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High dynamic range (HDR) image, which has a powerful capacity to represent the wide dynamic range of real-world scenes, has been receiving attention from both academic and industrial communities. Although HDR imaging devices have become prevalent, the display devices for HDR images are still limited. To facilitate the visualization of HDR images in standard low dynamic range displays, many different tone mapping operators (TMOs) have been developed. To create a fair comparison of different TMOs, this paper proposes a BLInd QUality Evaluator to blindly predict the quality of Tone-Mapped Images (BLIQUE-TMI) without accessing the corresponding HDR versions. BLIQUE-TMI measures the quality of TMIs by considering the following aspects: 1) visual information; 2) local structure; and 3) naturalness. To be specific, quality-aware features related to the former two aspects are extracted in a local manner based on sparse representation, while quality-aware features related to the third aspect are derived based on global statistics modeling in both intensity and color domains. All the extracted local and global quality-aware features constitute a final feature vector. An emergent machine learning technique, i.e., extreme learning machine, is adopted to learn a quality predictor from feature space to quality space. The superiority of BLIQUE-TMI to several leading blind IQA metrics is well demonstrated on two benchmark databases. [ABSTRACT FROM AUTHOR]

The screen content images (SCIs) quality influences the user experience and the interactive performance of remote computing systems. With numerous approaches proposed to evaluate the quality of natural images, much less work has been dedicated to reduced-reference image quality assessment (RR-IQA) of SCIs. Here, we propose an RR-IQA method from the perspective of SCI visual perception. In particular, the quality of the distorted SCI is evaluated by comparing a set of extracted statistical features that consider both primary visual information and unpredictable uncertainty. A unique property that differentiates the proposed method from previous RR-IQA methods for natural images is the consideration of behaviors when human subjects view the screen content, which motivates us to establish the perceptual model according to the distinct properties of SCIs. Validations based on the screen content IQA database show that the proposed algorithm provides accurate predictions across a wide range of SCI distortions with negligible transmission overhead. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an efficient blind image quality assessment (BIQA) algorithm, which is characterized by a new feature fusion scheme and a k -nearest-neighbor (KNN)-based quality prediction model. Our goal is to predict the perceptual quality of an image without any prior information of its reference image and distortion type. Since the reference image is inaccessible in many applications, the BIQA is quite desirable in this context. In our method, a new feature fusion scheme is first introduced by combining an image’s statistical information from multiple domains (i.e., discrete cosine transform, wavelet, and spatial domains) and multiple color channels (i.e., Y, Cb, and Cr). Then, the predicted image quality is generated from a nonparametric model, which is referred to as the label transfer (LT). Based on the assumption that similar images share similar perceptual qualities, we implement the LT with an image retrieval procedure, where a query image’s KNNs are searched for from some annotated images. The weighted average of the KNN labels (e.g., difference mean opinion score or mean opinion score) is used as the predicted quality score. The proposed method is straightforward and computationally appealing. Experimental results on three publicly available databases (i.e., LIVE II, TID2008, and CSIQ) show that the proposed method is highly consistent with human perception and outperforms many representative BIQA metrics. [ABSTRACT FROM AUTHOR]