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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Ke Gu Guangtao Zhai Xiaokang Yang Wenjun Zhang Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processingand Transmissions

During the last two decades, image quality assessment has been a major research area, which considerably helps to promote the development of image processing. Following the tremendous success of Structural SIMilarity (SSIM) index in terms of the correlation between the quality predictions and the subjective scores, many improved algorithms have been further exploited, such as Multi-Scale SSIM (MS-SSIM) and Information content Weighted SSIM (IW-SSIM). However, a growing number of researchers have been devoted to the study of the effects of uneven responses to different image distortion categories on prediction accuracy of the quality metrics. Inspired by this, we propose an improved full-reference image quality assessment paradigm based on structure compensation. Experimental results on laboratory for image and Video Engineering (LIVE) database and Tampere image Database 2008 (TID2008) are provided to confirm our introduced approach has superior prediction performance as compared to mainstream image quality metrics. Besides, it is worth emphasizing that our algorithm not introduces other operators but only applies the SSIM function to compensate itself, and furthermore, it also has an effective capability of image distortion classification.

关键词： Nonlinear distortion Measurement image quality Transform coding Indexes Prediction algorithms

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Design and Implementation of Embedded Multimedia Surveillance System

Design and Implementation of Embedded Multimedia Surveillanc...

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International Workshop on Knowledge Discovery and Data Mining (WKDD)

作者： Yan Liu Ren-Fa Li Cheng Xu Fei Yu School of Computer and Communication Hunan University Changsha China Guangdong Province Key Laboratory of Electronic Commerce Market Application Technology Guangdong University of Business Studies Guangzhou China Provincial Key Laboratory of Image Processing & Image Communication Nanjing University of Posts and Telecommunications Nanjing China

Due to the thrive of networking multimedia, artificial intelligence and embedded system, video surveillance system is evolved from computer-based to embedded-based. This paper presents an intelligent multimedia surveillance system based on embedded video server. Design and implementation issues of this surveillance system are discussed including system architecture, hardware design and software framework. Moreover, practical applications show that our framework can suitable for commercial applications.

关键词： Multimedia systems Artificial intelligence Application software Embedded system Video surveillance Computer networks Embedded computing Intelligent systems Network servers Computer architecture

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Stereoscopic images quality assessment based on deep learning

Stereoscopic images quality assessment based on deep learnin...

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IEEE Visual communications and image processing (VCIP)

作者： Kai Wang Jun Zhou Ning Liu Xiao Gu Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781509053179

With the popularity of stereoscopic 3D (S3D) images and videos, many advanced objective quality assessment methods have been proposed to evaluate viewers' Quality of Experience (QoE). Among them, most algorithms take advantages of the disparity maps to extract useful features. On the other hand, deep learning has been one of the hottest research topics during these years, but limited efforts focused on the field in objective quality evaluation of S3D images. In this paper, we propose a S3D image quality assessment (S3D IQA) method based on deep learning. In this method, the Convolutional Restricted Boltzmann Machines (CRBM) combined with Factored Third-Order RBM (FTO-RBM) is considered as learning model to extract feature maps from pre-processed left and right images automatically. Then an improved traversal algorithm based on two pooling strategies is put forward to optimize extracted feature maps, which improves the final quality assessment performance significantly. Experimental results show that our S3D IQA method achieves good performance on 3D databases tested.

关键词： Feature extraction Training Databases Machine learning image quality Three-dimensional displays Data preprocessing

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Online video synopsis method through simple tube projection strategy

Online video synopsis method through simple tube projection ...

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International Conference on Wireless communications and Signal processing (WCSP)

作者： Jing Jin Feng Liu Zongliang Gan Ziguan Cui Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781509028610

Traditional video synopsis methods model the processing into an optimization formula where relations among objects such as collision cost are utilized while entire re-calculation is introduced under each possible temporal shift. Unlike the pairwise cost optimization, we propose a low-complexity and efficient online synopsis method where each tube is processed independently. Without tubes' comparison, the rearrangement is accomplished by a simple projection strategy and an updating projection matrix which records the newest information of the moving space. Furthermore, buffer and a predefined fitness condition also help to increase spatial and temporal utilization. Experiment results demonstrate that the proposed method is superior to other synopsis methods in the processing speed and temporal consistency.

关键词： Streaming media Electron tubes Trajectory Optimization Surveillance Telecommunications Cameras

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Spatial-Temporal Constrained Pseudo-labeling for Unsupervised Person Re-identification via GCN Inference 18th

Spatial-Temporal Constrained Pseudo-labeling for Unsupervis...

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18th International Forum of Digital Multimedia communication, IFTC 2021

作者： Ling, Sen Yang, Hua Liu, Chuang Chen, Lin Zhao, Hongtian The Institute of Image Communication and Network Engineering Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789811922657

Most existing unsupervised person re-identification (Re-ID) methods primarily depend on the cluster distance, and merely exploit the available source labeled data to assign pseudo labels for the unannotated data. Whereas, the cluster distance usually fails to adapt to different datasets due to the domain gap. Besides, learning exclusively from the source data can not generate accurate pseudo labels for the lack of the target data information. To address this problem, we propose to exploit the spatial-temporal constraints to facilitate the pseudo label generation process. Specifically, graphs for the labeled source data are constructed and the graph convolution network (GCN) is used to learn graph embeddings. Based on these graph embeddings, the likelihood of linkages between graph nodes is estimated and utilized to assign pseudo labels for the unlabeled data. Then, with the pseudo labels, a smoothed spatial-temporal probability distribution model is generated to amend the likelihood of linkages between graph nodes as well as correct the visual similarity scores for person Re-ID. Finally, we optimize the pseudo label assignment, feature extraction networks, and spatial-temporal model alternatively and iteratively to improve the person Re-ID performance. Comprehensive experiments demonstrate that the proposed method outperforms state-of-the-art methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Graph theory

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Enhancement of color image based on tone-preserving

Enhancement of color image based on tone-preserving

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International Conference on Wireless communications and Signal processing (WCSP)

作者： Ya'nan Yang Xiaofan Wang Feng Liu Zongliang Gan Jiangsu Province Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781509028610

Since the lighting conditions in strong contrast regions between the light and dark cant be estimated accurately by traditional center/surround Retinex algorithm, the over-enhancement and color distortion may exist. In view of this, combining with the human visual characteristics, a color image enhancement algorithm based on tone-preserving was proposed. A determination function was added to the bilateral filter to estimate illuminance image more accurately and weaken over-enhancement. According to human visual masking effect, the improved gamma correction was utilized to correct the brightness of illumination image adaptively and the local contrast of reflection image obtained by division was enhanced based on local statistics. Besides, the final enhanced image was obtained by combining illumination image with reflection image, which can make image appear more natural. Compared with other similar algorithms from both subjective and objective aspects, the results show that this method being applied to low-contrast color image enhancement can not only improve image clarity, but reduce color distortion.

关键词： image color analysis Brightness Lighting Color Visualization Nonlinear distortion

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An Improved Frame-Layer Bit Allocation Scheme for H.264/AVC Rate Control

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Journal of Shanghai Jiaotong university(Science) 2009年第2期14卷 143-148页

作者：林贵旭郑世宝朱良家 Institute of Image Communication & Information Processing Department of Electronic EngineeringShanghai Jiaotong University Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiaotong University Department of Automatic Control National University of Defense Technology

In this paper,we aim at improving the video quality degradation due to high motions or scene changes. An improved frame-layer bit allocation scheme for H.264/AVC rate control is ***,current frame is pre-encoded in 16×16 modes with a fixed quantization parameter(QP).The frame coding complexity is then measured based on the resulting bits and peak signal-to-ratio(PSNR) in the pre-coding ***,a bit budget is calculated for current frame according to its coding complexity and inter-frame PSNR fluctuation,combined with the buffer *** results show that,in comparison with the H.264 adopted rate control scheme, our method is more efficient to suppress the sharp PSNR drops caused by high motions and scene *** visual quality variations in a sequence are also relieved.

关键词： H.264/AVC rate control bit allocation quality degradation

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未来视频技术专题(英文)

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China communications 2013年第5期 8-10页

作者：陈熙霖朱秀昌王桥胡栋 Ming-Ting SUN the Institute of Computing Technology Chinese Academy of Sciences (CAS) the IEEE Transactions on Image Processing the Journal of Computer Science and Technology the Chinese Journal of Computers College of Communications and Information Engineering NUPT Image Processing and Image Communications Key Laboratory of Information Industrial Ministry and Jiangsu Province Signal and Information Processing Laboratory Southeast University

In the past few years, video has become one of the most powerful engines to push communications forward. The increasing of digital video technology requires larger and larger bandwidth for various applications, such as IPTV, VOD, video phone, mobile search, etc. The goal of this Feature Topic column is to present and highlight the latest progress and future video-related technologies. We hope this

关键词： IEEE over

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Spatial Error Concealment with Adaptive Linear Predictor

Spatial Error Concealment with Adaptive Linear Predictor

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IEEE International Symposium on Circuits and Systems

作者： Jing Liu Guangtao Zhai Xiaokang Yang Bing Yang Institute of Image Communication and Network Engineering Shanghai Key Laboratory of Digital Media Processing and Transmissions Shanghai Jiaotong University Shanghai 200240 China

ISBN: (纸本)9781479934331

In this paper, we propose a novel spatial error concealment algorithm based on adaptive linear predictor. The predictor automatically tunes its order and support shape according to local contexts. The optimal order is determined using Bayesian Index Criterion (BIC). The order-adaptive predictor is able to recover more important features or structures. Simulation results show that compared to the state-of-the-art error concealment algorithms, the proposed method gives better reconstruction performance in terms of objective and subjective evaluations.

关键词： Data Security linear predictor Self tuning subjective evaluation Simulation results

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Enhancing Security in Smart Homes with IoT Using Logit-Boosted Techniques 6

Enhancing Security in Smart Homes with IoT Using Logit-Boost...

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6th International Conference on Computer Information Science and Application Technology, CISAT 2023

作者： Rahim, Asif Zhong, Yanru Ahmad, Tariq School of Computer and Information Security Guilin university of Electronic Technology Guilin54001 China Guangxi Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin541001 China School of Information and Communication Engineering Guilin University of Electronic Technology Guilin541001 China

ISBN: (纸本)9781510668546

The utilization and advancement of Internet of Things (IoT) gadgets have noticeably increased in recent times. Various sectors, including intelligent homes, healthcare, sports analysis, and different industries, use IoT devices. Traffic is a crucial aspect of IoT devices, and it differs from conventional device traffic in several ways. This investigation employed 41 Internet of Things (IoT) devices. A machine learning model was developed that can classify IoT devices into multiple categories based on 13 different network traffic features. The raw data that was gathered was preprocessed using techniques like standardization and scaling techniques of the dataset. Algorithms for feature selecting can be utilized to extract characteristics from textual data. Following stratification, The dataset contains 106,234 sets of features that were utilized to enhance the predictive model. This research employed various evaluation metrics to demonstrate the effectiveness of the logistic-boosted algorithms. In this research, collaborative machine learning methods were utilized as a starting point to develop an intrusion detection system (IDS) that can identify abnormal network traffic. The principal aim of this investigation is to identify attacks and abnormalities within a Smart Home IoT setting. We have introduced a new technique for constructing Logit-Boosted algorithm, namely Logi-CatBoost Classifier. Once the logitboosted algorithm was employed to classify the dataset, Logi-CBC performed the best, with an accuracy of 88.70%. We suggested Logi-CBC algorithm achieved the top precision among previously used Logit-Boosted algorithms in past studies on the same dataset. © 2023 SPIE.

关键词： Internet of things

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