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Image quality assessment (IQA) is an important task of image processing and has diverse applications, such as image super-resolution reconstruction, image transmission and monitoring systems. This paper proposes a perceptual hashing algorithm with complementary color wavelet transform (CCWT) and compressed sensing (CS) for reduced-reference (RR) IQA. The CCWT is exploited to decompose input color image into different sub-bands. Since the calculation of CCWT uses all color channels without discarding any information, the distortions introduced by digital operations on color channels are preserved in the CCWT sub-bands. The block-based CS is used to extract features from the CCWT sub-bands. As the Euclidean distance between the block-based CS features is slightly influenced by content-preserving operations, perceptual features constructed by Euclidean distances are robust, discriminative and compact. Hash sequence is finally determined by quantifying the perceptual features. Effectiveness of the proposed hashing is verified by various experiments on four open image databases. Experimental results demonstrate that the proposed hashing is superior to some state-of-the-art algorithms in terms of classification and RR IQA application. [ABSTRACT FROM AUTHOR]

In this paper, we conduct an extensive study on the use of pre-trained convolutional neural networks (CNNs) for omnidirectional image quality assessment (IQA). To cope with the lack of available IQA databases, transfer learning from seven pre-trained CNN models is investigated over retraining on standard 2D databases. In addition, we explore the influence of various image representations and training strategies on the model’s performance. A comparison of the use of projected versus radial content, and multichannel CNN versus patch-wise training is also covered. The experimental results on two publicly available databases are used to draw conclusions about which strategy best fits the visual quality prediction and at which computational cost. The analysis shows that retraining CNN models on 2D IQA databases improves the prediction accuracy. The latter and the required computational time are found to be significantly affected by the training strategy. Cross-database evaluations demonstrate that the nature and variety of the content impact the generalization ability of the models. Finally, we show that conclusions coming from other image processing communities may not hold for IQA. The provided discussion shall provide insights and recommendations when using pre-trained CNNs for omnidirectional IQA. [ABSTRACT FROM AUTHOR]

Image quality assessment (IQA) plays a central role in many image processing algorithms and systems. Although many popular IQA models achieves high performance on existing released databases, they are still not well accepted in practical applications due to the not-always satisfactory accuracy on real-world data and situations. In this paper, we revisit the IQA research, and point out an ignored but interesting problem in IQA: the coarse-grained (i.e., when quality variation is sufficiently big, as the setting of most IQA databases up to date) statistical results evaluated on existing databases mask the fine-grained differentiation. Accordingly, we present a survey on image quality assessment from a new perspective: fine-grained image quality assessment (FG-IQA). Recent FG-IQA research on five major kinds of images is introduced, and some popular IQA methods are analyzed from FG-IQA perspective. The potential problems for current IQA research based on existing coarse-grained databases are analyzed and the necessity of more FG-IQA research is justified. Finally, we discuss some challenges and possible directions for future works in FG-IQA. [ABSTRACT FROM AUTHOR]

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]