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  Subjects -> ELECTRONICS (Total: 193 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advanced Materials Technologies     Hybrid Journal  
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 7)
Advances in Electronics     Open Access   (Followers: 94)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Power Electronics     Open Access   (Followers: 39)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 355)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 26)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 14)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal  
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Batteries     Open Access   (Followers: 7)
Batteries & Supercaps     Hybrid Journal  
Bell Labs Technical Journal     Hybrid Journal   (Followers: 30)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 22)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 13)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 9)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 311)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 123)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 104)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 103)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 55)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage     Hybrid Journal  
Energy Storage Materials     Full-text available via subscription   (Followers: 3)
EPE Journal : European Power Electronics and Drives     Hybrid Journal  
EPJ Quantum Technology     Open Access   (Followers: 1)
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 212)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 100)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 81)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 51)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 75)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 73)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 58)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 44)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 78)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access  
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 57)
IET Smart Grid     Open Access  
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 74)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 13)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 11)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 3)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 16)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 10)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 25)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 11)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 35)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 8)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 9)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 3)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 186)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 10)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 10)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 11)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 30)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Microelectronics and Solid State Electronics     Open Access   (Followers: 28)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 42)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 9)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 2)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 56)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 78)
Solid State Electronics Letters     Open Access  
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Transactions on Electrical and Electronic Materials     Hybrid Journal  
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Ural Radio Engineering Journal     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Similar Journals
Journal Cover
IEEE Transactions on Circuits and Systems for Video Technology
Journal Prestige (SJR): 0.977
Citation Impact (citeScore): 5
Number of Followers: 26  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1051-8215
Published by IEEE Homepage  [191 journals]
  • IEEE Transactions on Circuits and Systems for Video Technology publication
           information
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • IEEE Transactions on Circuits and Systems for Video Technology publication
           information
    • Abstract: Provides a listing of current committee members and society officers.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Nonconvex Truncated Nuclear Norm Minimization Based on Adaptive Bisection
           Method
    • Authors: Xinhua Su;Yilun Wang;Xuejing Kang;Ran Tao;
      Pages: 3159 - 3172
      Abstract: The explosive growth in high-dimensional visual data requires effective regularization techniques to utilize the underlying low-dimensional structure. We consider low-rank matrix recovery, and many existing approaches are based on the nuclear norm regularization. Recently, truncated nuclear norm (TNNR) has been proposed to achieve a better approximation to the rank function than that of the traditional nuclear norm. TNNR was defined by the nuclear norm by subtracting the sum of the largest r singular values. However, the estimation of r is not trivial. In addition, the original algorithm based on TNNR only considers the matrix completion cases and requires double loops, which is not quite computationally efficient. Correspondingly, in this paper, we propose the adaptive bisection method to adaptively estimate r, which can efficiently reduce the cost of computation. Moreover, to further accelerate computing, we apply iteratively reweighted nuclear norm to solve the nonconvex TNNR directly, and the convergence can also be guaranteed. Finally, we extend the applications of TNNR from the matrix completion problems to the general low-rank matrix recovery. Extensive experiments validate the superiority of the proposed algorithm over the state-of the-art methods.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Robust Discriminative Metric Learning for Image Representation
    • Authors: Zhengming Ding;Ming Shao;Wonjun Hwang;Sungjoov Suh;Jae-Joon Han;Changkyu Choi;Yun Fu;
      Pages: 3173 - 3183
      Abstract: Metric learning has attracted significant attention in the past decades, because of its appealing advances in various real-world tasks, e.g., person re-identification and face recognition. Traditional supervised metric learning attempts to seek a discriminative metric, which could minimize the pairwise distance of within-class data samples, while maximizing the pairwise distance of data samples from various classes. However, it is still a challenge to build a robust and discriminative metric, especially for corrupted data in the real-world application. In this paper, we propose a Robust Discriminative Metric Learning algorithm through fast low-rank representation and denoising strategy. To be specific, the metric learning problem is guided by a discriminative regularization by incorporating the pair-wise or class-wise information. Moreover, the low-rank basis learning is jointly optimized with the metric to better uncover the global data structure and remove noise. Furthermore, the fast low-rank representation is implemented to mitigate the computational burden and ensure the scalability on large-scale datasets. Finally, we evaluate our learned metric on several challenging tasks, e.g., face recognition/verification, object recognition, image clustering, and person re-identification. The experimental results verify the effectiveness of our proposed algorithm in comparison to many metric learning algorithms, even deep learning ones.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Toward Efficient Image Representation: Sparse Concept Discriminant Matrix
           Factorization
    • Authors: Meng Pang;Yiu-Ming Cheung;Risheng Liu;Jian Lou;Chuang Lin;
      Pages: 3184 - 3198
      Abstract: The key ingredients of matrix factorization lie in basic learning and coefficient representation. To enhance the discriminant ability of the learned basis, discriminant graph embedding is usually introduced in the matrix factorization model. However, the existing matrix factorization methods based on graph embedding generally conduct discriminant analysis via a single type of adjacency graph, either similarity-based graphs (e.g., Laplacian eigenmaps graph) or reconstruction-based graphs (e.g., L1-graph), while ignoring the cooperation of the different types of adjacency graphs that can better depict the discriminant structure of original data. To address the above issue, we propose a novel Fisher-like criterion, based on graph embedding, to extract sufficient discriminant information via two different types of adjacency graphs. One graph preserves the reconstruction relationships of neighboring samples in the same category, and the other suppresses the similarity relationships of neighboring samples from different categories. Moreover, we also leverage the sparse coding to promote the sparsity of the coefficients. By virtue of the proposed Fisher-like criterion and sparse coding, a new matrix factorization framework called Sparse concept Discriminant Matrix Factorization (SDMF) is proposed for efficient image representation. Furthermore, we extend the Fisher-like criterion to an unsupervised context, thus yielding an unsupervised version of SDMF. Experimental results on seven benchmark datasets demonstrate the effectiveness and efficiency of the proposed SDMFs on both image classification and clustering tasks.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Optimized Tone Mapping Function for Contrast Enhancement Considering Human
           Visual Perception System
    • Authors: Ki Sun Song;Moon Gi Kang;
      Pages: 3199 - 3210
      Abstract: Conventional contrast enhancement methods, including global and local enhancements, produce enhanced images with some limitations. Global contrast enhancement does not take the local characteristics into consideration, and therefore, the enhancement performance could be limited. On the other hand, a local contrast enhancement method achieves a noticeable improvement, but it generates unnatural improvement results compared with the input image. Due to the complementary characteristics of these two methods, it is hard to achieve remarkable contrast enhancement without visual artifacts. To overcome the limitations, we propose a new tone mapping function for contrast enhancement using an optimization approach that is subject to constraints such as the output image needing to be enhanced naturally and noticeably. Since contrast enhancement without artificiality is possible when the enhancement process mimics the human eye, we model the human visual perception system, and then, the model is incorporated into the proposed tone mapping function. Consequently, the contrast of the image is adaptively enhanced according to a region that is more attractive to a person. The experimental results demonstrate that the proposed algorithm outperforms other contrast enhancement methods in terms of both objective and subjective criteria.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Visual Haze Removal by a Unified Generative Adversarial Network
    • Authors: Yanwei Pang;Jin Xie;Xuelong Li;
      Pages: 3211 - 3221
      Abstract: Existence of haze significantly degrades visual quality and hence negatively affects the performance of visual surveillance, video analysis, and human-machine interaction. To remove haze from a visual signal, in this paper, we propose a generative adversarial network for visual haze removal called HRGAN. HRGAN consists of a generator network and a discriminator network. A unified network jointly estimating transmission maps, atmospheric light, and haze-free images (called UNTA) is proposed as the generator network of HRGAN. Instead of being optimized by minimizing the pixel-wise loss, HRGAN is optimized by minimizing a novel loss function consisting of pixel-wise loss, perceptual loss, and adversarial loss produced by a discriminator network. Classical model-based image dehazing algorithms consist of three separate stages: 1) estimating transmission map; 2) estimating atmospheric light; and 3) restoring haze-free image by using an atmospheric scattering model to process the transmission map and atmospheric light. Such a separate scheme is not guaranteed to achieve optimal results. On the contrary, UNTA performs transmission map estimation and atmospheric light estimation simultaneously to obtain joint optimal solutions. The experimental results on both synthetic and real-world image databases demonstrate that HRGAN outperforms the state-of-the-art algorithms in terms of both effectiveness and efficiency.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Adaptive Texture-Preserving Denoising Method Using Gradient Histogram and
           Nonlocal Self-Similarity Priors
    • Authors: Linwei Fan;Xuemei Li;Hui Fan;Yanli Feng;Caiming Zhang;
      Pages: 3222 - 3235
      Abstract: Natural image priors play an important role in image denoising, and various prior-based methods have been widely proposed for noise removal. However, these methods tend to smooth the fine image textures while suppressing noise, degrading the image visual quality. To address this problem, in this paper, we propose an adaptive texture-preserving denoising method. In contrast to most existing prior-based denoising methods, two types of priors [gradient histogram matching priors and nonlocal self-similarity (NSS) priors] are proposed, and their combination is used for image denoising. We introduce a hyper-Laplacian distribution of the gradient histogram matching prior, which enforces the gradient histogram of the denoised image to be as close as possible to the estimated reference histogram from the original image. Meanwhile, the proposed model obtained by introducing the NSS priors effectively preserves fine image details and generates sharp image edges. To improve the accuracy of the method, a content-adaptive parameter selection scheme based on edge detection filters is proposed. Moreover, the optimization problem with two types of priors and the content-adaptive parameter added into the objective function becomes a challenging non-convex optimization problem. To effectively solve this problem, we have developed a new numerical solution based on augmented Lagrangian multipliers and alternating minimization scheme. The experimental results demonstrate that the proposed method effectively preserves the texture features of the denoised images and outperforms several variational methods and other state-of-the-art methods in terms of various evaluation indices and visual quality, especially at medium and high noise levels.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Skewed Histogram Shifting for Reversible Data Hiding Using a Pair of
           Extreme Predictions
    • Authors: Suah Kim;Xiaochao Qu;Vasily Sachnev;Hyoung Joong Kim;
      Pages: 3236 - 3246
      Abstract: Reversible data hiding hides data in an image such that the original image is recoverable. This paper presents a novel embedding framework with reduced distortion called skewed histogram shifting using a pair of extreme predictions. Unlike traditional prediction error histogram shifting schemes, where only one good prediction is used to generate a prediction error histogram, the proposed scheme uses a pair of extreme predictions to generate two skewed histograms. By exploiting the structure of the skewed histogram, only the pixels from the peak and the short tail are used for embedding, which decreases the distortion from the lesser number of pixels being shifted. Detailed experiments and analysis are provided using several image databases.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Skeleton-Based Action Recognition With Gated Convolutional Neural Networks
    • Authors: Congqi Cao;Cuiling Lan;Yifan Zhang;Wenjun Zeng;Hanqing Lu;Yanning Zhang;
      Pages: 3247 - 3257
      Abstract: For skeleton-based action recognition, most of the existing works used recurrent neural networks. Using convolutional neural networks (CNNs) is another attractive solution considering their advantages in parallelization, effectiveness in feature learning, and model base sufficiency. Besides these, skeleton data are low-dimensional features. It is natural to arrange a sequence of skeleton features chronologically into an image, which retains the original information. Therefore, we solve the sequence learning problem as an image classification task using CNNs. For better learning ability, we build a classification network with stacked residual blocks and having a special design called linear skip gated connection which can benefit information propagation across multiple residual blocks. When arranging the coordinates of body joints in one frame into a skeleton feature, we systematically investigate the performance of part-based, chain-based, and traversal-based orders. Furthermore, a fully convolutional permutation network is designed to learn an optimized order for data rearrangement. Without any bells and whistles, our proposed model achieves state-of-the-art performance on two challenging benchmark datasets, outperforming existing methods significantly.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Mask-Pose Cascaded CNN for 2D Hand Pose Estimation From Single Color Image
    • Authors: Yangang Wang;Cong Peng;Yebin Liu;
      Pages: 3258 - 3268
      Abstract: We present a cascaded convolutional neural network for 2D hand pose estimation from single in-the-wild RGB images. Inspired by the commonly used silhouette information in the generative pose estimation approaches, we build the cascaded network with two stages, including mask prediction stage as well as pose estimation stage. We find that the two stages network architecture for end-to-end training could benefit from each other for detecting the hand mask and 2D pose. To further improve the hand pose detection accuracy, we contribute a new RGB hand dataset named OneHand10K, which contains 10K RGB images. Each image contains one single hand. We manually obtain the segmented mask and labeled keypoints for guided learning. We hope that this dataset will be a benchmark and encourage more people to conduct research on this challenging topic. Experiments on the validation dataset have demonstrated the superior performance of the proposed cascaded convolutional neural network.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Heterogeneous Association Graph Fusion for Target Association in Multiple
           Object Tracking
    • Authors: Hao Sheng;Yang Zhang;Jiahui Chen;Zhang Xiong;Jun Zhang;
      Pages: 3269 - 3280
      Abstract: Tracking-by-detection is one of the most popular approaches to tracking multiple objects in which the detector plays an important role. Sometimes, detector failures caused by occlusions or various poses are unavoidable and lead to tracking failure. To cope with this problem, we construct a heterogeneous association graph that fuses high-level detections and low-level image evidence for target association. Compared with other methods using low-level information, our proposed heterogeneous association fusion (HAF) tracker is less sensitive to particular parameters and is easier to extend and implement. We use the fused association graph to build track trees for HAF and solve them by the multiple hypotheses tracking framework, which has been proven to be competitive by introducing efficient pruning strategies. In addition, the novel idea of adaptive weights is proposed to analyze the contribution between motion and appearance. We also evaluated our results on the MOT challenge benchmarks and achieved state-of-the-art results on the MOT Challenge 2017.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Effective Gradient Descent-Based Chroma Subsampling Method for Bayer CFA
           Images in HEVC
    • Authors: Kuo-Liang Chung;Yu-Ling Lee;Wei-Che Chien;
      Pages: 3281 - 3290
      Abstract: The most widely used color filter array (CFA) pattern in commercial digital color cameras is the Bayer pattern, and the captured image is called the Bayer CFA image, in which each pixel contains only one color value and each image consists of 25% red, 50% green and 25% blue color values. The chroma 4:2:2 or 4:2:0 subsampling of Bayer CFA images is a necessary process prior to compression. According to the block-distortion minimization principle, in this paper, we propose an effective gradient descent-based chroma subsampling (GDCS) method for Bayer CFA images. Based on the test Bayer CFA images collected from the Kodak and IMAX datasets, experimental results demonstrated that in high efficiency video coding, our GDCS method has better quality and quality-bitrate tradeoff performance of the reconstructed images when compared with the existing chroma subsampling methods.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Enhanced Bi-Prediction With Convolutional Neural Network for
           High-Efficiency Video Coding
    • Authors: Zhenghui Zhao;Shiqi Wang;Shanshe Wang;Xinfeng Zhang;Siwei Ma;Jiansheng Yang;
      Pages: 3291 - 3301
      Abstract: In this paper, we propose an enhanced bi-prediction scheme based on the convolutional neural network (CNN) to improve the rate-distortion performance in video compression. In contrast to the traditional bi-prediction strategy which computes the linear superposition as the predictive signals with pixel-to-pixel correspondence, the proposed scheme employs CNN to directly infer the predictive signals in a data-driven manner. As such, the predicted blocks are fused in a nonlinear fashion to improve the coding performance. Moreover, the patch-to-patch inference strategy with CNN also improves the prediction accuracy since the patch-level information for the prediction of each individual pixel can be exploited. The proposed enhanced bi-prediction scheme is further incorporated into the high-efficiency video coding standard, and the experimental results exhibit a significant performance improvement under different coding configurations.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Flexible Motion Vector Resolution Prediction for Video Coding
    • Authors: Bappaditya Ray;Mohamed-Chaker Larabi;Joël Jung;
      Pages: 3302 - 3316
      Abstract: The latest video coding standard, High Efficiency Video Coding (HEVC), uses quarter-pixel motion vector (MV) resolution for motion compensation. The adaptation of MV resolution supported by progressive MV resolution (PMVR) brings further improvement to performance by progressively adjusting the resolution according to the distance between the MV and its predictor. However, progressive adjustment of resolution by PMVR does not consider the inherent characteristics of the coding block. In this paper, we propose several ways to improve PMVR. First, we show that the performance of PMVR is correlated with the spatiotemporal characteristics of the video sequence. Then, to cope with the limitations of PMVR, we propose a flexible framework for the adaptation of MV resolution using: 1) PU size and gradient; 2) PU size, gradient, and MV components; and 3) PU size and spatiotemporal characteristics of the frames. Finally, a smart motion estimation around multiple MV predictors is performed to take full advantage of the proposed scheme. The proposed tools are implemented on top of HM-16.6. Extensive experiments and comparison with HEVC show 1.3%, 2.7%, and 1.0% average BD-Rate savings for random access, low-delay P, and low-delay B configurations, respectively.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Efficient Shape Coding for Object-Based 3D Video Applications
    • Authors: Zhongjie Zhu;Yuer Wang;Gangyi Jiang;Yueping Yang;
      Pages: 3317 - 3325
      Abstract: Shape is a popular way to define objects and shape coding is a key technique for object-based 3D video applications. In this paper, the issue of efficient shape coding for object-based 3D video applications is addressed, and a novel contour-based and chain-represented scheme is proposed. For a given 3D shape video, contour extraction and preprocessing are first implemented followed by chain-based representation. Then, to achieve high coding efficiency, a chain-based prediction and compensation technique is developed based on joint motion-compensated prediction and disparity-compensated prediction to effectively exploit the intra-view temporal correlation and the inter-view spatial correlation. Experiments are conducted, and the results demonstrate that the proposed scheme is more efficient than the existing methods, including state-of-the-art methods.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Texture-Distortion-Constrained Joint Source-Channel Coding of Multi-View
           Video Plus Depth-Based 3D Video
    • Authors: Pan Gao;Wei Xiang;Dong Liang;
      Pages: 3326 - 3340
      Abstract: A novel joint source and channel coding scheme tailored to 3D video is proposed in this paper to minimize the end-to-end view synthesis distortion within a given total bit rate for both texture and depth as well as a maximum tolerable distortion constraint for texture. First, we formulate a joint texture and depth coding mode selection strategy for error-resilient source coding of multi-view video plus depth-based 3D video through using the Lagrange multiplier method. Then, by considering the effect of residual errors after channel coding, we evolve to a more general formulation that jointly optimizes error-resilient source coding and channel coding in an integrated manner for unequal error protection between texture and depth, for which a theoretic solution using a proposed dual-trellis is derived. Finally, we extend the general formulation by including the texture distortion constraint. We show how to optimize the view synthesis quality while simultaneously catering to the texture quality constraint. Experimental results demonstrate the proposed algorithm has much better performance than existing related work.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Flexible Lossy Compression for Selective Encrypted Image With Image
           Inpainting
    • Authors: Chuan Qin;Qing Zhou;Fang Cao;Jing Dong;Xinpeng Zhang;
      Pages: 3341 - 3355
      Abstract: In this paper, a novel lossy compression scheme for encrypted image based on image inpainting is proposed. In order to maintain confidentiality, the content owner encrypts the original image through a modulo-256 addition encryption and block permutation to mask image content. Then, the third party, such as a cloud server, can compress the selective encrypted image before transmitting to the receiver. During compression, encrypted blocks are categorized into four sets corresponding to different complexity degrees in plaintext domain without the loss of security. By allocating various bit rates to the encrypted blocks from different sets, flexible compression can be achieved with difference quantization. After parsing and decoding the compressed bit stream, the receiver first recovers partial encrypted pixels and then decrypts them. The other missing pixels are further recovered with the assistance of image inpainting based on a total variation model, and the final reconstructed image can be produced. Experimental results demonstrate that the proposed scheme achieves better rate-distortion performance than some of the state-of-the-art schemes.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Mobile Visual Search Compression With Grassmann Manifold Embedding
    • Authors: Zhaobin Zhang;Li Li;Zhu Li;Houqiang Li;
      Pages: 3356 - 3366
      Abstract: With the increasing popularity of mobile phones and tablets, the explosive growth of query-by-capture applications calls for a compact representation of the query image feature. Compact descriptors for visual search (CDVS) is a recently released standard from the ISO/IEC moving pictures experts group, which achieves state-of-the-art performance in the context of image retrieval applications. However, they did not consider the matching characteristics in local space in a large-scale database, which might deteriorate the performance. In this paper, we propose a more compact representation with scale invariant feature transform (SIFT) descriptors for the visual query based on Grassmann manifold. Due to the drastic variations in image content, it is not sufficient to capture all the information using a single transform. To achieve more efficient representations, a SIFT manifold partition tree (SMPT) is initially constructed to divide the large dataset into small groups at multiple scales, which aims at capturing more discriminative information. Grassmann manifold is then applied to prune the SMPT and search for the most distinctive transforms. The experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the standard benchmark CDVS dataset.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • End User Video Quality Prediction and Coding Parameters Selection at the
           Encoder for Robust HEVC Video Transmission
    • Authors: Gosala Kulupana;Dumidu S. Talagala;Hemantha Kodikara Arachchi;Anil Fernando;
      Pages: 3367 - 3381
      Abstract: Along with the rapid increase in the availability of high-quality video formats such as high definition (HD), ultra HD, and high dynamic range, a huge demand for data rates during their transmission has become inevitable. Consequently, the role of video compression techniques has become crucially important in the process of mitigating the data rate requirements. Even though the latest video codec high efficiency video coding (HEVC) has succeeded in significantly reducing the data rate compared to its immediate predecessor H.264/advanced video coding, the HEVC coded videos in the meantime have become even more vulnerable to network impairments. Therefore, it is equally important to assess the consumers' perceived quality degradation prior to transmitting HEVC coded videos over an error-prone network, and to include error resilient features so as to minimize the adverse effects of those impairments. To this end, this paper proposes a probabilistic model which accurately predicts the overall distortion of the decoded video at the encoder followed by an accurate QP-λ relationship which can be used in the rate-distortion optimization (RDO) process. During the derivation process of the probabilistic model, the impacts from the motion vectors, the pixels in the reference frames, and the clipping operations are accounted, and consequently, the model is capable of minimizing the prediction error to as low as 3.11%, whereas the state-of-the-art methods cannot reach below 20.08%, under identical conditions. Furthermore, the enhanced RDO process has resulted in 21.41%-43.59% improvement in the BD-rate compared to the state-of-the-art error resilient algorithms.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • A Low-Power Digital Pixel Driving Scheme for Single-Pulse-PWM-Based
           Display Using AND-Embedded Pixel Circuits
    • Authors: Je-Kwang Cho;Minsu Jeong;
      Pages: 3382 - 3392
      Abstract: A new digital pixel driving scheme is presented for reducing power in the column-line drivers in a single-pulse-PWM-based display. Rather than updating the digital pixel memory value for every access of the memory, the proposed driving scheme utilizes the property that there are only two level transitions in a single-pulse PWM for representing a digital value. With the use of AND-embedded SRAM pixel memory and simple logic controlling the AND gates, this minimizes the number of column-line signal transitions as the row-line scanning progresses. As a result, power dissipation in the column-line drivers is greatly reduced compared to the case of using a conventional digital pixel driving scheme. A quantitative analysis describing the number of column-line signal transitions for both the conventional and proposed schemes agrees well with the simulation results for random digital inputs and real sample images as well, verifying the efficacy on power reduction in the column-line drivers. Including power dissipation by the circuits controlling the AND gates in the pixel memories, power reduction efficiency for many different image samples is at least more than 50%.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Visual-Quality Guided Global Backlight Dimming for Video Display on Mobile
           Devices
    • Authors: Chia-Hung Yeh;Kyle Shih-Huang Lo;Weisi Lin;
      Pages: 3393 - 3403
      Abstract: This proposes a visual-quality guided global backlight dimming (VQG-GBD) algorithm to reduce the power consumption of liquid-crystal display on mobile devices. We build a backlight scaling ratio (BSR) prediction model via visual-quality assessment that not only considers the display contents but also the backlight intensity while measuring video quality. Also, we add visual uncertainty as an indicator to dim the backlight without being noticed by observers. The VQG-GBD includes a training stage and an online stage. For the training stage, first, we collect videos with distinct attributes of brightness and uncertainty. Then, the subjective rating obtains the relationship among the visual quality, BSR, brightness, and visual uncertainty. Finally, we use the trust-region method to build the BSR prediction model. In the online stage, the model is applied to mobile devices for real-time video display and a BSR optimization strategy is proposed to eliminate the flicker effect between frames, followed by three techniques to accelerate the process: 1) motion vector extraction; 2) pixel subsampling to reduce the computation while analyzing frame content; and 3) GPU rendering to speed up the pixel compensation. The experimental results show that VQG-GBD achieves 21% of the power demand reduction on average for displaying videos on mobile devices while preserving good visual quality. The VQG-GBD delivers more power reduction than the state-of-the-art algorithm image integrity-based gray-level error control and multi-histogram-based gray-level error control by 10% and 8%, respectively.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Fast Parallel Implementation of Dual-Camera Compressive Hyperspectral
           Imaging System
    • Authors: Shipeng Zhang;Hua Huang;Ying Fu;
      Pages: 3404 - 3414
      Abstract: Coded aperture snapshot spectral imager (CASSI) provides a potential solution to recover the 3D hyperspectral image (HSI) from a single 2D measurement. The latest proposed design of the dual-camera compressive hyperspectral imager (DCCHI) can collect more information simultaneously with the CASSI to improve the reconstruction quality. The main bottleneck now lies in the high computation complexity of the reconstruction methods, which hinders the practical application. In this paper, we propose a fast parallel implementation based on DCCHI to reach a stable and efficient HSI reconstruction. Specifically, we develop a new optimization method for the reconstruction problem, which integrates the alternative direction multiplier method with the total variation-based regularization to boost the convergence rate. Then, to improve the time efficiency, a novel parallel implementation based on GPU is proposed. The performance of the proposed method is validated on both synthetic and real data. The experimental results demonstrate that our method has a significant advantage in time efficiency, while maintaining a comparable reconstruction fidelity.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Efficient Algorithm Adaptations and Fully Parallel Hardware Architecture
           of H.265/HEVC Intra Encoder
    • Authors: Yuanzhi Zhang;Chao Lu;
      Pages: 3415 - 3429
      Abstract: The growing demand for high-performance ultra-high-definition video coding leads to H.265/high-efficiency video coding (HEVC), where the increased computational complexity and data/timing dependence hinder its coding throughput. To address these challenges, this paper presents four algorithm adaptations and a fully parallel hardware architecture for an H.265/HEVC intra encoder. To the best of our knowledge, this is the first fully parallel H.265/HEVC intra encoder. This design supports 35 prediction modes and all coding tree unit partitions. All PUs are independently processed in four prediction engines for high parallelism. An appropriate set of intra prediction modes, RDO candidates, and CABAC rate estimate instances is assigned to each prediction engine, where internal computational tasks are pipelined and scheduled to maximize the processing throughput. Compared with the HM-15.0 software, the proposed algorithm adaptations lead to a reduction of 27% in computational workload, while the average BD-rate and BD-PSNR are 4.39% and -0.21 dB, respectively. This BD-rate is lower than the existing designs with the same video resolution. FPGA implementation of the proposed design shows that it operates at 120 MHz and supports 45 fps of 1080P video sequences using 201-K logic elements and 120-KB on-chip SRAM. ASIC implementation of the proposed design in TSMC 90-nm technology shows that its clock frequency reaches 320 MHz with a hardware gate count of 2288 K, and that it supports real-time encoding of 30 fps of 4-K video sequences. Compared with the state-of-the-art designs, our proposed design demonstrates advantages in computational complexity, bit rate, video quality, throughput, reliability, and flexibility.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Microshift: An Efficient Image Compression Algorithm for Hardware
    • Authors: Bo Zhang;Pedro V. Sander;Chi-Ying Tsui;Amine Bermak;
      Pages: 3430 - 3443
      Abstract: In this paper, we propose a lossy image compression algorithm called microshift. We employ an algorithm-hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, and then the sub-quantized values are further compressed. Two methods, FAST and MRF models, are proposed to recover the bitdepth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. On an average, our compression algorithm can compress images to 1.25-bits per pixel with a resulting quality that outperforms the state-of-the-art on-chip compression algorithms in both peak signal-to-noise ratio and structural similarity. Then, we propose a hardware architecture and implement the algorithm on an FPGA. The results on the ASIC design further validate the low-hardware complexity and high-power efficiency, showing that our method is promising, particularly for low-power wireless vision sensor networks.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • A 68-mw 2.2 Tops/w Low Bit Width and Multiplierless DCNN Object Detection
           Processor for Visually Impaired People
    • Authors: Xiaobai Chen;Jinglong Xu;Zhiyi Yu;
      Pages: 3444 - 3453
      Abstract: Deep convolutional neural network (DCNN) object detection is a powerful solution in visual perception, but it requires huge computation and communication costs. We proposed a fast and low-power always-on object detection processor that allows visually impaired people to understand their surroundings. We designed an automatic DCNN quantization algorithm that successfully quantizes the data to 8-bit fix-points with 32 values and uses 5-bit indexes to represent them, reducing hardware cost by over 68% compared to the 16-bit DCNN, with negligible accuracy loss. A specific hardware accelerator is designed, which uses reconfigurable process engines to realize multi-layer pipelines to significantly reduce or eliminate the off-chip temporary data transfer. A lookup table is used to implement all multiplications in convolutions to reduce the power significantly. The design is fabricated in SMIC 55-nm technology, and the post-layout simulation shows only 68-mw power at 1.1-v voltage with 155 Go/s performance, achieving 2.2 Top/w energy efficiency.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • Beyond the Watching: Understanding Viewer Interactions in Crowdsourced
           Live Video Broadcasting Services
    • Authors: Xiaodong Wang;Ye Tian;Rongheng Lan;Wen Yang;Xinming Zhang;
      Pages: 3454 - 3468
      Abstract: Crowdsourced live video broadcasting services, such as Twitch and YouTube Live, are becoming increasingly popular. In such a service, viewers are allowed to perform rich interactions, such as posting comments and donating monetary virtual gifts, while watching videos. Understanding viewer interactions is essential for people to comprehend the production and consumption of the crowdsourced live video content and improve the service. However, the basic characteristics of the viewer interactions are still unknown. In this paper, we present a comprehensive measurement study of the viewer interactions on Douyu, a popular crowdsourced live video broadcasting website in China. Our measurement spans four months and contains comment posting and virtual gift donating interactions from tens of millions of viewers in hundreds of thousands of channels. Based on the measurement data, we carry out a content analysis on danmu comments and characterize the patterns of the viewer interactions. We build a suite of models for capturing the gift donating process, viewer activity, and channel popularity. We further analyze the influences of the broadcaster's behavioral factors on a channel's popularity and present methodologies for popularity predicting. Our measurement and analysis have important implications on the design and business policy of the crowdsourced live video broadcasting services.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
  • ICME 2020
    • Pages: 3469 - 3470
      Abstract: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.
      PubDate: Nov. 2019
      Issue No: Vol. 29, No. 11 (2019)
       
 
 
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