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Proceedings of the 33rd Chinese Control Conference, CCC 2014

作者： Zheng, Jiangyu Tan, Xiaobin Zou, Cliff Niu, Yukun Zhu, Jin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230026 China Department of Automation University of Science and Technology of China Hefei230026 China Department of Electrical Engineering and Computer Science University of Central Florida OrlandoFL32816-2362 United States

ISBN: (纸本)9789881563842

With the widespread use of mobile devices, the location-based service (LBS) applications become increasingly popular, which introduces the new security challenge to protect user's location privacy. On one hand, a user expects to report his own location as far as possible away from his real location to protect his location privacy. On the other hand, in order to obtain high quality of service (QoS), users are required to report their locations as accurate as possible. To achieve the dedicated tradeoff between privacy requirement and QoS requirement, we propose a novel approach based on cloaking technique. We also discuss the disadvantage of the traditional general system model and propose an improved model. The basic idea of our approach is to select a sub-area from the generated cloaking area as user's reported location. The sub-area may not contain a user's real location, which prevents an adversary from performing attack with side information. Specifically, by defining an objective function with a novel location privacy metric and a QoS metric, we are able to convert the privacy issue to an optimization problem. Then, location privacy metric and QoS metric are given. To reduce the complexity of the optimization, a heuristic algorithm is proposed. Through privacy-preserving analysis and comparison with related work [8], we demonstrate the effectiveness and efficiency of our approach. © 2014 TCCT, CAA.

关键词： Location based services

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Evolution of AVS video coding standards: twenty years of innovation and development

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Science China(information Sciences) 2022年第9期65卷 50-73页

作者： Siwei MA Li ZHANG Shiqi WANG Chuanmin JIA Shanshe WANG Tiejun HUANG Feng WU Wen GAO Institute of Digital Media School of Computer Science Peking University Bytedance Inc. Department of Computer Science City University of Hong Kong CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China

Ever since the founding of the Audio and Video Coding Standard(AVS) Workgroup of China in 2002, it has been dedicated to advancing and innovating the digital audio-video industry with highly efficient and economical encoding/decoding technologies. Three representative generations of video coding standards have been finalized and published, consistently improving the coding performance in the past two *** series of AVS standards establish solid foundations for ubiquitous video applications in the areas including acquisition, coding, production, delivery, integrated system, public service, general screen content, and mixed reality media. Along with the standardization process, an extensive amount of studies have been carried out on efficiency-aware designation, algorithm optimization, and hardware implementation of these innovative video coding techniques. This paper explains how those developed techniques provide a lasting impact on the video coding community, extensively, technologically, and systematically. In particular, we provide a comprehensive survey of the three generations of the standards, and timely and in-depth summarize the efforts of the AVS video coding standards in the twenty years. The rate-distortion performance comparisons,in particular in terms of the 8K ultra-high-definition(UHD) contents, reflect the elegant design of the state-of-the-art AVS3 standards. We have also elaborated on a variety of well-established and promising applications, including commercial level real-time 8K encoder, high-frame-rate decoder chip for cell phone,and live streaming solution for sports. In addition, the China Central Television(CCTV) of China Media Group(CMG), the state television of China, has officially launched the first 8K broadcasting channel(CCTV-8K) since 2021 using AVS3. Given the significant success realized by the AVS standards, it is envisioned that a new era of 8K UHD video is arriving.

关键词： video coding AVS standards video applications intelligent coding

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Quantum-behaved PSO-based Lyapunov control of closed quantum systems

Quantum-behaved PSO-based Lyapunov control of closed quantum...

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第40届中国控制会议

作者： Song Liu Sen Kuang Department of Automation University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China

A Lyapunov-based control scheme is presented to drive closed quantum systems into any target eigenstate with as high population as possible by the quantum-behaved particle swarm optimization（PSO） algorithm. Based on a Lyapunov function with a Hermitian operator to be constructed, a control law with the unknown parameters contained in the Hermitian operator is designed. To achieve high-population state transfer to the target state, we first initialize those unknown parameters by choosing a path to the target state in its energy-level connectivity graph and setting their values along the path. Then, a set of optimal parameters is found by the quantum-behaved PSO algorithm. Finally, numerical simulation experiments are performed on a five-level quantum system and a four-qubit system to demonstrate the effectiveness of the control scheme in this paper.

关键词： Quantum systems Lyapunov control quantum-behaved PSO algorithm energy-level connectivity graph high-population transfer

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Networked predictive control of Hammerstein systems

Networked predictive control of Hammerstein systems

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第三十三届中国控制会议

作者： Tian-tian Wang Yu Kang Yun-Bo Zhao Department of Automation and CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China College of Information Engineering Zhejiang University of Technology

ISBN: (纸本)9781479947249

This article mainly deals with the control and stability problems of networked Hammerstein with nonlinear input. A novel predictive controller design method is proposed to offset the effect of network delay and data dropout. The controller gain which depends on the time delay of the feedback channel is time-variant. Since we assume that the state is not measurable, the control signal is based on the state estimated by the observer. As for the nonlinear part of the input,We assume it satisfies a sector constraint and treat it as a input inaccuracy. Theoretical results are presented for the closed-loop stability by modeling the system as time-delay Hammerstein system with nonlinear inputs. A second-order Hammerstein system is implemented to show the enhanced performance of this control method.

关键词： Networked control system,Hammerstein,nonlinear,delay,dropout

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Disparity-compensated Inter-layer Motion Prediction Using Standardized HEVC Extensions

Disparity-compensated Inter-layer Motion Prediction Using St...

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IEEE International Symposium on Circuits and systems

作者： Li Chen Miska M. Hannuksela Houqiang Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Nokia Technologies Tampere Finland

ISBN: (纸本)9781479983926

The scalable and multiview extensions of the High Efficiency Video Coding share the same high-level syntax coding structure. For the scalable extension, the motion field of the inter-layer reference picture is modified through Motion Field Mapping before used for motion vector prediction. However, the motion field of the inter-layer reference picture is used without modification in the multiview extension. In this paper, a disparity-compensated inter-layer motion prediction is proposed for multiview video coding to achieve disparity compensation in inter-layer motion prediction using the scaled reference layer offset. The experimental results show that comparing with the multiview extension anchor, the proposed method achieves an average of 1.1% bitrate reduction.

关键词： Video coding motion vector Scalability

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Topology-based automatic 3D modeling from multiple images

Topology-based automatic 3D modeling from multiple images

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Long Zhang Xuejin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei P. R. China

3D reconstruction from multiple-view images has drawn a lot of attentions in computer graphics and computer vision communities. Traditional techniques usually end at discrete 3D point clouds computed from feature correspondence. However, geometric structure remains ambiguous in these unstructured point clouds, especially for man-made objects like buildings, indoor scenes. This paper proposes a new method to automatically reconstruct the main geometric structure of the scene composed of planar faces. First, dense 3D point clouds are reconstructed by applying patch-based multi-view stereo (PMVS) algorithm [1]. Then 3D planar primitives are extracted using a RANSAC-based approach [2]. We present a novel method to analyze the adjacency relations of the planar primitives to estimate the 3D intersection lines on the corresponding faces. Junctions and polygonal faces are computed from the 3D intersection lines along with complementary image information to compose the topology structure. Finally, texture for each face is extracted from the image under the best view. Experimental results demonstrate the feasibility of our system by successfully reconstructing the main structure of a wide range of scenes and constructing a texture-mapped piecewise-planar 3D model from images in multiple views.

关键词： Three-dimensional displays Image reconstruction Solid modeling Computational modeling Junctions Topology Surface reconstruction

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Sasl: Saliency-adaptive sparsity learning for neural network acceleration

arXiv

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arXiv 2020年

作者： Shi, Jun Xu, Jianfeng Tasaka, Kazuyuki Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China KDDI Research Japan

Accelerating the inference speed of CNNs is critical to their deployment in real-world applications. Among all the pruning approaches, those implementing a sparsity learning framework have shown to be effective as they learn and prune the models in an end-to-end data-driven manner. However, these works impose the same sparsity regularization on all filters indiscriminately, which can hardly result in an optimal structuresparse network. In this paper, we propose a Saliency-Adaptive Sparsity Learning (SASL) approach for further optimization. A novel and effective estimation of each filter, i.e., saliency, is designed, which is measured from two aspects: The importance for the prediction performance and the consumed computational resources. During sparsity learning, the regularization strength is adjusted according to the saliency, so our optimized format can better preserve the prediction performance while zeroing out more computation-heavy filters. The calculation for saliency introduces minimum overhead to the training process, which means our SASL is very efficient. During the pruning phase, in order to optimize the proposed data-dependent criterion, a hard sample mining strategy is utilized, which shows higher effectiveness and efficiency. Extensive experiments demonstrate the superior performance of our method. Notably, on ILSVRC-2012 dataset, our approach can reduce 49.7% FLOPs of ResNet-50 with very negligible 0.39% top-1 and 0.05% top-5 accuracy degradation. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Offline and Online Optical Flow Enhancement for Deep Video Compression

arXiv

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arXiv 2023年

作者： Tang, Chuanbo Sheng, Xihua Li, Zhuoyuan Zhang, Haotian Li, Li Liu, Dong CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flows in most of the existing deep video compression networks. Indeed, these networks often adopt pre-trained optical flow estimation networks for motion estimation. The optical flows, however, may be less suitable for video compression due to the following two factors. First, the optical flow estimation networks were trained to perform inter-frame prediction as accurately as possible, but the optical flows themselves may cost too many bits to encode. Second, the optical flow estimation networks were trained on synthetic data, and may not generalize well enough to real-world videos. We address the twofold limitations by enhancing the optical flows in two stages: offline and online. In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e.g. H.266/VVC, as we believe the motion information of H.266/VVC achieves a better rate-distortion tradeoff. In the online stage, we further optimize the latent features of the optical flows with a gradient descent-based algorithm for the video to be compressed, so as to enhance the adaptivity of the optical flows. We conduct experiments on a state-of-the-art deep video compression scheme, DCVC. Experimental results demonstrate that the proposed offline and online enhancement together achieves on average 12.8% bitrate saving on the tested videos, without increasing the model or computational complexity of the decoder side. Copyright © 2023, The Authors. All rights reserved.

关键词： Image coding

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Image-to-image translation with multi-path consistency regularization

arXiv

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arXiv 2019年

作者： Lin, Jianxin Xia, Yingce Wang, Yijun Qin, Tao Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China University of Science and Technology of China Microsoft Research Asia

Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from source domain Ds to target domain Dt as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images;the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version through Ds → Dt → Ds translation. In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation Ds → Dt and indirect translation Ds → Da → Dt with Da as an auxiliary domain, to regularize training. For multi-domain translation (at least, three) which focuses on building translation models between any two domains, at each training iteration, we randomly select three domains, set them respectively as the source, auxiliary and target domains, build the multi-path consistency loss and optimize the network. For two-domain translation, we need to introduce an additional auxiliary domain and construct the multi-path consistency loss. We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-toface translation, paint-to-photo translation, and deraining/de-noising translation. Copyright © 2019, The Authors. All rights reserved.

关键词： Iterative methods

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Image Deblocking via Group Sparsity Optimization

Image Deblocking via Group Sparsity Optimization

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IEEE International Symposium on Circuits and systems

作者： Zhenbo Lu Houqiang Li Weiping Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Heifei Anhui China

ISBN: (纸本)9781479983926

Block-wise compressed image often suffers from the blocking artifacts. In this paper, we propose a novel deblocking scheme for compressed image, by combining image's sparse property and its self-similarity together, called group sparsity optimization. Instead of processing each image patch individually, in the proposed scheme, similar patches in one group are required to be well-represented on learned dictionary collaboratively, using group sparsity regularization. The group sparsity not only imposes every patch's representation to be sparse, bus also requires patches' coefficients in the group share the similar pattern. The experiment results on standard test images demonstrate that our scheme can improve the PSNR of the compressed images by an average of 1.25 dB, and outperform state of the art deblocking approaches.

关键词： Image Deblocking Image Restoration JPEG compression

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