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Joint DBF and SAO Parallel Filtering Based on Multithread Load Balancing

Journal of Physics: Conference Series 2021年第1期1828卷

作者： Hao Ma Dong Hu Yi Li Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme.

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Scale Adaptive Block Target Tracking Based on Multi-layer Convolution Features and Kernel Correlation Filter

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Ting Zhang Dong Hu Jing Zhang Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy.

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Utility-Oriented Underwater image Quality Assessment Based on Transfer Learning

arXiv

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arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater image Utility Database (UIUD) and a learning-based Underwater image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

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H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System

arXiv

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arXiv 2022年

作者： Cheng, Ming Xu, Yiling Shen, Wang Salman Asif, M. Ma, Chao Sun, Jun Ma, Zhan Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China University of California at Riverside RiversideCA92521 United States MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China School of Electronic Science and Engineering Nanjing University Jiangsu210093 China

High-speed, high-resolution stereoscopic (H2-Stereo) video allows us to perceive dynamic 3D content at fine granularity. The acquisition of H2-Stereo video, however, remains challenging with commodity cameras. Existing spatial super-resolution or temporal frame interpolation methods provide compromised solutions that lack temporal or spatial details, respectively. To alleviate this problem, we propose a dual camera system, in which one camera captures high-spatial-resolution low-frame-rate (HSR-LFR) videos with rich spatial details, and the other captures low-spatial-resolution high-frame-rate (LSR-HFR) videos with smooth temporal details. We then devise a Learned Information Fusion network (LIFnet) that exploits the cross-camera redundancies to enhance both camera views to high spatiotemporal resolution (HSTR) for reconstructing the H2Stereo video effectively. We utilize a disparity network to transfer spatiotemporal information across views even in large disparity scenes, based on which, we propose disparity-guided flow-based warping for LSR-HFR view and complementary warping for HSR-LFR view. A multi-scale fusion method in feature domain is proposed to minimize occlusion-induced warping ghosts and holes in HSR-LFR view. The LIFnet is trained in an end-to-end manner using our collected high-quality Stereo Video dataset from YouTube. Extensive experiments demonstrate that our model outperforms existing state-of-the-art methods for both views on synthetic data and camera-captured real data with large disparity. Ablation studies explore various aspects, including spatiotemporal resolution, camera baseline, camera desynchronization, long/short exposures and applications, of our system to fully understand its capability for potential applications. © 2022, CC BY.

关键词： Optical flows

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3D face registration based on principal axis analysis and labeled regions orientation

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Journal of Systems Engineering and Electronics 2009年第6期000卷

作者： Guo Zhe Zhang Yanning Lin Zenggang Liu Yantong Shaanxi Key Lab. of Speech& Image Information Processing School of Computer ScienceChang'an Campus of Northwestern Polytechnical Univ. Xi'an 710129 P. R. China

A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is *** pre-registration is achieved by transforming the multi-pose models to the standard frontal model\'s reference frame using the principal axis analysis *** significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution *** regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final *** results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.

关键词： local orientation registration principal axis analysis label regions orientation iterative closest point

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Non-sparse Infinite-Kernel Learning for Automated Identification of Alzheimer's Disease Using PET Imaging

Non-sparse Infinite-Kernel Learning for Automated Identifica...

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International Conference on Control, Automation, Robotics & Vision

作者： Yong Xia Shen Lu Wei Wei David Dagan Feng Yanning Zhang Shaanxi Key Lab of Speech & Image Information Processing (SAIIP) School of Computer Science Northwestern Polytechnical University Xi'an 710072 China Biomedical and Multimedia Information Technology (BMIT) Research Group School of Information Technologies The University of Sydney NSW2006 Australia

ISBN: (纸本)9781479952007

Multi-kernel learning machine (MKLM) has recently been introduced to the research of computer-aided dementia identification and pathology progress tracking. Despite its good performance especially in case of using heterogeneous data, such learning schema and its variants usually utilize a L-1 norm constraint that promotes sparse solutions, which may cause loss of potentially important information. In this paper, we propose the non-sparse infinite-kernel learning machine (NS-IKLM) for automated identification of Alzheimer cases from normal controls. In our approach, a modified constraint is utilized to promotes non-sparse solutions and kernel parameters are automatically tuned during the learning process. The proposed algorithm has been evaluated on a set of FDG-PET images selected from the Alzheimer's disease neuroimaing initiative (ADNI) cohort. Our results demonstrate that the proposed non-sparse NS-IKLM is able to achieve satisfying dementia identification at a relatively low computational cost.

关键词： Infinite kernel learning dementia FDG-PET ADNI

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CTCNet: A CNN-Transformer Cooperation Network for Face image Super-Resolution

arXiv

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arXiv 2022年

作者： Gao, Guangwei Xu, Zixiang Li, Juncheng Yang, Jian Zeng, Tieyong Qi, Guo-Jun The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The School of Communication & Information Engineering Shanghai University Shanghai China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China The Center for Mathematical Artificial Intelligence Department of Mathematics The Chinese University of Hong Kong Hong Kong Research Center for Industries of the Future School of Engineering Westlake University OPPO Research Seattle United States

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be available at https://***/IVIPlab/CTCNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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A Siamese Network Tracking Algorithm Based on Hierarchical Attention Mechanism

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Hu Zhang Dong Hu Yingcan Qiu Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Nanjing University of Posts and Telecommunications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing 210003 China

A siamese network tracking algorithm based on hierarchical attention mechanism is proposed in this paper. In order to obtain more robust target tracking results, different layer features are fused effectively. In the process of extracting features, attention mechanism is used to recalibrate the feature map, and AdaBoost algorithm is used to weight the target feature map, which improves the reliability of the response map. Besides, the Inception module is also introduced which not only increases the width of the network and the adaptability of the siamese network to the scale, but also reduces the parameters and improves the speed of network training. Experimental results show that this method can effectively solve the impact of background clutter and improve the accuracy of tracking.

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Evaluation of signal enhancement algorithms for hearing instruments

Evaluation of signal enhancement algorithms for hearing inst...

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European Signal processing Conference (EUSIPCO)

作者： Koen Eneman Heleen Luts Jan Wouters Michael Büchler Norbert Dillier Wouter Dreschler Matthias Froehlich Giso Grimm Volker Hohmann Rolph Houben Arne Leijon Anthony Lombard Dirk Mauler Marc Moonen Henning Puder Michael Schulte Ann Spriet Matthias Vormann ExpORL - Dept. Neurosciences Katholieke Universiteit Leuven Leuven Belgium Denartment of Otorhinolaryngology University Hospital Zürich Switzerland AMC KNO-Audiologie Amsterdam the Netherlands Siemens Audiologische Technik GmbH Erlangen Germamy Medical Physics Section Carl von Ossietzky-Universität Oldenburg Germany Sound and Image Processing Lab Royal Institute of Technology (KTH) Stockholm Sweden Multimedia Communications and Signal Processing Universität Erlangen-Nürnberg Germany Institute of Communication Acoustics (IKA) Ruhr-Universität Bochum Germany ESAT/SISTA Katholieke Universiteit Leuven Belgium Hörzentrum Oldenburg GmbH Oldenburg Germany Hörzentrum Oldenburg GmbH Germany

In the frame of the HearCom1 project five promising signal enhancement algorithms are validated for future use in hearing instrument devices. To assess the algorithm performance solely based on simulation experiments, a number of physical evaluation measures have been proposed that incorporate basic aspects of normal and impaired human hearing. Additionally, each of the algorithms has been implemented on a common real-time hardware/software platform, which facilitates a profound subjective validation of the algorithm performance. Recently, a multicenter study has been set up across five different test centers in Belgium, the Netherlands, Germany and Switzerland to perceptually evaluate the selected signal enhancement approaches with normally hearing and hearing impaired listeners.

关键词： Auditory system Signal processing algorithms Speech Signal to noise ratio Noise measurement

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Simulation of realistic infrared texture of grassland

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Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2011年第12 SUPPL.期32卷 103-108页

作者： Liu, Qian Zhu, Feng Hao, Yingming Shenyang Institute of Automation Chinese Acad. of Sci. Shenyang 110016 China Graduate School of the Chinese Acad. of Sci. Beijing 110080 China Key Laboratory of Opto-Electronics Information Processing Chinese Acad. of Sci. Shenyang 110016 China Key Lab. of Image Understanding and Computer Vision Liaoning Shenyang 110016 China

A new method is introduced to simulate realistic infrared texture of grassland. First, the radiative temperature of grassland is solved by integrating material composition, environmental and meteorological factors. As the spectroscopic properties of grass and soil vary greatly, the infrared radiation characteristics are computed respectively. Next, the texture structure of grassland is modeled based on the analysis of spatial distribution of natural grassland. Finally, the infrared radiance of grass and soil are mapped to image space and a realistic grassland texture is generated. Experiment results show that similar visual features exhibit between simulated textures and actual grassland textures, which justifies the effectiveness of the proposed method.

关键词： Infrared radiation

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