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共 4148 条记录，以下是1211-1220 订阅

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Introducing Latent Space Correlation to Conditional Autoencoders for Intra Prediction

Introducing Latent Space Correlation to Conditional Autoenco...

引用

IEEE visual communications and image processing (VCIP)

作者： Fabian Brand Jürgen Seiler André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Intra prediction has been an integral part of image and video coders for a long time. A predominant method is angular prediction that extends the reference area in a certain angle into the block. Recently many deep-learning-based methods have been proposed. Since intra prediction uses multiple modes this usually requires training a large number of networks. With a conditional autoencoder we are able to generate an arbitrary number of modes with only one network. In this paper we introduce a novel loss function enforcing a spatially correlated latent space and extend the network structure to the same end. Thereby we are able to propose a simple spatial mode prediction scheme using most-probable-mode lists. By replacing matrix-based intra prediction in VVC with our method, we obtain average rate savings of 0.84% with peak gains of 2.37%.

关键词： Training Correlation Encoding Neural networks Rate-distortion Quantization (signal) Optimization

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Pyramid Real image Denoising Network 34

Pyramid Real Image Denoising Network

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Zhao, Yiyun Jiang, Zhuqing Men, Aidong Ju, Guodong Beijing University of Posts and Telecommunications Beijing China GuangDong TUS-TuWei Technology Co. Ltd Guangzhou China

ISBN: (纸本)9781728137230

While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on real-world noisy images. The main reason is that the real-world noise is more sophisticated and diverse. To tackle the issue of blind denoising, in this paper, we propose a novel pyramid real image denoising network (PRIDNet), which contains three stages. First, the noise estimation stage uses channel attention mechanism to recalibrate the channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features. Third, the stage of feature fusion adopts a kernel selecting operation to adaptively fuse multi-scale features. Experiments on two datasets of real noisy photographs demonstrate that our approach can achieve competitive performance in comparison with state-of-The-Art denoisers in terms of both quantitative measure and visual perception quality. © 2019 IEEE.

关键词： image denoising

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Multi-Scale Video Inverse Tone Mapping with Deformable Alignment

Multi-Scale Video Inverse Tone Mapping with Deformable Align...

引用

IEEE visual communications and image processing (VCIP)

作者： Jiaqi Zou Ke Mei Songlin Sun Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Inverse tone mapping(iTM) is an operation to transform low-dynamic-range (LDR) content to high-dynamic-range (HDR) content, which is an effective technique to improve the visual experience. ITM has developed rapidly with deep learning algorithms in recent years. However, the great majority of deep-learning-based iTM methods are aimed at images and ignore the temporal correlations of consecutive frames in videos. In this paper, we propose a multi-scale video iTM network with deformable alignment, which increases time consistency in videos. We first align the input consecutive LDR frames at the feature level by deformable convolutions and then simultaneously use multi-frame information to generate the HDR frame. Additionally, we adopt a multi-scale iTM architecture with a pyramid pooling module, which enables our network to reconstruct details as well as global features. The proposed network achieves better performance compared to other iTM methods on quantitative metrics and gain a significant visual improvement.

关键词： Feature extraction Dynamic range visualization image reconstruction Correlation Training Telecommunications

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Application of Brain-Computer Interface and Virtual Reality in Advancing Cultural Experience

Application of Brain-Computer Interface and Virtual Reality ...

引用

IEEE visual communications and image processing (VCIP)

作者： Hao-Lun Fu Po-Hsiang Fang Chan-Yu Chi Chung-ting Kuo Meng-Hsuan Liu Howard Muchen Hsu Cheng-Hsun Hsieh Sheng-Fu Liang Shulan Hsieh Cheng-Ta Yang National Cheng Kung University Tainan City Taiwan Southern Taiwan University of Science and Technology Tainan City Taiwan

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Virtual reality (VR), a computer-generated interactive environment, is provided to a user by projecting a peripheral image onto environmental surfaces. VR has an advantage of enhancing the immersive experience. Nowadays, VR has been widely applied in tourism and cultural experience. On the other hand, a recent integration of electroencephalography-based (EEG-based) brain-computer interface (BCI) and VR is capable of promoting the immersive virtual experience. Therefore, our study aims to propose an integrative framework to implement EEG-based BCI in a VR game to advance the cultural experience. A room escape game in a Tainan temple is created. EEG signals arc recorded while users arc playing the game. The online analyses of EEG signals arc used to interact with the VR display. This integrative framework can result in a better experience than the conventional setup.

关键词： visual communication image processing conferences

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Learn image Object Co-segmentation with Multi-scale Feature Fusion 34

Learn Image Object Co-segmentation with Multi-scale Feature ...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Li, Lina Liu, Zhi Zhang, Jian Zhou, Xiaofei Shanghai University Shanghai China University of Technology Sydney Australia Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781728137230

image object co-segmentation aims to segment common objects in a group of images. This paper proposes a novel neural network, which extracts multi-scale convolutional features at multiple layers via a modified VGG network and fuses them both within and across images as the intra-image and the inter-image features. Then these two kinds of features are further fused at each scale as the multi-scale co-features of common objects, and finally the multi-scale co-features are summed up and upsampled to obtain the co-segmentation results. To simplify the network and reduce the rapidly rising resource cost along with the inputs, the reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-The-Art co-segmentation methods while the computation cost has been effectively reduced. © 2019 IEEE.

关键词： Convolution

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Text-to-image Generation via Semi-Supervised Training

Text-to-Image Generation via Semi-Supervised Training

引用

IEEE visual communications and image processing (VCIP)

作者： Zhongyi Ji Wenmin Wang Baoyang Chen Xiao Han School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Intemational Institute of Next Generation Internet Macau University of Science and Technology Macau China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

Synthesizing images from text is an important problem and has various applications. Most of the existing studies of text-to-image generation utilize supervised methods and rely on a fully-labeled dataset, but detailed and accurate descriptions of images are onerous to obtain. In this paper, we introduce a simple but effective semi-supervised approach that considers the feature of unlabeled images as "Pseudo Text Feature". Therefore, the unlabeled data can participate in the following training process. To achieve this, we design a Modality-invariant Semantic- consistent Module which aims to make the image feature and the text feature indistinguishable and maintain their semantic information. Extensive qualitative and quantitative experiments on MNIST and Oxford-102 flower datasets demonstrate the effectiveness of our semi-supervised method in comparison to supervised ones. We also show that the proposed method can be easily plugged into other visual generation models such as image translation and performs well.

关键词： Training Task analysis Semantics Feature extraction visualization image synthesis Semisupervised learning

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Analysis of Feature Enhancements in visual Secret Sharing

Analysis of Feature Enhancements in Visual Secret Sharing

引用

International conference on communications and Signal processing

作者： Raviraja Holla M. D. Suma Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Manipal Karnataka India

ISBN: (数字)9781728149882

ISBN: (纸本)9781728149899

visual Secret Sharing (VSS) aims at sharing of secrecy among many shadows depending on the number of dealers. These encrypted shadows themselves cannot reveal secrecy. A group of a pre-qualified number of shadows is sufficient to obtain the secret image. The quality of this secret is usually approximate to the original confidential image. This technique has evolved, offering solutions to the demands of the matured Internet domain. This paper analyzes the feature advancements in the VSS field. The VSS approaches remain distinct from the conventional cryptography, thereby offering new security solutions in many applications. Security being the central and challenging demand in contemporary information systems, the analysis of the state-of-the-art VSS stands significant.

关键词： image color analysis visualization Encryption Gray-scale Internet

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Deep Residual-Dense Attention Network for image Super-Resolution 1

引用

26th International conference on Neural Information processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Qin, Ding Gu, Xiaodong Fudan Univ Dept Elect Engn Shanghai 200433 Peoples R China

ISBN: (数字)9783030368029

ISBN: (纸本)9783030368029;9783030368012

Recently, a great variety of CNN-based methods have been proposed for single image super-resolution. But how to restore more high-frequency details is still an unsolved issue. It is easy to find that the low-frequency information is similar in a pair of low-resolution and high-resolution images. So the model only needs to pay more attention to the high-frequency information to restore more realistic images which have abundant details and meet human visual system better. In this paper, we propose a deep residual-dense attention network (RDAN) for image super-resolution. Specially, we propose a channel attention module to change the weight of each channel and a spatial attention module to rescale the region weight in a channel map, which can make the model focus more on the high-frequency information. Experimental results on five benchmark datasets show that RDAN is superior to those state-of-the-art methods for both accuracy and visual performance.

关键词： image super-resolution Deep convolution neural network Attention mechanism

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No-Reference Stereoscopic image Quality Assessment Considering Multi-loss Constraints

No-Reference Stereoscopic Image Quality Assessment Consideri...

引用

IEEE visual communications and image processing (VCIP)

作者： Yongtian Han Sumei Li Guanghui Yue Yongli Chang School of Electrical and Information Engineering Tianjin University Tianjin China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

In this paper, a three-channel convolutional neural network (CNN) constrained by multiple loss functions is designed for stereoscopic image quality assessment (SIQA). Given that both monocular and binocular information are crucial for SIQA, we take the patches of left images, right images and difference images as the inputs of the three channels respectively. Since using the ground truth as the labels of image patches cannot accurately characterize their quality, we propose to individually label each image patch to preserve the quality difference among different regions and views. Moreover, the multi-loss structure is adopted in the proposed method to consider both local features and global features simultaneously, which can constrain the feature learning from multiple perspectives. And the additional adaptive loss weights make the multi-loss network more flexible and universal. The experimental results show that the proposed method is superior to other existing SIQA methods with state-of-the-art performance.

关键词： Feature extraction Stereo image processing Training Databases Task analysis Adaptive systems Quality assessment

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Reference picture synthesis for video sequences captured with a monocular moving camera 34

Reference picture synthesis for video sequences captured wit...

引用

34th IEEE International conference on visual communications and image processing, VCIP 2019

作者： Golestani, Hossein Bakhshi Rohlfing, Christian Ohm, Jens-Rainer RWTH Aachen University Institute for Communications Engineering Aachen Germany

ISBN: (纸本)9781728137230

Inter-frame prediction plays an important role in video coding by predicting the current frame from previously encoded pictures, called reference pictures. In the case of camera motion, the content of a current frame could be very different from its reference pictures and may consequently lead to a more difficult Motion Compensation (MC). The main idea of this paper is to process the input 2D video sequence in order to estimate the 3D geometry of the scene and then employ this data to virtually synthesize "geometrically compensated" reference pictures. Since these virtual reference pictures are more similar to the current frame, motion estimation and consequently coding efficiency could be enhanced. The proposed method is tested over six different video sequences and around 11% bitrate reduction is achieved compared to the High Efficiency Video Coding (HEVC) standard. © 2019 IEEE.

关键词： Motion compensation

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