## 1 Introduction  
Recently, camera manufacturers and researchers have developed various post-processing methods that enhance image quality. With the development of computer performance, image enhancement techniques have been actively developed for the last 20 years. These image enhancement techniques include denoising that reduces image noise [1,2], sharpening that creates a less blurry image [3], filtering that changes image property [4,5], and histogram equalization that enhances the contrast of the image [6]. Furthermore, various cutting-edge techniques such as super-resolution used to increase image resolutions [7, 8, 9], artifact and distortion removal for images degraded by compression [10, 11], and methods to adjust the colorfulness, contrast, and sharpness of images have been developed to improve the visual preference of human visual systems (HVS) [12, 13, 14, 15, 16, 17, 18]. These methods need parameter adjustments in order to obtain high-quality images. Since the perception of image quality is influenced by HVS properties, these parameter values can be determined through subjective viewer preferences. However, subjective evaluations are time-consuming and expensive because many people are required for the evaluation of test images. Accordingly, objective assessment methods that automatically evaluate no-reference images have been extensively researched, which resulted in developments such as just-noticeable difference (JND)-based techniques [19; 20]. Furthermore, natural scene statistic (NSS)-based methods have been developed to improve the correlation with subjective evaluations considering HVS properties [21; 22; 23; 24; 25]. The natural image-quality index (NIQE) [24] method uses the statistical difference between the fitted Gaussian functions of the high-quality and low-quality images in spatial domains. NIQE does not require a reference database of human-rated images. Additionally, this method considers a human visual system using a statistics-based difference between a pre-trained high-quality image database. However, the aim of these previous methods has been completed to evaluate images distorted by compression or noise and they are not suitable for evaluating colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images. Furthermore, they depend on the luminance of images without considering color components. Several efforts have been made to evaluate contrast-adjusted [26; 27] and sharpness-adjusted [27; 28; 29] images. Recently, a color quality enhancement evaluation (CQE) method was developed [30], which considered color components. This method considers a color component and is specialized in evaluating the adjusted image. In this method, the overall quality scores of an image are obtained from a linear combination of several feature values that represent the levels of quality for each adjustment. However, this method does not take HVS properties into account and, therefore, its results correlate poorly with the results of subjective evaluations [31]. In this study, to improve the correlation of such no-reference methods with subjective evaluations, we propose in this study a fully automatic method that evaluates colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images by taking into account HVS preferences using a high dynamic range (HDR) [32; 33; 34]--derived features. These HDR-derived features are extracted from HDR images that have higher visual preferences for HVS than that for LDR images. Furthermore, since HDR images have a wider dynamic range, they have more contrast, colorfulness, and sharpness components than LDR images do [18]. To evaluate the performance of our proposed method, we compared its correlation results (with subjective evaluation scores for the colorfulness-adjusted, contrast-adjusted, and sharpness-adjusted images) with those of two conventional methods, a natural image quality evaluator (NIQE) [24], and CQE [30].