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arXiv 2019年

作者： Zhou, Yangming Hao, Jin-Kao Fu, Zhang-Hua Wang, Zhe Lai, Xiangjing School of Information Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai200237 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education Department of Computer Science LERIA Université d'Angers 2 Boulevard Lavoisier Angers49045 France Institut Universitaire de France 1 rue Descartes Paris75231 France Robotics Laboratory for Logistics Service Institute of Robotics and Intelligent Manufacturing Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size was used in such algorithms to record some best solutions sampled during the search. However, given the particular features of the problem instance under consideration, a population of variable size would be more suitable to ensure the best search performance possible. In this work, we propose variable population memetic search (VPMS), where a strategic population sizing mechanism is used to dynamically adjust the population size during the memetic search process. Our VPMS approach starts its search from a small population of only two solutions to focus on exploitation, and then adapts the population size according to the search status to continuously influence the balancing between exploitation and exploration. We illustrate an application of the VPMS approach to solve the challenging critical node problem (CNP). We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure (called diversified late acceptance search) and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms. The algorithm is able to discover new upper bounds for 13 instances out of the 42 popular benchmark instances, while matching 23 previous best-known upper bounds. Copyright © 2019, The Authors. All rights reserved.

关键词： Population statistics

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On the Tightness of a Cut-Set Bound on Network Function Computation

On the Tightness of a Cut-Set Bound on Network Function Comp...

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IEEE International Symposium on Information Theory

作者： Jie Wang Shenghao Yang Congduan Li School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)9781538641026;9781538647813

The following model of network function computation in directed acyclic networks is considered: A sink node desires to compute correctly a target function with all possible inputs of the function generated at multiple source nodes. The network links have limited capacity and are error-free. The intermediate network nodes perform network coding without any computation bound. The computing rate is measured by the average number of times that the target function can be computed for one use of the network. Guang, Yang and Li recently proposed a general upper bound on the computing capacity that is tight for all the instances of the problem with known computing capacity in literature. In this paper, we show that their upper bound is not tight in general by explicitly characterizing the computing capacity of an example. Our technique can be extended to characterize upper bounds on the computing capacity of a general instance of the network function computing problem.

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Measurement of forward charged hadron flow harmonics in peripheral PbPb collisions at √sNN = 5.02 TeV with the LHCb detector

arXiv

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arXiv 2023年

作者： Aaij, R. Abdelmotteleb, A.S.W. Abellan Beteta, C. Abudinén, F. Ackernley, T. Adeva, B. Adinolfi, M. Adlarson, P. Agapopoulou, C. Aidala, C.A. Ajaltouni, Z. Akar, S. Akiba, K. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Alfonso Albero, A. Aliouche, Z. Alvarez Cartelle, P. Amalric, R. Amato, S. Amey, J.L. Amhis, Y. An, L. Anderlini, L. Andersson, M. Andreianov, A. Andreola, P. Andreotti, M. Andreou, D. Anelli, A.A. Ao, D. Archilli, F. Argenton, M. Arguedas Cuendis, S. Artamonov, A. Artuso, M. Aslanides, E. Atzeni, M. Audurier, B. Bacher, D. Bachiller Perea, I. Bachmann, S. Bachmayer, M. Back, J.J. Bailly-Reyre, A. Baladron Rodriguez, P. Balagura, V. Baldini, W. de Souza Leite, J. Baptista Barbetti, M. Barbosa, I.R. Barlow, R.J. Barsuk, S. Barter, W. Bartolini, M. Baryshnikov, F. Basels, J.M. Bassi, G. Batsukh, B. Battig, A. Bay, A. Beck, A. Becker, M. Bedeschi, F. Bediaga, I.B. Beiter, A. Belin, S. Bellee, V. Belous, K. Belov, I. Belyaev, I. Benane, G. Bencivenni, G. Ben-Haim, E. Berezhnoy, A. Bernet, R. Bernet Andres, S. Bernstein, H.C. Bertella, C. Bertolin, A. Betancourt, C. Betti, F. Bex, J. Bezshyiko, Ia. Bhom, J. Bieker, M.S. Biesuz, N.V. Billoir, P. Biolchini, A. Birch, M. Bishop, F.C.R. Bitadze, A. Bizzeti, A. Blago, M.P. Blake, T. Blanc, F. Blank, J.E. Blusk, S. Bobulska, D. Bocharnikov, V. Boelhauve, J.A. Boente Garcia, O. Boettcher, T. Bohare, A. Boldyrev, A. Bolognani, C.S. Bolzonella, R. Bondar, N. Borgato, F. Borghi, S. Borsato, M. Borsuk, J.T. Bouchiba, S.A. Bowcock, T.J.V. Boyer, A. Bozzi, C. Bradley, M.J. Braun, S. Brea Rodriguez, A. Breer, N. Brodzicka, J. Brossa Gonzalo, A. Brown, J. Brundu, D. Buonaura, A. Buonincontri, L. Burke, A.T. Burr, C. Bursche, A. Butkevich, A. Butter, J.S. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Calabrese, R. Calefice, L. Cali, S. Calvi, M. Calvo Gomez, M. Cambon Bouzas, J. Campana, P. Campora Perez, D.H. Campoverde Quezada, A.F. Capelli, S. Capriotti, L. Caravaca Mora, R.C. Carbone, A. Carcedo Salgado, L. Cardinale, R. School of Physics and Astronomy Monash University Melbourne Australia Rio de Janeiro Brazil Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing China Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China San Jose Costa Rica Université Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Cagliari Monserrato Italy Università degli Studi di Padova Università e INFN Padova Padova Italy INFN Sezione di Perugia Perugia Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma La Sapienza Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics VU University Amsterdam Amsterdam Netherlands AGH - University of Scien

Flow harmonic coefficients, vn, which are the key to studying the hydrodynamics of the quark-gluon plasma (QGP) created in heavy-ion collisions, have been measured in various collision systems and kinematic regions and using various particle species. The study of flow harmonics in a wide pseudorapidity range is particularly valuable to understand the temperature dependence of the shear viscosity to entropy density ratio of the QGP. This paper presents the first LHCb results of the second- and the third-order flow harmonic coefficients of charged hadrons as a function of transverse momentum in the forward region, corresponding to pseudorapidities between 2.0 and 4.9, using the data collected from PbPb collisions in 2018 at a center-of-mass energy of 5.02 TeV. The coefficients measured using the two-particle angular correlation analysis method are smaller than the central-pseudorapidity measurements at ALICE and ATLAS from the same collision system but share similar features. © 2023, CC BY.

关键词： Heavy ions

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Chiral Plasmons with Twisted Atomic Bilayers

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Physical Review Letters 2020年第7期125卷 077401-077401页

作者： Xiao Lin Zifei Liu Tobias Stauber Guillermo Gómez-Santos Fei Gao Hongsheng Chen Baile Zhang Tony Low Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Science and Technology Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Materials Science Factory Instituto de Ciencia de Materiales de Madrid CSIC E-28049 Madrid Spain Institute for Theoretical Physics University of Regensburg D-93040 Regensburg Germany Departamento de Física de la Materia Condensada Instituto Nicolás Cabrera and Condensed Matter Physics Center (IFIMAC) Universidad Autónoma de Madrid E-28049 Madrid Spain International Joint Innovation Center ZJU-UIUC Institute Zhejiang University Haining 314400 China Centre for Disruptive Photonic Technologies NTU Singapore 637371 Singapore Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA

van der Waals heterostructures of atomically thin layers with rotational misalignments, such as twisted bilayer graphene, feature interesting structural moiré superlattices. Because of the quantum coupling between the twisted atomic layers, light-matter interaction is inherently chiral; as such, they provide a promising platform for chiral plasmons in the extreme nanoscale. However, while the interlayer quantum coupling can be significant, its influence on chiral plasmons still remains elusive. Here we present the general solutions from full Maxwell equations of chiral plasmons in twisted atomic bilayers, with the consideration of interlayer quantum coupling. We find twisted atomic bilayers have a direct correspondence to the chiral metasurface, which simultaneously possesses chiral and magnetic surface conductivities, besides the common electric surface conductivity. In other words, the interlayer quantum coupling in twisted van der Waals heterostructures may facilitate the construction of various (e.g., bi-anisotropic) atomically-thin metasurfaces. Moreover, the chiral surface conductivity, determined by the interlayer quantum coupling, determines the existence of chiral plasmons and leads to a unique phase relationship (i.e., ±π/2 phase difference) between their transverse-electric (TE) and transverse-magnetic (TM) wave components. Importantly, such a unique phase relationship for chiral plasmons can be exploited to construct the missing longitudinal spin of plasmons, besides the common transverse spin of plasmons.

关键词： Light-matter interaction Near-field optics Photonics Plasmonics Twist deformation Van der Waals systems

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Redshift gaps in one-dimensional photonic crystals containing hyperbolic metamaterials

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Physical Review Applied 2018年第6期10卷 064022-064022页

作者： Feng Wu Guang Lu Zhiwei Guo Haitao Jiang Chunhua Xue Minjia Zheng Chaoxin Chen Guiqiang Du Hong Chen MOE Key Laboratory of Advanced Micro-Structured Materials School of Physics Science and Engineering Tongji University Shanghai 200092 China School of Space Science and Physics Shandong University at Weihai Weihai 264209 China School of Computer Science & Communication Engineering Guangxi University of Science and Technology Liuzhou Guangxi 545006 China State Key Laboratory of Surface Physics and Department of Physics Key Laboratory of Micro and Nano-Photonic Structures (Ministry of Education) Fudan University Shanghai 200433 China

In conventional all-dielectric one-dimensional photonic crystals (1DPCs), photonic gaps will shift toward short wavelengths (blueshift) as the incident angle increases for both transverse magnetic (TM) and transverse electric (TE) polarizations. We theoretically and experimentally achieve redshift gaps in 1DPCs composed of alternative hyperbolic metamaterials (HMMs) and dielectrics for TM polarization based on the anomalous wavevector dispersion of HMMs. The HMM is mimicked by layered titanium dioxide and silver with a subwavelength unit cell. However, the gaps remain to be blue-shifted for TE polarization. Therefore, the edges of such gaps can be utilized for high-efficiency wide-angle polarization selection. We achieve efficient polarization selection for incident angles ranging from 50° to 80° at the wavelength of 365 nm.

关键词： Band gap Hyperbolic metamaterials Photonic crystals Polarization of light 1-dimensional systems Devices Optical materials & elements Photonic crystal lasers

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Underwater acoustic intensity field reconstruction by kriged compressive sensing 13

Underwater acoustic intensity field reconstruction by kriged...

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13th ACM International Conference on Underwater Networks and systems, WUWNet 2018

作者： Sun, Jie Yu, Jiancheng Zhang, Aiqun Song, Aijun Zhang, Fumin State Key Laboratory of Robotics Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Electrical Engineering University of Alabama TuscaloosaAL35487 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781450361934

This paper presents a novel Kriged Compressive Sensing (KCS) approach for the reconstruction of underwater acoustic intensity fields sampled by multiple gliders following sawtooth sampling patterns. Blank areas in between the sampling trajectories may cause unsatisfying reconstruction results. The KCS method leverages spatial statistical correlation properties of the acoustic intensity field being sampled to improve the compressive reconstruction process. Virtual data samples generated from a kriging method are inserted into the blank areas. We show that by using the virtual samples along with real samples, the acoustic intensity field can be reconstructed with higher accuracy when coherent spatial patterns exist. Corresponding algorithms are developed for both unweighted and weighted KCS methods. By distinguishing the virtual samples from real samples through weighting, the reconstruction results can be further improved. Simulation results show that both algorithms can improve the reconstruction results according to the PSNR and SSIM metrics. The methods are applied to process the ocean ambient noise data collected by the Sea-Wing acoustic gliders in the South China Sea. © 2018 Association for Computing Machinery.

关键词： Kriging

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Observation of new baryons in the Ξb−π+π− and Ξb0π+π− systems

arXiv

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arXiv 2023年

作者： Aaij, R. Abdelmotteleb, A.S.W. Beteta, C. Abellan Abudinén, F. Ackernley, T. Adeva, B. Adinolfi, M. Adlarson, P. Afsharnia, H. Agapopoulou, C. Aidala, C.A. Ajaltouni, Z. Akar, S. Akiba, K. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Albero, A. Alfonso Aliouche, Z. Cartelle, P. Alvarez Amalric, R. Amato, S. Amey, J.L. Amhis, Y. An, L. Anderlini, L. Andersson, M. Andreianov, A. Andreotti, M. Andreou, D. Ao, D. Archilli, F. Artamonov, A. Artuso, M. Aslanides, E. Atzeni, M. Audurier, B. Perea, I. Bachiller Bachmann, S. Bachmayer, M. Back, J.J. Bailly-Reyre, A. Rodriguez, P. Baladron Balagura, V. Baldini, W. de Souza Leite, J. Baptista Barbetti, M. Barbosa, I.R. Barlow, R.J. Barsuk, S. Barter, W. Bartolini, M. Baryshnikov, F. Basels, J.M. Bassi, G. Batsukh, B. Battig, A. Bay, A. Beck, A. Becker, M. Bedeschi, F. Bediaga, I.B. Beiter, A. Belin, S. Bellee, V. Belous, K. Belov, I. Belyaev, I. Benane, G. Bencivenni, G. Ben-Haim, E. Berezhnoy, A. Bernet, R. Andres, S. Bernet Berninghoff, D. Bernstein, H.C. Bertella, C. Bertolin, A. Betancourt, C. Betti, F. Bezshyiko, Ia. Bhom, J. Bian, L. Bieker, M.S. Biesuz, N.V. Billoir, P. Biolchini, A. Birch, M. Bishop, F.C.R. Bitadze, A. Bizzeti, A. Blago, M.P. Blake, T. Blanc, F. Blank, J.E. Blusk, S. Bobulska, D. Bocharnikov, V. Boelhauve, J.A. Garcia, O. Boente Boettcher, T. Boldyrev, A. Bolognani, C.S. Bolzonella, R. Bondar, N. Borgato, F. Borghi, S. Borsato, M. Borsuk, J.T. Bouchiba, S.A. Bowcock, T.J.V. Boyer, A. Bozzi, C. Bradley, M.J. Braun, S. Rodriguez, A. Brea Breer, N. Brodzicka, J. Gonzalo, A. Brossa Brown, J. Brundu, D. Buonaura, A. Buonincontri, L. Burke, A.T. Burr, C. Bursche, A. Butkevich, A. Butter, J.S. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Calabrese, R. Calefice, L. Cali, S. Calvi, M. Gomez, M. Calvo Campana, P. Perez, D.H. Campora Quezada, A.F. Campoverde Capelli, S. Capriotti, L. Carbone, A. Cardinale, R. Cardini, A. Carniti, P. Carus, L. Vidal, A. Casais Caspary, R. Casse, G. Cattaneo, M. Cavallero, G. Rio de Janeiro Brazil Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing China Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China Université Savoie Mont Blanc CNRS IN2P3-LAPP Annecy France Université Clermont Auvergne CNRS IN2P3 LPC Clermont-Ferrand France Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université Paris-Saclay CNRS IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Cagliari Monserrato Italy Università degli Studi di Padova Università e INFN Padova Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma La Sapienza Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics VU University Amsterdam Amsterdam Netherlands AGH - University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Henryk Niewodniczanski Institute of Nuclear Physics Poli

The first observation and study of two new baryonic structures in the final state Ξb0π+π− and the confirmation of the Ξb(6100)− state in the Ξb−π+π− decay mode are reported using proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9 fb−1. In addition, the properties of the known Ξb∗0, Ξb′− and Ξb∗− resonances are measured with improved precision. The new decay mode of the Ξb0 baryon to the Ξc+π−π+π− final state is observed and exploited for the first time in these measurements. © 2023, CC BY.

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LEARNING FROM WEAKLY-LABELED CLINICAL DATA FOR AUTOMATIC THYROID NODULE CLASSIFICATION IN ULTRASOUND IMAGES

LEARNING FROM WEAKLY-LABELED CLINICAL DATA FOR AUTOMATIC THY...

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IEEE International Conference on Image Processing

作者： Jianxiong Wang Shuai Li Wenfeng Song Hong Qin Bo Zhang Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science Stony Brook University Stony Brook USA Department of Ultrasound Chinese Academy of Medical Science & Peking Union Medical College Hospital Beijing China

This paper proposes a semi-supervised learning method based on weakly-labeled data to automatically classify ultrasound (US) thyroid nodules. key to our new approach is the unification of multi-instance learning (MIL) with deep learning. Benefiting from that, our method can directly use off-the-shelf clinical data, which involves no labels to indicate nodule classes. To this end, we take the US images of a patient as a bag, and take the corresponding pathology report as the bag label. Specifically, we first propose a bag generating method, wherein the detected thyroid nodules are considered as instances corresponding to certain bag. After that, we design an effective EM algorithm to train a convolutional neural network (CNN) for nodule classification. We conduct extensive experiments and comprehensive evaluations on different datasets, and all the experiments confirm that, our method significantly outperforms state-of-the-art MIL algorithms, which exhibits great potential in clinical applications.

关键词： Cancer Proposals Image edge detection Training Biomedical imaging Feature extraction Ultrasonic imaging

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Resource Efficiency Optimization Engine in Smart Production Networks via intelligent Cloud Manufacturing Platforms

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Procedia CIRP 2018年 78卷 19-24页

作者： Alessandro Simeone Alessandra Caggiano Bin Deng Yunfeng Zeng Lev Boun Intelligent Manufacturing Key Laboratory of Ministry of Education Shantou University China Department of Industrial Engineering University of Naples Federico II Italy Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT UniNaples) Italy Dynamic Computer Systems PoB 1341 30300 Atlit Israel

The aim of this paper is to develop an optimization engine to be implemented in a cloud manufacturing platform to promote resource efficiency in sharing of manufacturing services related to sheet metal cutting. The optimization engine allows to properly select the manufacturing service requests collected through the cloud platform, analyse the possible pairings with the supplier ongoing production orders and dynamically choose the best production strategy (e.g. incorporate, queue, prioritize or reject) considering the surface utilization rate of the metal sheets as key performance index. A simulation of different possible scenarios in terms of customer and supplier orders is reported to exemplify the diverse decision-making scheduling strategies proposed by the platform and the related quantification of resource efficiency improvement.

关键词： Cloud Manufacturing Industry 4.0 Resource Efficiency Smart Manufacturing Network

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LaG-DESIQUE: A local-and-global blind image quality evaluator without training on human opinion scores 6th

LaG-DESIQUE: A local-and-global blind image quality evaluato...

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6th CCF Academic Conference on Big Data, CCF Big Data 2018

作者： Liu, Ruyi Zhang, Yi Chandler, Damon M. Miao, Qiguang Liu, Tiange School of Computer Science and Technology Xidian University XianShaanxi710071 China Xian Key Laboratory of Big Data and Intelligent Vision XianShaanxi710071 China School of Electronic and Information Engineering Xian Jiaotong University XianShaanxi710049 China Department of Electrical and Electronic Engineering Shizuoka University HamamatsuShizuoka432-8561 Japan School of Information Science and Engingeering Yanshan University QinhuangdaoHebei066004 China

ISBN: (纸本)9789811329210

This paper extends our previous DESIQUE [1] algorithm to a local-and-global way (LaG-DESIQUE) to blindly measure image quality without training on human opinion scores. The local DESIQUE extracts block-based log-derivative features and evaluates image quality through measuring the multivariate Gaussian distance between selected natural and test image patches. The global DESIQUE extracts image-based log-derivative features and image quality is estimated based on a two-stage framework, which was trained on a set of regenerated distorted images with their quality scores estimated by MAD [2] algorithm. The overall quality is the weighted average of local and global DESIQUE scores. Test on several image databases demonstrates that LaG-DESIQUE performs competitively well in predicting image quality. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Image quality

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