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检索条件"任意字段=Conference on Visual Communications and Image Processing"

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Flocking Birds of a Feather Together: Dual-step GAN Distillation via Realer-Fake Samples

Flocking Birds of a Feather Together: Dual-step GAN Distilla...

引用

IEEE visual communications and image processing (VCIP)

作者： Jingwen Ye Zunlei Feng Xinchao Wang National University of Singapore Zhejiang University

ISBN: (纸本)9781665475938

Recent Generative Adversarial Networks (GAN) models deliver visually authentic images and find their applications in various applications. Due to their heavy size, however, it remains cumbersome to deploy state-of-the-art GAN on the edge side, such as mobile terminals. In this paper, we propose a practical and very effective approach for distilling GAN models, so as to produce competent student models that come with smaller sizes. Unlike other convolutional neural network models whose final output spans a small or reasonably-sized space, based on which knowledge distillation is carried out, GAN produces images as output that span an enormous space, making the distillation much more challenging. To this end, we propose an effective dual-step distillation approach tailored for GAN. Instead of directly enforcing the student output distribution to imitate that of the teacher, we partition the generated images into two sets, the “realer” one and the “fake” one, based on the image quality, and sequentially minimize the discrepancy first between fake images of the student and the teacher, and then between realer ones. This seemingly simple strategy turns out highly effective in terms of exploring output space structure and further the knowledge transfer. Experimental results have shown that the proposed method can be easily generalized and demonstrate the learned portable generator produces competitive or even better results both in quality and quantity.

关键词： Training Knowledge engineering image quality visual communication image processing image edge detection Generative adversarial networks

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Center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation

Center-free intuitionistic fuzzy c-means clustering algorith...

引用

Natural Language processing (ICNLP), International conference on

作者： Lan Rong Wang Shumin He Hu Zhao Feng Yu Haiyan Zhang Lu School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

In order to address the issue that the center-free fuzzy c-means (CFFCM) clustering algorithm does not consider the texture features and spatial information of pixels, and the time complexity is too high, a center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation is proposed. In the proposed algorithm, the voting model is used to generate intuitionistic fuzzy sets (IFS), and the generated hesitation degree and membership degree are combined with spatial information to design a spatial intuitionistic membership degree similarity model. This model can deal with the similarity between pixels and classes in gray information, so the segmentation efficiency is improved. At the same time, the intuitionistic fuzzy local binary pattern (IFLBP) operator is used to extract the image texture information and introduce it into the objective function. Spatial membership similarity model is used to process texture information and improve the segmentation accuracy of the algorithm. The results of simulation experiment show that the proposed has advantages in both visual effect and evaluation index.

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UGNet: Underexposed images Enhancement Network based on Global Illumination Estimation

UGNet: Underexposed Images Enhancement Network based on Glob...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Fang, Yuan Zhu, Wenzhe Zhu, Qing Beijing Univ Technol Sch Software Engn Beijing Peoples R China

ISBN: (纸本)9781728180687

This paper proposes a new neural network for enhancing underexposed images. Instead of the decomposition method based on Retinex theory, we introduce smooth dilated convolution to estimate global illumination of the input image, and implement an end-to-end learning network model. Based on this model, we formulate a multi-term loss function that combines content, color, texture and smoothness losses. Our extensive experiments demonstrate that this method is superior to other methods in underexposed image enhancement. It can cover more color details and be applied to various underexposed images robustly.

关键词： underexposed enhancement global illumination estimation image restoration smooth dilated convolution

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Adaptive Kernelized Evidence C-Means Clustering Combining Spatial Information for Noisy image Segmentation

Adaptive Kernelized Evidence C-Means Clustering Combining Sp...

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Natural Language processing (ICNLP), International conference on

作者： Lan Rong Mi Haowen Qu Na Zhao Feng Yu Haiyan Zhang Lu School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

Although the evidence c-means clustering (ECM) has the capability to process uncertain information, it is not suitable for noisy image segmentation, because the spatial information of pixels is not considered. To solve the problem, an adaptive kernelized evidence c-means clustering combining spatial information for noisy image segmentation algorithm is proposed. Firstly, an adaptive noise distance that can be iteratively updated is constructed using the local information of the pixels. Secondly, to improve the classification performance, an adaptive kernel function is proposed to measure the distance between the pixel and the cluster center. Simultaneously, the original, local and non-local information of pixels are introduced adaptively into the objective function to enhance the robustness to noise. In the iteration, the noise cluster is automatically recovered using the recovery factor constructed by the gray and spatial information of neighborhood pixels. Finally, the credal partition is transformed into a fuzzy partition by pignistic transformation, the classification of pixel be determined by the maximum membership principle. Experiments on synthetic images and real images demonstrate that the proposed algorithm has strong noise suppression ability. visual effects and evaluation indexes verify the effectiveness of the proposed algorithm for noisy image segmentation.

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Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition

Annotating Only at Definite Pixels: A Novel Weakly Supervise...

引用

IEEE visual communications and image processing (VCIP)

作者： Xun Zhu Mengqiu Xu Ming Wu Chuang Zhang Bin Zhang Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665475938

Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning method relies on the pixel-level label, which is labor-intensive and time-consuming. Moreover, it is impossible to accurately annotate all pixels of the sea fog region due to the limited ability of the human eye to distinguish between low clouds and sea fog. In this paper, we propose a novel approach of point-based annotation for weakly supervised semantic segmentation with the auxiliary information of International Comprehensive Ocean-Atmosphere Data Set (ICOADS) visibility data. It only needs several definite points for both foreground and background, which significantly reduces the annotation cost of manpower. We conduct extensive experiments on Himawari-8 satellite remote sensing images to demonstrate the effectiveness of our annotation method. The mean intersection over union (mIoU) and overall recognition accuracy of our annotation method reach 82.72% and 95.18 %, respectively. Compared with the fully supervised learning method, the accuracy and the recognition rate of sea fog area are improved with a maximum increase of 7.69% and 9.69 %, respectively.

关键词： image recognition Satellites Costs Annotations visual communication Semantic segmentation Supervised learning

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Deep Reference Frame Interpolation based Inter Prediction Enhancement for Versatile Video Coding

Deep Reference Frame Interpolation based Inter Prediction En...

引用

IEEE visual communications and image processing (VCIP)

作者： Jianghao Jia Zizheng Liu Xiaozhong Xu Shan Liu Zhenzhong Chen School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Media Lab Shenzhen China Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9781665475938

In video coding, bi-directional inter prediction aims to remove temporal redundancy via two-sided previously coded frames as reference. High-quality reference frames are essential to reduce the prediction residuals and improve coding efficiency performance. In this paper, we propose a deep learning-based reference frame interpolation method to enhance bi-prediction by introducing a synthetic frame to reference picture lists. Specifically, reconstructed frames are fed into a well-designed interpolation and filtering network to synthesize a picture which can be regarded as an additional reference of to-be-coded frame. Then, the picture is inserted at the appropriate place in reference picture lists to provide a more reliable reference for subsequent motion estimation and motion compensation. Experimental results show that the proposed method achieves 2.03%/6.96%/6.40% coding efficiency improvements for Y/U/V components under random access configuration, when compared with the VVC reference software VTM-15.0.

关键词： Video coding Interpolation image coding Filtering visual communication Redundancy Bidirectional control

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Augmented Normalizing Flow for Point Cloud Geometry Coding

Augmented Normalizing Flow for Point Cloud Geometry Coding

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IEEE visual communications and image processing (VCIP)

作者： Siao-Yu Li Ji-Jin Chiu Jui-Chiu Chiang Wen-Hsiao Peng Wen-Nung Lie Department of Electrical Engineering National Chung Cheng University Chia-Yi Taiwan Department of Computer Science NationalYang Ming Chiao Tung University Hsin-Chu Taiwan

ISBN: (纸本)9781665475938

With the increased popularity of immersive media, point clouds have become one of the popular data representations for presenting 3D scenes. The huge amount of point cloud data poses a great challenge on their storage and real-time transmission, which calls for efficient point cloud compression. This paper presents a novel point cloud geometry compression technique based on learning end-to-end an augmented normalizing flow (ANF) model to represent the occupancy status of voxelized data points. The higher expressive power of ANF than variational autoencoders (V AE) is leveraged for the first time to represent binary occupancy status. Compared to two coding standards developed by MPEG, namely G-PCC (geometry-based point cloud compression) and V-PCC (video-based point cloud compression), our method achieves more than 80% and 30% bitrate reduction, respectively. Compared to several learning-based methods, our method also yields better performance.

关键词： Point cloud compression Geometry Learning systems image coding Three-dimensional displays visual communication Transform coding

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Adaptive boundary width of Geometric Partitioning Mode for Beyond Versatile Video Coding

Adaptive boundary width of Geometric Partitioning Mode for B...

引用

IEEE visual communications and image processing (VCIP)

作者： Haruhisa Kato Yoshitaka Kidani Kei Kawamura Sei Naito KDDI Research Inc. Saitama Japan

ISBN: (纸本)9781665475938

In order to improve coding efficiency beyond versatile video coding (VVC), we propose an extended geometric partitioning mode (GPM). GPM is a new inter prediction in VVC and is applied to the object boundary between the foreground and background with different motions. Specifically, GPM partitions a rectangular coding block into two regions with 64 predefined types of straight lines, generates inter prediction samples for each partitioned region and then blends them with a fixed boundary width to obtain the final prediction samples. However, the fixed boundary width of GPM is not always optimal for diverse video content. To solve this problem, the proposed method allows GPM to select multiple boundary widths by block-wise signaling. Furthermore, the proposed method also restricts the selectable boundary width according to the short side of the block to reduce the encoding time for selecting the optimal width. Experiment results following common test conditions in JVET showed an improvement in coding efficiency with bitrate savings of 0.11 % and 3.20 % for camera-captured content and for pure screen or video game content, respectively, compared VVC reference software.

关键词： Video coding Video games image coding visual communication Bit rate Cameras Software

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A Fast Motion Estimation Method With Hamming Distance for LiDAR Point Cloud Compression

A Fast Motion Estimation Method With Hamming Distance for Li...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuhao An Yiting Shao Ge Li Wei Gao Shan Liu School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Media Lab Tencent America Palo Alto CA USA

ISBN: (纸本)9781665475938

With more three-dimensional space information, Light detection and ranging (LiDAR) point clouds, which are promising to play more roles in the future, have an urgent need to be efficiently compressed. There are lots of compression methods based on spatial correlations, whereas few studies consider exploiting temporal correlations. In this paper, we propose a different perspective for the motion estimation. In most previous works, geometric distance between matching points was used as the criterion, which has an expensive computational cost and is not accurate. We first propose the Hamming distance between the octree's nodes, instead of the geometric distance between per point which is a more direct criterion. We have implemented our method in the MPEG (Moving Picture Expert Group) Geometry-based PCC (Point Cloud Compression) inter-exploration (G-PCC Inter-EM). Experimental results show our method can provide the average 3.5 % bitrate savings and 92.5 % encoding speed increase in lossless geometric coding, compared to the G-PCC Inter-EM.

关键词： Point cloud compression MPEG standards Geometry image coding Laser radar Correlation visual communication

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Multimodal Crowd Counting with Mutual Attention Transformers

Multimodal Crowd Counting with Mutual Attention Transformers

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Wu, Zhengtao Liu, Lingbo Zhang, Yang Mao, Mingzhi Lin, Liang Li, Guanbin School of Computer Science and Engineering Sun Yat-sen University China Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Crowd counting is a fundamental yet challenging task that aims to automatically estimate the number of people in crowded scenes. Nowadays, with the rapid development of thermal and depth sensors, thermal images and depth maps become more accessible, which are proven to be beneficial information in boosting the performance of crowd counting. Consequently, we propose a Mutual Attention Transformer (MAT) module to fully leverage the complementary information of different modalities. Specifically, our MAT employs a cross-modal mutual attention mechanism to utilize the features of one modality to enhance the features of the other. Moreover, to improve performance by learning better visual representation and further exploiting modality-wise comple-mentarity, we design a self-supervised pre-training method based on cross-modal image reconstruction. Extensive experiments on two standard benchmarks (i.e., RGBT-CC and ShanghaiTechRGBD) show that the proposed method is effective and universal for multimodal crowd counting, outper-forming previous state-of-the-art methods. © 2022 IEEE.

关键词： image reconstruction

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