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In virtual reality (VR), correct and precise estimations of user’s visual fixations and head movements can enhance the quality of experience by allocating more computation resources for analysing and rendering on the areas of interest. However, there is insufficient research about understanding the visual exploration of users when modeling VR visual attention. To bridge the gap between the saliency prediction for traditional 2D content and omnidirectional content, we construct the visual attention dataset and propose the visual saliency prediction framework for panoramic videos. Around the instantaneous viewing behavior, we propose a traditional method to adapt 2D saliency models and design a CNN-based model to better predict visual saliency. In the proposed traditional model, mechanism of visual attention and viewing behaviors are considered in the computation of edge weights on graphs which are interpreted as Markov chains. The fraction of the visual attention that is diverted to each high-clarity vision (HCV) area is estimated through equilibrium distribution of this chain. We also propose the Graph-Based CNN model. The RGB channel and optical flow form the spatial-temporal units of HCVs, from which node feature vectors are extracted. Graph convolution is used to learn the mutual information between node feature vectors of HCVs and retain geometric information. Then feature vectors are aligned according to geometry structure of equirectangular format, and the feature decoder maps the aligned feature maps to the data distribution. We also construct the dynamic omnidirectional monocular (DOM) saliency dataset with 64 diverse videos evaluated by 28 people. The subjective results show that the instantaneous viewing behavior is important in the VR experience. Extensive experiments are conducted on the dataset and the results demonstrate the effectiveness of the proposed framework. The dataset will be released to facilitate the future studies related to visual saliency prediction for 360-degree contents. [ABSTRACT FROM AUTHOR]

In the era of multimedia and Internet, the quick response (QR) code helps people obtain information from offline to online quickly. However, the QR code is often limited in many scenarios because of its random and dull appearance. Therefore, this article proposes a novel approach to embed hyperlinks into common images, making the hyperlinks invisible for human eyes but detectable for mobile devices equipped with a camera. Our approach is an end-to-end neural network with an encoder to hide messages and a decoder to extract messages. To maintain the hidden message resilient to cameras, we build a distortion network between the encoder and the decoder to augment the encoded images. The distortion network uses differentiable 3-D rendering operations, which can simulate the distortion introduced by camera imaging in both printing and display scenarios. To maintain the visual attraction of the image with hyperlinks, a loss function conforming to the human visual system (HVS) is used to supervise the training of the encoder. Experimental results show that the proposed approach outperforms the previous work on both robustness and quality. Based on the proposed approach, many applications become possible, for example, “image hyperlinks” for advertisement on TV, website, or poster, and “invisible watermark” for copyright protection on digital resources or product packagings. [ABSTRACT FROM AUTHOR]

With the development of multimedia technology, Augmented Reality (AR) has become a promising next-generation mobile platform. The primary value of AR is to promote the fusion of digital contents and real-world environments, however, studies on how this fusion will influence the Quality of Experience (QoE) of these two components are lacking. To achieve better QoE of AR, whose two layers are influenced by each other, it is important to evaluate its perceptual quality first. In this paper, we consider AR technology as the superimposition of virtual scenes and real scenes, and introduce visual confusion as its basic theory. A more general problem is first proposed, which is evaluating the perceptual quality of superimposed images, i.e., confusing image quality assessment. A ConFusing Image Quality Assessment (CFIQA) database is established, which includes 600 reference images and 300 distorted images generated by mixing reference images in pairs. Then a subjective quality perception experiment is conducted towards attaining a better understanding of how humans perceive the confusing images. Based on the CFIQA database, several benchmark models and a specifically designed CFIQA model are proposed for solving this problem. Experimental results show that the proposed CFIQA model achieves state-of-the-art performance compared to other benchmark models. Moreover, an extended ARIQA study is further conducted based on the CFIQA study. We establish an ARIQA database to better simulate the real AR application scenarios, which contains 20 AR reference images, 20 background (BG) reference images, and 560 distorted images generated from AR and BG references, as well as the correspondingly collected subjective quality ratings. Three types of full-reference (FR) IQA benchmark variants are designed to study whether we should consider the visual confusion when designing corresponding IQA algorithms. An ARIQA metric is finally proposed for better evaluating the perceptual quality of AR images. Experimental results demonstrate the good generalization ability of the CFIQA model and the state-of-the-art performance of the ARIQA model. The databases, benchmark models, and proposed metrics are available at: https://github.com/DuanHuiyu/ARIQA. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of data imbalance in learning deep models for visual object tracking. Although it is well known that data distribution plays a crucial role in learning and inference models, considerably less attention has been paid to data imbalance in visual tracking. For the deep regression trackers that directly learn a dense mapping from input images of target objects to soft response maps, we identify their performance is limited by the extremely imbalanced pixel-to-pixel differences when computing regression loss. This prevents existing end-to-end learnable deep regression trackers from performing as well as discriminative correlation filters (DCFs) trackers. For the deep classification trackers that draw positive and negative samples to learn discriminative classifiers, there exists heavy class imbalance due to a limited number of positive samples when compared to the number of negative samples. To balance training data, we propose a novel shrinkage loss to penalize the importance of easy training data mostly coming from the background, which facilitates both deep regression and classification trackers to better distinguish target objects from the background. We extensively validate the proposed shrinkage loss function on six benchmark datasets, including the OTB-2013, OTB-2015, UAV-123, VOT-2016, VOT-2018 and LaSOT. Equipped with our shrinkage loss, the proposed one-stage deep regression tracker achieves favorable results against state-of-the-art methods, especially in comparison with DCFs trackers. Meanwhile, our shrinkage loss generalizes well to deep classification trackers. When replacing the original binary cross entropy loss with our shrinkage loss, three representative baseline trackers achieve large performance gains, even setting new state-of-the-art results. [ABSTRACT FROM AUTHOR]

The emerging vision-and-language navigation (VLN) problem aims at learning to navigate an agent to the target location in unseen photo-realistic environments according to the given language instruction. The main challenges of VLN arise mainly from two aspects: first, the agent needs to attend to the meaningful paragraphs of the language instruction corresponding to the dynamically-varying visual environments; second, during the training process, the agent usually imitate the expert demonstrations, i.e., the shortest-path to the target location specified by associated language instructions. Due to the discrepancy of action selection between training and inference, the agent solely on the basis of imitation learning does not perform well. Existing VLN approaches address this issue by sampling the next action from its predicted probability distribution during the training process. This allows the agent to explore diverse routes from the environments, yielding higher success rates. Nevertheless, without being presented with the golden shortest navigation paths during the training process, the agent may arrive at the target location through an unexpected longer route. To overcome these challenges, we design a cross-modal grounding module, which is composed of two complementary attention mechanisms, to equip the agent with a better ability to track the correspondence between the textual and visual modalities. We then propose to recursively alternate the learning schemes of imitation and exploration to narrow the discrepancy between training and inference. We further exploit the advantages of both these two learning schemes via adversarial learning. Extensive experimental results on the Room-to-Room (R2R) benchmark dataset demonstrate that the proposed learning scheme is generalized and complementary to prior arts. Our method performs well against state-of-the-art approaches in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive region proposal scheme with feature channel regularization to facilitate robust object tracking. We consider tracking as a linear regression problem and an ensemble of correlation filters is trained on-line to distinguish the foreground target from the background. Further, we integrate adaptively learned region proposals into an enhanced two-stream tracking framework based on correlation filters. For the tracking stream, we learn two-stage cascade correlation filters on deep convolutional features to ensure competitive tracking performance. For the detection stream, we employ adaptive region proposals, which are effective in recovering target objects from tracking failures caused by heavy occlusion or out-of-view movement. In contrast to traditional tracking-by-detection methods using random samples or sliding windows, we perform target re-detection over adaptively learned region proposals. Since region proposals naturally take the objectness information into account, we show that the proposed adaptive region proposals can handle the challenging scale estimation problem as well. In addition, we observe the channel redundancy and noisy of feature representation, especially for the convolutional features. Thus, we apply a channel regularization to the correlation filter learning. Extensive experimental validations on OTB, VOT and UAV-123 datasets demonstrate that the proposed method performs favorably against state-of-the-art tracking algorithms. [ABSTRACT FROM AUTHOR]

Hashing has been widely used in large-scale image retrieval based on approximate nearest neighbor search. Most learning-to-hashing methods adopt a two-stage algorithm to generate binary codes. First, original images are mapped into continuous visual features. Then, binary codes are generated by quantization step or separate projection. Nevertheless, these methods are sensitive to quantization operation, i.e., thresholding. To explicitly address this issue, this study proposes a novel feature quantization scheme with a loopy recurrent neural network, called loopy residual hashing, for the purpose of high accuracy in image retrieval. Instead of one-off thresholding-based feature binarization, the proposed approach performs an iterative threshold-then-approximate operation, which calculates the quantization residual after each thresholding step and then imitates another round of binarization to further approximate the coding residual. The resulting sequences of binary codes possess higher representation accuracy and extensive experiments on image retrieval demonstrate its superior discriminative capability over the prior art. In the meantime, theoretical approximation error analysis is given. [ABSTRACT FROM AUTHOR]

Hashing is a widely adopted method based on an approximate nearest neighbor search and is used in large-scale image retrieval tasks. Conventional learning-based hashing algorithms employ end-to-end representation learning, which is a one-off technique. Because of the tradeoff between efficiency and performance, conventional learning-based hashing methods must sacrifice code length to improve performance, which increases their computational complexity. To improve the efficiency of binary codes, motivated by the “nonsalient-to-salient” attention scheme of humans, we propose a recursive hashing mechanism that maps progressively expanded salient regions to a series of binary codes. These salient regions are generated by a conventional saliency model based on bottom-up saliency-driven attention and a semantic-guided saliency model based on top-down task-driven attention. After obtaining a series of salient regions, we perform long-range temporal modeling of salient regions using a graph-based recurrent deep network to obtain more refined representative features. The later output nodes inherit aggregated information from all previous nodes and extract discriminative features from more salient regions. Therefore, this network possesses more significant information and satisfactory scalability. The proposed recursive hashing neural network, optimized by a triplet ranking loss, is end-to-end trainable. Extensive experimental results from several image retrieval benchmarks show the scalability of our method and demonstrate its strong performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Visual tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we propose to exploit the rich hierarchical features of deep convolutional neural networks to improve the accuracy and robustness of visual tracking. Deep neural networks trained on object recognition datasets consist of multiple convolutional layers. These layers encode target appearance with different levels of abstraction. For example, the outputs of the last convolutional layers encode the semantic information of targets and such representations are invariant to significant appearance variations. However, their spatial resolutions are too coarse to precisely localize the target. In contrast, features from earlier convolutional layers provide more precise localization but are less invariant to appearance changes. We interpret the hierarchical features of convolutional layers as a nonlinear counterpart of an image pyramid representation and explicitly exploit these multiple levels of abstraction to represent target objects. Specifically, we learn adaptive correlation filters on the outputs from each convolutional layer to encode the target appearance. We infer the maximum response of each layer to locate targets in a coarse-to-fine manner. To further handle the issues with scale estimation and re-detecting target objects from tracking failures caused by heavy occlusion or out-of-the-view movement, we conservatively learn another correlation filter, that maintains a long-term memory of target appearance, as a discriminative classifier. We apply the classifier to two types of object proposals: (1) proposals with a small step size and tightly around the estimated location for scale estimation; and (2) proposals with large step size and across the whole image for target re-detection. Extensive experimental results on large-scale benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art tracking methods. [ABSTRACT FROM AUTHOR]

The visual quality of perceptions is highly correlated with the mechanisms of the human brain and visual system. Recently, the free-energy principle, which has been widely researched in brain theory and neuroscience, is introduced to quantize the perception, action, and learning in human brain. In the field of image quality assessment (IQA), on one hand, the free-energy principle can resort to the internal generative model to simulate the visual stimulus of the human beings. On the other hand, abundant psychological and neurobiological studies reveal that different frequency and orientation components of one visual stimulus arouse different neurons in the striate cortex, and the striate cortex processes visual information in the cerebral cortex. Motivated by these two aspects, a novel reduce-reference IQA metric called the multi-channel free-energy based reduced-reference quality metric is proposed in this paper. First, a two-level discrete Haar wavelet transform is used to decompose the input reference and distorted images. Next, to simulate the generative model in the human brain, the sparse representation is leveraged to extract the free-energy-based features in subband images. Finally, the overall quality metric is obtained through the support vector regressor. Extensive experimental comparisons on four benchmark image quality databases (LIVE, CSIQ, TID2008, and TID2013) demonstrate that the proposed method is highly competitive with the representative reduced-reference and classical full-reference models. [ABSTRACT FROM AUTHOR]

The general purpose of seeing a picture is to attain information as much as possible. With it, we in this paper devise a new no-reference/blind metric for image quality assessment (IQA) of contrast distortion. For local details, we first roughly remove predicted regions in an image since unpredicted remains are of much information. We then compute entropy of particular unpredicted areas of maximum information via visual saliency. From global perspective, we compare the image histogram with the uniformly distributed histogram of maximum information via the symmetric Kullback–Leibler divergence. The proposed blind IQA method generates an overall quality estimation of a contrast-distorted image by properly combining local and global considerations. Thorough experiments on five databases/subsets demonstrate the superiority of our training-free blind technique over state-of-the-art full- and no-reference IQA methods. Furthermore, the proposed model is also applied to amend the performance of general-purpose blind quality metrics to a sizable margin. [ABSTRACT FROM PUBLISHER]

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method. [ABSTRACT FROM PUBLISHER]

With the widespread adoption of multidevice communication, such as telecommuting, screen content images (SCIs) have become more closely and frequently related to our daily lives. For SCIs, the tasks of accurate visual quality assessment, high-efficiency compression, and suitable contrast enhancement have thus currently attracted increased attention. In particular, the quality evaluation of SCIs is important due to its good ability for instruction and optimization in various processing systems. Hence, in this paper, we develop a new objective metric for research on perceptual quality assessment of distorted SCIs. Compared to the classical MSE, our method, which mainly relies on simple convolution operators, first highlights the degradations in structures caused by different types of distortions and then detects salient areas where the distortions usually attract more attention. A comparison of our algorithm with the most popular and state-of-the-art quality measures is performed on two new SCI databases (SIQAD and SCD). Extensive results are provided to verify the superiority and efficiency of the proposed IQA technique. [ABSTRACT FROM PUBLISHER]

Image quality assessment (IQA) has been an active research area during last decades. Many existing objective IQA models share a similar two-step structure with measuring local distortion before pooling. Compared with the rapid development for local distortion measurement, seldom effort has been made dedicated to effective pooling schemes. In this paper, we design a new pooling model via the analysis of distortion distribution affected by image content and distortion. That is, distributions of distortion position, distortion intensity, frequency changes, and histogram changes are comprehensively considered to infer an overall quality score. Experimental results conducted on four large-scale image quality databases (LIVE, TID2008, CSIQ, and CCID2014) concluded with three valuable findings. First, the proposed technique leads to consistent improvement in the IQA performance for studied local distortion measures. Second, relative to the traditional pooling, the performance gain of our algorithm is beyond 15% on average. Third, the best overall performance made by the proposed strategy outperforms state-of-the-art competitors. [ABSTRACT FROM PUBLISHER]

High dynamic range (HDR) imaging techniques have been working constantly, actively, and validly in the fault detection and disease diagnosis in the astronomical and medical fields, and currently they have also gained much more attention from digital image processing and computer vision communities. While HDR imaging devices are starting to have friendly prices, HDR display devices are still out of reach of typical consumers. Due to the limited availability of HDR display devices, in most cases tone mapping operators (TMOs) are used to convert HDR images to standard low dynamic range (LDR) images for visualization. But existing TMOs cannot work effectively for all kinds of HDR images, with their performance largely depending on brightness, contrast, and structure properties of a scene. To accurately measure and compare the performance of distinct TMOs, in this paper develop an effective and efficient no-reference objective quality metric which can automatically assess LDR images created by different TMOs without access to the original HDR images. Our model is shown to be statistically superior to recent full- and no-reference quality measures on the existing tone-mapped image database and a new relevant database built in this work. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of image contrast enhancement. Most existing relevant technologies often suffer from the drawback of excessive enhancement, thereby introducing noise/artifacts and changing visual attention regions. One frequently used solution is manual parameter tuning, which is, however, impractical for most applications since it is labor intensive and time consuming. In this research, we find that saliency preservation can help produce appropriately enhanced images, i.e., improved contrast without annoying artifacts. We therefore design an automatic contrast enhancement technology with a complete histogram modification framework and an automatic parameter selector. This framework combines the original image, its histogram equalized product, and its visually pleasing version created by a sigmoid transfer function that was developed in our recent work. Then, a visual quality judging criterion is developed based on the concept of saliency preservation, which assists the automatic parameters selection, and finally properly enhanced image can be generated accordingly. We test the proposed scheme on Kodak and Video Quality Experts Group databases, and compare with the classical histogram equalization technique and its variations as well as state-of-the-art contrast enhancement approaches. The experimental results demonstrate that our technique has superior saliency preservation ability and outstanding enhancement effect. [ABSTRACT FROM PUBLISHER]

Viewing distance and image resolution have substantial influences on image quality assessment (IQA), but this issue has been highly overlooked in the literature so far. In this paper, we examine the problem of optimal resolution adjustment as a preprocessing step for IQA. In general, the sampling of visual information by human eyes’ optics is approximately a low-pass process. For a given visual scene, the amount of the extractable information greatly depends on the viewing distance and image resolution. We first introduce a novel dedicated viewing distance-changed image database (VDID2014) with two groups of typical viewing distances and image resolutions to promote the IQA study for this issue. Then we design a new effective optimal scale selection (OSS) model in dual-transform domains, in which a cascade of adaptive high-frequency clipping in the discrete wavelet transform domain and adaptive resolution scaling in the spatial domain is used. Validation of our technique is conducted on five image databases (LIVE, IVC, Toyama, VDID2014, and TID2008). Experimental results show that the performance of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) can be substantially improved by applying these metrics to OSS model preprocessed images, superior to classical multi-scale-PSNR/SSIM and comparable to the state-of-the-art competitors. [ABSTRACT FROM PUBLISHER]

In this paper we propose a new no-reference (NR) image quality assessment (IQA) metric using the recently revealed free-energy-based brain theory and classical human visual system (HVS)-inspired features. The features used can be divided into three groups. The first involves the features inspired by the free energy principle and the structural degradation model. Furthermore, the free energy theory also reveals that the HVS always tries to infer the meaningful part from the visual stimuli. In terms of this finding, we first predict an image that the HVS perceives from a distorted image based on the free energy theory, then the second group of features is composed of some HVS-inspired features (such as structural information and gradient magnitude) computed using the distorted and predicted images. The third group of features quantifies the possible losses of “naturalness” in the distorted image by fitting the generalized Gaussian distribution to mean subtracted contrast normalized coefficients. After feature extraction, our algorithm utilizes the support vector machine based regression module to derive the overall quality score. Experiments on LIVE, TID2008, CSIQ, IVC, and Toyama databases confirm the effectiveness of our introduced NR IQA metric compared to the state-of-the-art. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new psychovisual quality metric of images based on recent developments in brain theory and neuroscience, particularly the free-energy principle. The perception and understanding of an image is modeled as an active inference process, in which the brain tries to explain the scene using an internal generative model. The psychovisual quality is thus closely related to how accurately visual sensory data can be explained by the generative model, and the upper bound of the discrepancy between the image signal and its best internal description is given by the free energy of the cognition process. Therefore, the perceptual quality of an image can be quantified using the free energy. Constructively, we develop a reduced-reference free-energy-based distortion metric (FEDM) and a no-reference free-energy-based quality metric (NFEQM). The FEDM and the NFEQM are nearly invariant to many global systematic deviations in geometry and illumination that hardly affect visual quality, for which existing image quality metrics wrongly predict severe quality degradation. Although with very limited or even without information on the reference image, the FEDM and the NFEQM are highly competitive compared with the full-reference SSIM image quality metric on images in the popular LIVE database. Moreover, FEDM and NFEQM can measure correctly the visual quality of some model-based image processing algorithms, for which the competing metrics often contradict with viewers' opinions. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a new algorithm for image re-ranking in web image search applications. The proposed method focuses on investigating the following two mechanisms: 1) Visual consistency. In most web image search cases, the images that closely related to the search query are visually similar. These visually consistent images which occur most frequently in the first few web pages will be given higher ranks. 2) Visual saliency. From visual aspect, it is obvious that salient images would be easier to catch users' eyes, and it is observed that these visually salient images in the front pages are often relevant to the user's query. By integrating the above two mechanisms, our method can efficiently re-rank the images from search engines and obtain a more satisfactory search result. Experimental results on a real-world web image dataset demonstrate that our approach can effectively improve the performance of image retrieval. [ABSTRACT FROM PUBLISHER]