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Neural network-based arithmetic coding of intra prediction modes in HEVC

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arXiv 2017年

作者： Song, Rui Liu, Dong Li, Houqiang Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

In both H.264 and HEVC, context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding method. CABAC relies on manually designed binarization processes as well as handcrafted context models, which may restrict the compression efficiency. In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC. Instead of binarization, we propose to directly estimate the probability distribution of the 35 intra prediction modes with the adoption of a multi-level arithmetic codec. Instead of handcrafted context models, we utilize convolutional neural network (CNN) to perform the probability estimation. Simulation results show that our proposed arithmetic coding leads to as high as 9.9% bits saving compared with CABAC. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

A convolutional neural network approach for half-pel interpolation in video coding

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arXiv 2017年

作者： Yan, Ning Liu, Dong Li, Houqiang Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Motion compensation is a fundamental technology in video coding to remove the temporal redundancy between video frames. To further improve the coding efficiency, sub-pel motion compensation has been utilized, which requires interpolation of fractional samples. The video coding standards usually adopt fixed interpolation filters that are derived from the signal processing theory. However, as video signal is not stationary, the fixed interpolation filters may turn out less efficient. Inspired by the great success of convolutional neural network (CNN) in computer vision, we propose to design a CNN-based interpolation filter (CNNIF) for video coding. Different from previous studies, one difficulty for training CNNIF is the lack of ground-truth since the fractional samples are actually not available. Our solution for this problem is to derive the "ground-truth" of fractional samples by smoothing high-resolution images, which is verified to be effective by the conducted experiments. Compared to the fixed half-pel interpolation filter for luma in High Efficiency Video Coding (HEVC), our proposed CNNIF achieves up to 3.2% and on average 0.9% BD-rate reduction under low-delay P configuration. Copyright © 2017, The Authors. All rights reserved.

关键词： Video signal processing

Hierarchical co-segmentation of 3D point clouds for indoor scene

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Hierarchical co-segmentation of 3D point clouds for indoor s...

International Conference on systems, Signals and Image processing, IWSSIP

作者： Yanting Lin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei City China

ISBN: (纸本)9781509063451

Segmentation of point clouds has been studied under a variety of scenarios. However, the segmentation of scanned point clouds for a clustered indoor scene remains significantly challenging due to noisy and incomplete data, as well as scene complexity. Based on the observation that objects in an indoor scene vary largely in scale but are typically supported by planes, we propose a co-segmentation approach. This technique utilizes the mutual agency between the point clouds captured at different times after the objects' poses change due to human actions. Hence, we hierarchically segment scenes from different times into patches and generate tree structures to store their relations. By iteratively clustering patches and co-analyzing them based on the relations between patches, we modify the tree structures and generate our results. To test the robustness of our method, we evaluate it on imperfectly scanned point clouds from a childroom, a bedroom, and two offices scenes.

关键词： Three-dimensional displays Image color analysis geometry Mathematical model Principal component analysis Silicon

Randomized consensus based distributed kalman filtering over wireless sensor networks

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arXiv 2018年

作者： Qin, Jiahu Wang, Jie Shi, Ling Kang, Yu Department of Automation University of Science and Technology of China Hefei230027 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Automation State Key Laboratory of Fire Science Institute of Advanced Technology University of Science and Technology of China Hefei230027 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China

This paper is concerned with developing a novel distributed Kalman filtering algorithm over wireless sensor networks based on randomized consensus strategy. Compared with centralized algorithm, distributed filtering techniques require less computation per sensor and lead to more robust estimation since they simply use the information from the neighboring nodes in the network. However, poor local sensor estimation caused by limited observability and network topology changes which interfere the global consensus are challenging issues. Motivated by this observation, we propose a novel randomized gossip based distributed Kalman filtering algorithm. information exchange and computation in the proposed algorithm can be carried out in an arbitrarily connected network of nodes. In addition, the computational burden can be distributed for a sensor which communicates with a stochastically selected neighbor at each clock step under schemes of gossip algorithm. In this case, the error covariance matrix changes stochastically at every clock step, thus the convergence is considered in a probabilistic sense. We provide the mean square convergence analysis of the proposed algorithm. Under a sufficient condition, we show that the proposed algorithm is quite appealing as it achieves better mean square error performance theoretically than the noncooperative decentralized Kalman filtering algorithm. Besides, considering the limited computation, communication and energy resources in the wireless sensor networks, we propose an optimization problem which minimizes the average expected state estimation error based on the proposed algorithm. To solve the proposed problem efficiently, we transform it into a convex optimization problem. And a sub-optimal solution is attained. Examples and simulations are provided to illustrate the theoretical results. Copyright © 2018, The Authors. All rights reserved.

关键词： Kalman filters

LAND PRICE ASSESMENT BASED ON DEEP NEURAL NETWORK

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LAND PRICE ASSESMENT BASED ON DEEP NEURAL NETWORK

IEEE International geoscience and Remote Sensing Symposium

作者： Ankai Hou Jiaxi Liu Yuxuan Tao Shaobin Jiang Kai Li Zezhong Zheng Jun Xia Yong He Mingcang Zhu Guoqing Zhou Hongsheng Zhang Jiang Li School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC School of Resources and Environment University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu PRC Department of Natural Resources of Sichuan Province Chengdu PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin PRC Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin Hong Kong Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

The land resource is becoming scarcer and scarcer for a rapidly developing city. Thus, the land price assessment is important for the government to auction the land appropriately. In the paper, we introduced the deep neural network to evaluate the land price, taking the Shenzhen city in China as a case. Firstly, twenty influencing factors and land price data were gathered. Then, Shenzhen city was segmented into many grids with a size of 300 × 300 m. Secondly, the land price of each grid was derived with Kriging approach based upon the samples of land price. And the twenty influencing factors was quantified. Thirdly, the land price data and influencing factors were partitioned into training and testing datasets with the ratio of 8:1, and the training data were utilized to train the deep neural network based on regression analysis and classification with different hidden layers. Finally, the results were analyzed, and the deep neural network with the highest accuracy was selected as the optimum model. Therefore, our proposed method is an efficient approach to evaluate the land price with deep neural network.

关键词： Land price assessment Deep neural network

Dynamic data packing towards the optimization of QoC and QoS in networked control systems

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Science China(Technological Sciences) 2016年第1期59卷 72-80页

作者： KANG Yu ZHAO YunBo Department of Automation and State Key Laboratory of Fire Science University of Science and Technology of China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Department of Automation Zhejiang University of Technology

A class of networked control systems is investigated whose communication network is shared with other applications. The design objective for such a system setting is not only the optimization of the control performance but also the efficient utilization of the communication resources. We observe that at a large time scale the data packet delay in the communication network is roughly varying piecewise constant, which is typically true for data networks like the Internet. Based on this observation, a dynamic data packing scheme is proposed within the recently developed packet-based control framework for networked control systems. As expected this proposed approach achieves a fine balance between the control performance and the communication utilization: the similar control performance can be obtained at dramatically reduced cost of the communication resources. Simulations illustrate the effectiveness of the proposed approach.

关键词： networked control systems packet delay variation dynamic data packing quality of control quality of service

Repeat-pass SAR interferometry experiments with gaofen-3: A case study of ningbo area

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arXiv 2017年

作者： Zhang, Tao Lv, Xiaolei Han, Bing Lei, Bin Hong, Jun Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

This paper reports the repeat-pass interferometric SAR results of Gaofen-3, a Chinese civil SAR satellite, acquired in November 2016 and March 2017 from Ningbo area. With the spatial baseline about 600 m and time baseline 116 days, the coherence of the two images still achieve good enough to generate the digital elevation model (DEM). During the InSAR processing, we compared several baseline estimating methods and obtained a good flat-earth phase removed interferogram map. By using the latest SAR interferogram filter and phase unwrapping method we proposed, we improved the coherence up to 0.8 in urban area and obtained a high quality DEM in Ningbo area. In addition, we evaluated the elevation model by comparing with the elevation values extracted from SRTM. And the result shows that accuracy of the elevation map is about 5 m (RMS) in plane area and is about 22m (RMS) in mountainous region, which demonstrated that Gaofen-3 has the powerful ability of repeat-pass SAR Interferometry. Copyright © 2017, The Authors. All rights reserved.

关键词： Surveying

URBAN FUNCTIONAL REGIONS DISCOVERING BASED ON DEEP LEARNING

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URBAN FUNCTIONAL REGIONS DISCOVERING BASED ON DEEP LEARNING

IEEE International geoscience and Remote Sensing Symposium

作者： Fan Mou Rui Kong Kai Li Zezhong Zheng Jun Xia Yong He Mingcang Zhu Guoqing Zhou Hongsheng Zhang Zhigang Liu Ankai Hou Ling Jiang Shengli Wang Jiang Li School of Resources and Environment University of Electronic Science and Technology of China Chengdu PRC State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu PRC Department of Natural Resources of Sichuan Province Chengdu PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin PRC Institute of Space and Earth Information Science The Chinese University of Hong Kong Shatin Hong Kong State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing PRC Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

In recent years, the big data industry chain has become more mature. Analyzing and managing cities by utilizing various big data in cities has become a hot research topic. Urban functional regions discovering is one of the important applications. The mainstream in urban functional regions discovering are probabilistic topic models, such as latent Dirichlet allocation (LDA) based topic model, which seeing the regions as documents and their functions are their topics. These methods require feature engineering by hand, which will construct features of limited expressiveness. To overcome these methods' shortcomings, we introduced a deep learning topic model called document neural autoregressive distribution estimation (DocNADE) into urban functional regions mining. And we did an experiment to test its effect. The experimental result shows that this DocNADE framework has achieved a considerable result in urban function inference compared with Dirichlet Multinomial Regression (DMR) based topic model which is a state of the art of urban functional regions discovering.

关键词： Biological system modeling Deep learning Probabilistic logic Roads Urban areas Laboratories Data mining

RFID Cardinality Estimation Method for Intelligent Warehouse 35

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RFID Cardinality Estimation Method for Intelligent Warehouse

第35届中国控制会议

作者： SHEN Jingjing TAN Xiaobin WU Fan YAN Pei CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

ISBN: (纸本)9781509009107

With the rapid development of RFID technologies,RFID has been introduced into applications such as supply chain management,inventory control,sampling inspection,3-D positioning and object ***,the reader accesses all the tags in its interrogation region while some applications may only need to identify the tags in a specified area which is smaller than the reader's interrogation *** paper concerns the essential problem of estimating cardinality of tags in the specified *** key novelty of our solution builds on an estimation synopsis that can capture key counting information by moving the reader as well as a simple *** the help of this data structure,a BS can be obtained which only contains the target *** computing the number of 1 in the BS,we can easily get cardinality |E| of the tags in the specified *** conduct extensive experiments to examine this design and the results shows that our solution achieves high *** it not requires any modification of tags and can be implemented with only one reader and some passive RFID tags,the proposed method is easy to deploy in a practical system.

关键词： Cardinality estimation RFID tags algorithm design