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Blind visual quality assessment (BVQA) on 360° video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360° video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e., a progressive quality assessment paradigm. However, the existing BVQA approaches for 360° video neglect this paradigm. In this paper, we take into account the progressive paradigm of human perception towards spherical video quality, and thus propose a novel BVQA approach (namely ProVQA) for 360° video via progressively learning from pixels, frames and video. Corresponding to the progressive learning of pixels, frames and video, three sub-nets are designed in our ProVQA approach, i.e., the spherical perception aware quality prediction (SPAQ), motion perception aware quality prediction (MPAQ) and multi-frame temporal non-local (MFTN) sub-nets. The SPAQ sub-net first models the spatial quality degradation based on spherical perception mechanism of human. Then, by exploiting motion cues across adjacent frames, the MPAQ sub-net properly incorporates motion contextual information for quality assessment on 360° video. Finally, the MFTN sub-net aggregates multi-frame quality degradation to yield the final quality score, via exploring long-term quality correlation from multiple frames. The experiments validate that our approach significantly advances the state-of-the-art BVQA performance on 360° video over two datasets, the code of which has been public in https://github.com/yanglixiaoshen/ProVQA. [ABSTRACT FROM AUTHOR]

Due to the rapid increase in video traffic and relatively limited delivery infrastructure, end users often experience dynamically varying quality over time when viewing streaming videos. The user quality-of-experience (QoE) must be continuously monitored to deliver an optimized service. However, modern approaches for continuous-time video QoE estimation require densely annotating the continuous-time QoE labels, which is labor-intensive and time-consuming. To cope with such limitations, we propose a novel weakly-supervised domain adaptation approach for continuous-time QoE evaluation, by making use of a small amount of continuously labeled data in the source domain and abundant weakly-labeled data (only containing the retrospective QoE labels) in the target domain. Specifically, given a pair of videos from source and target domains, effective spatiotemporal segment-level feature representation is first learned by a combination of 2D and 3D convolutional networks. Then, a multi-task prediction framework is developed to simultaneously achieve continuous-time and retrospective QoE predictions, where a quality attentive adaptation approach is investigated to effectively alleviate the domain discrepancy without hampering the prediction performance. This approach is enabled by explicitly attending to the video-level discrimination and segment-level transferability in terms of the domain discrepancy. Experiments on benchmark databases demonstrate that the proposed method significantly improves the prediction performance under the cross-domain setting. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a leading cause of permanent blindness among the working-age people. Automatic DR grading can help ophthalmologists make timely treatment for patients. However, the existing grading methods are usually trained with high resolution (HR) fundus images, such that the grading performance decreases a lot given low resolution (LR) images, which are common in clinic. In this paper, we mainly focus on DR grading with LR fundus images. According to our analysis on the DR task, we find that: 1) image super-resolution (ISR) can boost the performance of both DR grading and lesion segmentation; 2) the lesion segmentation regions of fundus images are highly consistent with pathological regions for DR grading. Based on our findings, we propose a convolutional neural network (CNN)-based method for joint learning of multi-level tasks for DR grading, called DeepMT-DR, which can simultaneously handle the low-level task of ISR, the mid-level task of lesion segmentation and the high-level task of disease severity classification on LR fundus images. Moreover, a novel task-aware loss is developed to encourage ISR to focus on the pathological regions for its subsequent tasks: lesion segmentation and DR grading. Extensive experimental results show that our DeepMT-DR method significantly outperforms other state-of-the-art methods for DR grading over three datasets. In addition, our method achieves comparable performance in two auxiliary tasks of ISR and lesion segmentation. [ABSTRACT FROM AUTHOR]

Recently, convolutional sparse coding (CSC) has shown great success in many image processing tasks, such as image super-resolution and image separation. However, it performs poorly in image denoising task. In this letter, we provide a new insight for CSC denoising by revisiting the CSC from a multi-scale perspective. We propose a multi-scale CSC model for image denoising. By unrolling the multi-scale solution into a learnable network, we obtain an interpretable lightweight multi-scale network, namely MCSCNet. Experimental results show that the proposed MCSCNet significantly advances the denoising performance, with an average PSNR improvement of 0.32 dB over the state-of-the-art (SOTA) CSC based method. In addition, our MCSCNet is on par with many SOTA deep learning based methods, with less network parameters and lower FLOPs. The ablation study also validates the effectiveness of the multi-scale CSC mechanism. [ABSTRACT FROM AUTHOR]

For 360° video, the existing visual quality assessment (VQA) approaches are designed based on either the whole frames or the cropped patches, ignoring the fact that subjects can only access viewports. When watching 360° video, subjects select viewports through head movement (HM) and then fixate on attractive regions within the viewports through eye movement (EM). Therefore, this paper proposes a two-staged multi-task approach for viewport-based VQA on 360° video. Specifically, we first establish a large-scale VQA dataset of 360° video, called VQA-ODV, which collects the subjective quality scores and the HM and EM data on 600 video sequences. By mining our dataset, we find that the subjective quality of 360° video is related to camera motion, viewport positions and saliency within viewports. Accordingly, we propose a viewport-based convolutional neural network (V-CNN) approach for VQA on 360° video, which has a novel multi-task architecture composed of a viewport proposal network (VP-net) and viewport quality network (VQ-net). The VP-net handles the auxiliary tasks of camera motion detection and viewport proposal, while the VQ-net accomplishes the auxiliary task of viewport saliency prediction and the main task of VQA. The experiments validate that our V-CNN approach significantly advances state-of-the-art VQA performance on 360° video and it is also effective in the three auxiliary tasks. [ABSTRACT FROM AUTHOR]

Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction. Specifically, we establish a large-scale CT database containing 1,805 3D CT scans with fine-grained lesion annotations, and reveal 4 findings about lesion difference between COVID-19 and community acquired pneumonia (CAP). Inspired by our findings, DeepSC-COVID is designed with 3 subnets: a cross-task feature subnet for feature extraction, a 3D lesion subnet for lesion segmentation, and a classification subnet for disease diagnosis. Besides, the task-aware loss is proposed for learning the task interaction across the 3D lesion and classification subnets. Different from all existing models for COVID-19 diagnosis, our model is interpretable with fine-grained 3D lesion distribution. Finally, extensive experimental results show that the joint learning framework in our model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy. [ABSTRACT FROM AUTHOR]

When watching omnidirectional images (ODIs), subjects can access different viewports by moving their heads. Therefore, it is necessary to predict subjects’ head fixations on ODIs. Inspired by generative adversarial imitation learning (GAIL), this paper proposes a novel approach to predict saliency of head fixations on ODIs, named SalGAIL. First, we establish a dataset for attention on ODIs (AOI). In contrast to traditional datasets, our AOI dataset is large-scale, which contains the head fixations of 30 subjects viewing 600 ODIs. Next, we mine our AOI dataset and discover three findings: (1) the consistency of head fixations are consistent among subjects, and it grows alongside the increased subject number; (2) the head fixations exist with a front center bias (FCB); and (3) the magnitude of head movement is similar across the subjects. According to these findings, our SalGAIL approach applies deep reinforcement learning (DRL) to predict the head fixations of one subject, in which GAIL learns the reward of DRL, rather than the traditional human-designed reward. Then, multi-stream DRL is developed to yield the head fixations of different subjects, and the saliency map of an ODI is generated via convoluting predicted head fixations. Finally, experiments validate the effectiveness of our approach in predicting saliency maps of ODIs, significantly better than 11 state-of-the-art approaches. Our AOI dataset and code of SalGAIL are available online at https://github.com/yanglixiaoshen/SalGAIL. [ABSTRACT FROM AUTHOR]

The existing image compression methods usually choose or optimize low-level representation manually. Actually, these methods struggle for the texture restoration at low bit rates. Recently, deep neural network (DNN)-based image compression methods have achieved impressive results. To achieve better perceptual quality, generative models are widely used, especially generative adversarial networks (GAN). However, training GAN is intractable, especially for high-resolution images, with the challenges of unconvincing reconstructions and unstable training. To overcome these problems, we propose a novel DNN-based image compression framework in this paper. The key point is decomposing an image into multi-scale sub-images using the proposed Laplacian pyramid based multi-scale networks. For each pyramid scale, we train a specific DNN to exploit the compressive representation. Meanwhile, each scale is optimized with different aspects, including pixel, semantics, distribution and entropy, for a good “rate-distortion-perception” trade-off. By independently optimizing each pyramid scale, we make each stage manageable and make each sub-image plausible. Experimental results demonstrate that our method achieves state-of-the-art performance, with advantages over existing methods in providing improved visual quality. Additionally, a better performance in the down-stream visual analysis tasks which are conducted on the reconstructed images, validates the excellent semantics-preserving ability of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, people are getting used to taking photos to record their daily life, however, the photos are actually not consistent with the real natural scenes. The two main differences are that the photos tend to have low dynamic range (LDR) and low resolution (LR), due to the inherent imaging limitations of cameras. The multi-exposure image fusion (MEF) and image super-resolution (SR) are two widely-used techniques to address these two issues. However, they are usually treated as independent researches. In this paper, we propose a deep Coupled Feedback Network (CF-Net) to achieve MEF and SR simultaneously. Given a pair of extremely over-exposed and under-exposed LDR images with low-resolution, our CF-Net is able to generate an image with both high dynamic range (HDR) and high-resolution. Specifically, the CF-Net is composed of two coupled recursive sub-networks, with LR over-exposed and under-exposed images as inputs, respectively. Each sub-network consists of one feature extraction block (FEB), one super-resolution block (SRB) and several coupled feedback blocks (CFB). The FEB and SRB are to extract high-level features from the input LDR image, which are required to be helpful for resolution enhancement. The CFB is arranged after SRB, and its role is to absorb the learned features from the SRBs of the two sub-networks, so that it can produce a high-resolution HDR image. We have a series of CFBs in order to progressively refine the fused high-resolution HDR image. Extensive experimental results show that our CF-Net drastically outperforms other state-of-the-art methods in terms of both SR accuracy and fusion performance. The software code is available here https://github.com/ytZhang99/CF-Net. [ABSTRACT FROM AUTHOR]

Saliency prediction in traditional images and videos has drawn extensive research interests in recent years. Few works have been proposed for saliency prediction over 360° videos. They focus on directly predicting fixations over the whole panorama. When viewing 360° videos, a person can only observe the content in her viewport, which means that only a fraction of the 360° scene can be seen at any given time. In this paper, we study human attention over viewport of 360° videos and propose a novel visual saliency model, dubbed viewport saliency, to predict fixations over 360° videos. Two contributions are introduced. First, we find that where people look is affected by the content and location of the viewport in 360° video. We study this over 200+ 360° videos viewed by 30+ subjects over two recent benchmark databases. Second, we propose a Multi-Task Deep Neural Network (MT-DNN) method for Viewport Saliency (VS) prediction in 360° video, which considers the input content and location of the viewport. Extensive experiments and analyses show that our method outperforms other state-of-the-art methods in this task. In particular, over the two recent 360° video databases, our MT-DNN raises the average CC score by 0.149 and 0.205, compared to SalGAN and DeepVS methods, respectively. [ABSTRACT FROM AUTHOR]