 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. 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.