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SPCNet: A Panoramic image depth estimation method based on spherical convolution

SPCNet: A Panoramic image depth estimation method based on s...

引用

IEEE visual communications and image processing (VCIP)

作者： Si He Yu Liu Yumei Wang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Peng Cheng Laboratory Research Center of Networks and Communications Shenzhen China

ISBN: (纸本)9781728173221

As an emerging media format, virtual reality (VR) has attracted the attention of researchers. 6-DoF VR can reconstruct the surrounding environment with the help of the depth information of the scene, so as to provide users with immersive experience. However, due to the lack of depth information in panoramic image, it is still a challenge to convert panorama to 6-DOF VR. In this paper, we propose a new depth estimation method SPCNet based on spherical convolution to solve the problem of depth information restoration of panoramic image. Particularly, spherical convolution is introduced to improve depth estimation accuracy by reducing distortion, which is attributed to Equi-Rectangular Projection (ERP). The experimental results show that many indicators of SPCNet are better than other advanced networks. For example, RMSE is 0.419 lower than UResNet. Moreover, the threshold accuracy of depth estimation has also been improved.

关键词： Solid modeling Convolution visual communication Estimation Immersive experience Media Feature extraction

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image-Based Indoor Localization Using Smartphone Cameras In Textureless Environment

Image-Based Indoor Localization Using Smartphone Cameras In ...

引用

International conference on Information and Computer Technologies (ICICT)

作者： Ping Zheng Danyang Qin Jianan Bai Lin Ma Department of Electronics and Communication Engineering Heilongjiang University Harbin China Heilongjiang University Harbin China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Information and Communication Engineering Heilongjiang University Harbin China Department of Electronics and Information Engineering Harbin Institute of Technology Harbin China

In recent years, Location Based Services (LBS) have developed rapidly. There is an urgent need to establish a low-cost and convenient method to get precise positions indoors. Therefore, we choose an easier way of using monocular camera mounted on a mobile terminal to achieve indoor visual positioning. In this paper, two main problems are studied under this condition. Firstly, the feature extraction method is designed to minimize the loss of positioning information while processing quickly in textureless environment. Secondly, the relative direction vector obtained from the epipolar geometry cannot converge at the point to be located due to errors, so a position determination method is proposed to transform the problem of positioning into distance calculation. A WeChat mini program was built for users to obtain location information. Experimental results show that 90% positioning error was less than 0. 5m, and the average error was less than 0.32m.

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Underwater image Enhancement with Multi-Scale Residual Attention Network

Underwater Image Enhancement with Multi-Scale Residual Atten...

引用

IEEE visual communications and image processing (VCIP)

作者： Yosuke Ueki Masaaki Ikehara Keio Univ. Yokohama Kanagawa Japan

ISBN: (纸本)9781728173221

Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increased because of ocean engineering and underwater robotics. Existing underwater image enhancement methods are based on various assumptions. However, it is almost impossible to define appropriate assumptions for underwater images due to the diversity of underwater images. Therefore, they are only effective for specific types of underwater images. Recently, underwater image enhancement algorisms using CNNs and GANS have been proposed, but they are not as advanced as other image processing methods due to the lack of suitable training data sets and the complexity of the issues. To solve the problems, we propose a novel underwater image enhancement method which combines the residual feature attention block and novel combination of multi-scale and multi-patch structure. Multi-patch network extracts local features to adjust to various underwater images which are often Non-homogeneous. In addition, our network includes multi-scale network which is often effective for image restoration. Experimental results show that our proposed method outperforms the conventional method for various types of images.

关键词： Costs image color analysis visual communication Oceans Training data Feature extraction Distortion

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Encoder-Decoder Joint Enhancement for Video Chat

Encoder-Decoder Joint Enhancement for Video Chat

引用

IEEE visual communications and image processing (VCIP)

作者： Zhenghao Zhang Zhao Wang Yan Ye Shiqi Wang Changwen Zheng University of Chinese Academy of Sciences City University of Hong Kong Alibaba Group Chinese Academy of Sciences Institute of Software

ISBN: (纸本)9781728173221

Video chat becomes more and more popular in our daily life. However, how to provide a high-quality video chat with the limited bandwidth is a key challenging task. In this paper, beyond the state-of-the-art video compression system, we propose an encoder-decoder joint enhancement algorithm for the video chat. In particular, the sparse map of the original frame is extracted at the encoder side and signaled to the decoder, which is utilized together with the sparse map of the decoded frame to obtain the boundary transformation map. In this manner, the boundary transformation map represents the key difference between the original frame and the decoded frame and hence can be used to enhance the decoded frame. Experimental results show that the proposed algorithm brings clear subjective and objective quality improvements. At the same quality, the proposed algorithm can achieve 35% bitrate savings compared to the VVC.

关键词： visual communication image processing Bit rate Video compression Decoding Feeds Task analysis

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Learn to Look Around: Deep Reinforcement Learning Agent for Video Saliency Prediction

Learn to Look Around: Deep Reinforcement Learning Agent for ...

引用

IEEE visual communications and image processing (VCIP)

作者： Yiran Tao Yaosi Hu Zhenzhong Chen Wuhan Univeristy Wuhan China

ISBN: (纸本)9781728173221

In the video saliency prediction task, one of the key issues is the utilization of temporal contextual information of keyframes. In this paper, a deep reinforcement learning agent for video saliency prediction is proposed, designed to look around adjacent frames and adaptively generate a salient contextual window that contains the most correlated information of keyframe for saliency prediction. More specifically, an action set step by step decides whether to expand the window, meanwhile a state set and reward function evaluate the effectiveness of the current window. The deep Q-learning algorithm is followed to train the agent to learn a policy to achieve its goal. The proposed agent can be regarded as plug-and-play which is compatible with generic video saliency prediction models. Experimental results on various datasets demonstrate that our method can achieve an advanced performance.

关键词： Q-learning visual communication image processing Predictive models Prediction algorithms Task analysis Context modeling

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Multi-Dimension Aware Back Projection Network For Scene Text Detection

Multi-Dimension Aware Back Projection Network For Scene Text...

引用

IEEE visual communications and image processing (VCIP)

作者： Yizhan Zhao Sumei Li Yongli Chang School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781728173221

Recently, scene text detection based on deep learning has progressed substantially. Nevertheless, most previous models with FPN are limited by the drawback of sample interpolation algorithms, which fail to generate high-quality up-sampled features. Accordingly, we propose an end-to-end trainable text detector to alleviate the above dilemma. Specifically, a Back Projection Enhanced Up-sampling (BPEU) block is proposed to alleviate the drawback of sample interpolation algorithms. It significantly enhances the quality of up-sampled features by employing back projection and detail compensation. Further-more, a Multi-Dimensional Attention (MDA) block is devised to learn different knowledge from spatial and channel dimensions, which intelligently selects features to generate more discriminative representations. Experimental results on three benchmarks, ICDAR2015, ICDAR2017- MLT and MSRA-TD500, demonstrate the effectiveness of our method.

关键词： Deep learning Interpolation visual communication Shape image processing Detectors Benchmark testing

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Data Transformer for Anomalous Trajectory Detection

Data Transformer for Anomalous Trajectory Detection

引用

IEEE visual communications and image processing (VCIP)

作者： Hsuan-Jen Psan Wen-Jiin Tsai National Yang Ming Chiao Tung University HsinChu Taiwan

ISBN: (纸本)9781728173221

Anomaly detection is an important task in many traffic applications. Methods based on deep learning networks reach high accuracy; however, they typically rely on supervised training with large annotated data. Considering that anomalous data are not easy to obtain, we present data transformation methods which convert the data obtained from one intersection to other intersections to mitigate the effort of collecting training data. The proposed methods are demonstrated on the task of anomalous trajectory detection. A General model and a Universal model are proposed. The former focuses on saving data collection effort; the latter further reduces the network training effort. We evaluated the methods on the dataset with trajectories from four intersections in GTA V virtual world. The experimental results show that with significant reduction in data collecting and network training efforts, the proposed anomalous trajectory detection still achieves state-of-the-art accuracy.

关键词： Training Deep learning visual communication image processing Training data Data collection Transformers

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Neural Network based Inter bi-prediction Blending

Neural Network based Inter bi-prediction Blending

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IEEE visual communications and image processing (VCIP)

作者： Franck Galpin Philippe Bordes Thierry Dumas Pavel Nikitin Fabrice Le Leannec InterDigital Inc.

ISBN: (纸本)9781728173221

This paper presents a learning-based method to improve bi-prediction in video coding. In conventional video coding solutions, the motion compensation of blocks from already decoded reference pictures stands out as the principal tool used to predict the current frame. Especially, the bi-prediction, in which a block is obtained by averaging two different motion-compensated prediction blocks, significantly improves the final temporal prediction accuracy. In this context, we introduce a simple neural network that further improves the blending operation. A complexity balance, both in terms of network size and encoder mode selection, is carried out. Extensive tests on top of the recently standardized VVC codec are performed and show a BD-rate improvement of −1.4% in random access configuration for a network size of fewer than 10k parameters. We also propose a simple CPU-based implementation and direct network quantization to assess the complexity/gains tradeoff in a conventional codec framework.

关键词： Video coding Learning systems Quantization (signal) visual communication image processing Neural networks Motion compensation

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visually Optimized Two-Pass Rate Control for Video Coding Using the Low-Complexity XPSNR Model

Visually Optimized Two-Pass Rate Control for Video Coding Us...

引用

IEEE visual communications and image processing (VCIP)

作者： Christian R. Helmrich Ivan Zupancic Jens Brandenburg Valeri George Adam Wieckowski Benjamin Bross Video Communication and Applications Group Fraunhofer Heinrich Hertz Institute (HHI) Berlin Germany

ISBN: (纸本)9781728173221

Two-pass rate control (RC) schemes have proven useful for generating low-bitrate video-on-demand or streaming catalogs. visually optimized encoding particularly using latest-generation coding standards like Versatile Video Coding (VVC), however, is still a subject of intensive study. This paper describes the two-pass RC method integrated into version 1 of VVenC, an open VVC encoding software. The RC design is based on a novel two-step rate-quantization parameter (R-QP) model to derive the second-pass coding parameters, and it uses the low-complexity XPSNR visual distortion measure to provide numerically as well as visually stable, perceptually R-D optimized encoding results. Random-access evaluation experiments confirm the improved objective as well as subjective performance of our RC solution.

关键词： Video coding visualization image coding visual communication Distortion Software Numerical models

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SalGFCN: Graph Based Fully Convolutional Network for Panoramic Saliency Prediction

SalGFCN: Graph Based Fully Convolutional Network for Panoram...

引用

IEEE visual communications and image processing (VCIP)

作者： Yiwei Yang Yucheng Zhu Zhongpai Gao Guangtao Zhai School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

The saliency prediction of panoramic images is dramatically affected by the distortion caused by non-Euclidean geometry characteristic. Traditional CNN based saliency pre-diction algorithms for 2D images are no longer suitable for 360-degree images. Intuitively, we propose a graph based fully convolutional network for saliency prediction of 360-degree images, which can reasonably map panoramic pixels to spherical graph data structures for representation. The saliency prediction network is based on residual U-Net architecture, with dilated graph convolutions and attention mechanism in the bottleneck. Furthermore, we design a fully convolutional layer for graph pooling and unpooling operations in spherical graph space to retain node-to-node features. Experimental results show that our proposed method outperforms other state-of-the-art saliency models on the large-scale dataset.

关键词： Geometry visual communication image processing Distributed databases Predictive models Prediction algorithms Distortion

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