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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]

Retrieving 3D shapes based on 2D images is a challenging research topic, due to the significant gap between different domains. Recently, various approaches have been proposed to handle this problem. However, the majority of methods target the cross-domain retrieval task as a pure domain adaptation problem, which focuses on the alignment but ignores the visual relevance between the 2D images and their corresponding 3D shapes. To fundamentally decrease the divergence between different domains, we propose a novel cross-domain learning network (CLN) for 2D image-based 3D shape retrieval task. First, we estimate the pose information from the 2D image to guide the view rendering of 3D shapes, which increases the visual correlations of the cross-domain data to eliminate the divergence between them. Second, we introduce a novel joint learning network, considering both the domain-specific characteristics and the cross-domain interactions for data alignment, which further compensates for the gap between different domains by controlling the distance of intra- and inter-classes. After the metric learning process, discriminative descriptors of images and shapes are generated for the cross-domain retrieval task. To prove the effectiveness and robustness of the proposed method, we conduct extensive experiments on the MI3DOR, SHREC’13, and SHREC’14 datasets. The experimental results demonstrate the superiority of our proposed method, and significant improvements have been achieved compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this work, we simulate the early visual information processing mechanisms in biological visual system (BVS) to solve the visible-infrared image fusion (VIIF) task. Concretely, both infrared and visible images are first processed with a dynamic receptive field (DRF), which is imitated by a Difference of Gaussian (DoG) function whose parameters are adjusted according to local image statistics (e.g., local edge responses). The DRF processing produces two components for each source image (e.g., the visible image or the infrared image) that respectively represent the results of On-center based DRF and Off-center based DRF. Then, the results of On-center based DRF for visible image are fused with the results of Off-center based DRF for infrared image and the results of On-center based DRF for infrared image are fused with the results of Off-center based DRF for visible image, according to the mechanisms of cortex-based center-surround fusion. Algorithmically, this step fuses the visible and infrared images processed by DRF with On-center and Off-center according to the different levels of local homogeneity. Moreover, a feedward signal acting as the sub-cortical flow is also introduced to adjust the results during the fusion. The final output image is simply obtained by the summation of two components after the cortex and sub-cortex based fusion. Qualitative and quantitative tests on four datasets demonstrate that our approach can fuse the infrared and visible images effectively with the good background details and discernible salient areas. We emphasize the importance of corticothalamic feedback, cortex and sub-cortex-based fusion, and the interactions between On-center pathway and Off-center pathway that are ubiquitous in BVS for producing the high-quality visual signals. [ABSTRACT FROM AUTHOR]