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Enhancing Privacy-Utility Tradeoff with Few-Round Strategy in Heterogeneous Federated Learning

Enhancing Privacy-Utility Tradeoff with Few-Round Strategy i...

引用

IEEE visual communications and image processing (VCIP)

作者： Qingbin Wei Feilong Zhang Yuanchao Bai Deming Zhai Junjun Jiang Xianming Liu Faculty of Computing Harbin Institute of Technology

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Federated learning inherently provides a certain level of privacy protection, which however is often inadequate in many real-world scenarios. Existing privacy-preserving methods frequently incur unbearable time overheads or result in non-negligible deterioration to model performance, thus suffering from the tradeoff between performance and privacy. In this work, we propose a novel Federated Privacy-Preserving Knowledge Transfer framework, namely FedPPKT, which employs data-free knowledge distillation in a meta-learning manner to rapidly generates pseudo data and performs privacy-preserving knowledge transfer. FedPPKT establishes a protective barrier between the original private data and the federated model, thereby ensuring user privacy. Furthermore, leveraging the few-round strategy of FedPPKT, it has the capability to reduce the number of communication rounds, further mitigating the risk of privacy exposure for user data. With the help of the meta generator, the problem of uneven local label distribution on clients is alleviated, mitigating data heterogeneity and improving model performance. Experiments show that FedPPKT outperforms the state-of-the-art privacy-preserving federated learning methods. Our code is publicly available at https://***/HIT-weiqb/FedPPKT.

关键词： Data privacy Privacy image coding Federated learning visual communication Generators Data models Servers Protection Knowledge transfer

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Bird’s-Eye-View-Based LiDAR Point Cloud Coding For Machines

Bird’s-Eye-View-Based LiDAR Point Cloud Coding For Machines

引用

IEEE visual communications and image processing (VCIP)

作者： Xiang Gao Zhiwei Zhu Lu Yu Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) Hangzhou China

Recently, there has been a growing interest in image/video coding tailored for intelligent analysis tasks, such as image/Video Coding for Machines (ICM/VCM). These approaches have shown remarkable results when compared to human perception-based coding methods. However, point cloud coding methods for machine intelligence have not been extensively studied. Inspired by current LiDAR point cloud intelligent analysis methods which convert point cloud into bird’s-eye-view (BEV) perspective, we propose an end-to-end learnt point cloud coding framework for 3D machine intelligent tasks with BEV representation, named PC4M. Specifically, the PC4M system consists of a LiDAR encoder, a learnt BEV feature codec and a BEV region proposal network with a task-specific head. To achieve better rate-distortion performance for analysis tasks, we propose an efficient Res-NeXt fusion block with powerful multi-scale modeling ability to compress sparse BEV features, and design a long-distance adaptive attention module by using VanAtten block. Experimental results demonstrate that our method outperforms the state-of-the-art MPEG standard Geometry-based Point Cloud Coding (G-PCC) on the object detection and BEV map segmentation by 83.12% and 85.32% of BD-rate gain on nuScenes, respectively. To the best of our knowledge, this is the first end-to-end learnt task-oriented point cloud codec.

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Uniformly Accelerated Motion Model for Inter Prediction

Uniformly Accelerated Motion Model for Inter Prediction

引用

IEEE visual communications and image processing (VCIP)

作者： Zhuoyuan Li Yao Li Chuanbo Tang Li Li Dong Liu Feng Wu University of Science and Technology of China (USTC) Hefei China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Inter prediction is a key technology in video coding to reduce the temporal redundancy. In natural videos, there are usually moving objects with changing velocity, resulting in complex motion fields that are difficult to represent compactly. In Versatile Video Coding (VVC), existing inter prediction methods usually assume uniform speed motion between consecutive frames, which may not well handle the complex motion fields in the real world. To address these issues, we introduce a uniformly accelerated motion model (UAMM) to exploit velocity and acceleration of moving objects between the video frames, and further combine them to assist in the inter prediction methods to handle the motion change in the temporal domain. First, we review the theory of UAMM. Second, we propose UAMM-based parameter derivation and extrapolation schemes in the coding process. Third, we integrate the UAMM into existing inter prediction modes (Merge, MMVD, CIIP) to achieve higher prediction accuracy. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 0.38% BD-rate reduction compared to the VTM anchor, under the Low-delay P configuration, with a slight increase of time complexity on the encoding/decoding side.

关键词： Video coding Accuracy image coding visual communication Reviews Motion estimation Redundancy Predictive models Software Time complexity

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Second International conference on Digital Signal and Computer communications, DSCC 2022

Second International Conference on Digital Signal and Comput...

引用

2nd International conference on Digital Signal and Computer communications, DSCC 2022

ISBN: (纸本)9781510656734

The proceedings contain 72 papers. The topics discussed include: direction finding for mixed independent and correlated signals in impulse noise;design and implementation of 5G network user interface function selection method based on knowledge map;research on scheduling strategy for shockwave-field wireless collection nodes;medical image encryption based on hyper-chaotic system;research and implementation of land use and cover change monitoring methods;multi-task offloading in mobile edge computing networks;design of real-time acquisition and visual monitoring system of human breathing signal;research on ultrasonic-based long-distance and sub-millimeter real-time ranging system;design of air electromagnetic combat environment simulation system;design of a 4-channel high-reliability video processing module;and design and implementation of multi-source multi-standard audio processing system.

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A Pose Estimation Method in Dynamic Scene with Yolov5, Mask R-CNN and ORB-SLAM2 7

A Pose Estimation Method in Dynamic Scene with Yolov5, Mask ...

引用

7th International conference on Signal and image processing, ICSIP 2022

作者： Zhu, Junchao Jia, Yongxing Shen, Weixiang Qian, Xiaojian Army Engineering University of PLA College of Communications Engineering Nanjing China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Most visual SLAM systems exhibit strong robustness in static scenes. When there are dynamic objects in the scene, these systems tend to treat dynamic objects as static backgrounds for pose estimation, which reduces the accuracy of pose estimation. Based on Yolov5 and Mask R-CNN, this paper proposes a method to eliminate dynamic objects for estimating poses, improving the performance of SLAM system for localization and mapping in dynamic scene. Firstly, Yolov5 is used for the preliminary detection of dynamic objects, the purpose of this step is to obtain the anchor boxes of dynamic objects. Then, these anchor boxes are used as the input of Mask R-CNN to obtain the mask information of dynamic objects. Finally, the results of feature extraction is combined with the mask information to eliminate the point features in the regions which are marked as dynamic objects. The remaining point features are used as input for accurate pose estimation. The method is tested on standard KITTI datasets. Experimental results show that the accuracy of pose estimation with this method is higher than that of the original ORB-SLAM2. © 2022 IEEE.

关键词： Object detection

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End-to-end Prediction of Streaming Video Quality of Experience: Dataset and Approach

End-to-end Prediction of Streaming Video Quality of Experien...

引用

IEEE visual communications and image processing (VCIP)

作者： Ziheng Jia Xiongkuo Min Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

With the rapid development of video-on-demand (VOD) and real-time streaming video technologies, the accurate objective assessment of streaming video Quality of Experience (QoE) has become a focal point for optimizing streaming-related technologies. However, due to the inherent transmission distortions caused by poor Quality of Service (QoS) conditions in streaming videos, such as intermittent stalling, rebuffering, and drastic changes in video sharpness due to bitrate fluctuations, evaluating streaming video QoE presents numerous challenges. This paper introduces a large and diverse in-the-wild streaming video QoE evaluation dataset - the SJLIVE-1k dataset. This work addresses the limitations of corresponding datasets, which lack in-the-wild video sequences under real network conditions and whose amount of video content is insufficient. Furthermore, we propose an end-to-end objective QoE evaluation strategy that extracts video content and QoS features from the video itself without using any extra information. By implementing self-supervised contrastive learning as the "reminder" to bridge the gap between the different types of features, our approach achieves state-of-the-art results across three datasets. Our proposed dataset will be released to facilitate further research.

关键词： Adaptation models visual communication Video sequences Quality of service Contrastive learning Streaming media Feature extraction Quality assessment Quality of experience Video recording

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Predicting total time to compress a video corpus using online inference systems

Predicting total time to compress a video corpus using onlin...

引用

IEEE visual communications and image processing (VCIP)

作者： Xin Shu Vibhoothi Vibhoothi Anil Kokaram Electronic and Electrical Engineering Sigmedia Group Trinity College Dublin Dublin Ireland

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Predicting the computational cost of compressing/transcoding clips in a video corpus is important for resource management of cloud services and VOD (Video On Demand) providers. Currently, customers of cloud video services are unaware of the cost of transcoding their files until the task is completed. Previous work concentrated on predicting perclip compression time, and thus estimating the cost of video compression. In this work, we propose new Machine Learning (ML) systems which predict cost for the entire corpus instead. This is a more appropriate goal since users are not interested in per-clip cost but instead the cost for the whole corpus. In this work, we evaluate our systems with respect to two video codecs (x264, x265) and a novel high-quality video corpus. We find that the accuracy of aggregate time prediction for a video corpus is more than two times better than using per-clip predictions. Furthermore, we present an online inference framework in which we update the ML models as files are processed. A consideration of video compute overhead and appropriate choice of ML predictor for each fraction of corpus completed yields a prediction error of less than 5%. This is approximately two times better than previous work which proposed generalised predictors.

关键词： Accuracy Costs image coding Video on demand visual communication Computational modeling Machine learning Predictive models Video compression Video codecs

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Low-light image Enhancement Using Attention Mechanisms on Edge-Connect Architecture

Low-light Image Enhancement Using Attention Mechanisms on Ed...

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IEEE Signal processing and communications Applications (SIU)

作者： Muhammet Nuri Dudak Büşra Söylemez Serdar Çiftçi Bilgisayar Mühendisliği Bölümü Harran Üniversitesi Şanlıurfa Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

images captured in low-light conditions often suffer from various issues such as noise, insufficient brightness, loss of sharpness, lack of detail, and color distortion. Although many efforts have been made to overcome these problems, the desired results have not yet been achieved. In this study, we investigated the effectiveness of enhancing low-light images by feeding the Edge-Connect architecture, which has shown successful results in the in-painting problem, with additional edge information, followed by using residual attention mechanisms in the intermediate layers. The experiments show that the best performance is achieved by using a Convolutional Block Attention Module in the residual layers. Compared to state-of-the-art methods, the proposed method improves the PSNR, SSIM, and LPIPS metrics, and the visual results are closer to the ground truth.

关键词： Measurement visualization Attention mechanisms image color analysis Convolution image edge detection Brightness

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Deep Reference Frame for Versatile Video Coding with Structural Re-parameterization

Deep Reference Frame for Versatile Video Coding with Structu...

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IEEE visual communications and image processing (VCIP)

作者： Chengzhuo Gui Yuantong Zhang Weijie Bao Zhenzhong Chen Huairui Wang Shan Liu Hubei Luojia Laboratory Wuhan University Wuhan China School of Geodesy and Geomatics Wuhan University Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Shenzhen China Tencent America Palo Alto CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In video coding, inter-prediction leverages neigh-boring frames to reduce temporal redundancy. The quality of these reference frames is essential for effective inter-prediction. Although many neural network-based methods have been proposed to improve the quality of reference frames, there is still room for the performance and efficiency trade-off. In this paper, we propose an interpolation diverse branch block (InterDBB) suitable for lightweight frame interpolation networks, which optimizes deep reference frame interpolation networks to improve performance without sacrificing speed and increasing complexity. Specifically, we propose a multi-branch structural reparameterization block without batch normalization. This straightforward yet effective modification ensures training stability and performance improvement. Moreover, we propose a parameterized motion estimation strategy based on different input resolution, to achieve a better trade-off between performance and computational complexity. Experimental results demonstrate that our method achieves -2.01%/-2.87%/-2.44% coding efficiency improvements for Y/U/V components under random access (RA) configuration compared to VTM-11.0_NNVC-5.0.

关键词： Video coding Training Interpolation image coding image resolution visual communication Motion estimation Redundancy Computational complexity Batch normalization

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Frame Level Content Adaptive λ for Neural Video Compression

Frame Level Content Adaptive λ for Neural Video Compression

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IEEE visual communications and image processing (VCIP)

作者： Zhirui Zuo Junqi Liao Xiaomin Song Zheng Liu Huiming Zheng Dong Liu University of Science and Technology of China Sichuan Xinshi Chuangwei Ultra HD Technology Co. Ltd

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Neural video compression (NVC) methods have made significant advances in recent years. In most NVC methods, all frames share the same Rate-Distortion trade-off parameter λ, which might be sub-optimal. Recently, inspired by traditional video codecs' hierarchical quality structure, DCVC-DC proposed allocating periodic weights to λ to equip NVC with the hierarchical quality structure. However, the inspiration from traditional video codecs is designed to complement their complex reference structure. Compared to traditional video codecs, NVC methods' reference structure is much simpler and may not require large fluctuations in their hierarchical quality structure. Moreover, DCVC-DC's fixed hierarchical quality structure ignored the influence of video content. We conduct an elaborate study on the hierarchical quality structure in DCVC-DC, shedding light on the potential for improving compression performance by proposing a content adaptive λ to achieve a more reasonable hierarchical quality structure based on the fixed hierarchical weights. Experimental results demonstrate that the proposed method achieves a better rate-distortion performance than allocating the fixed weights to the fixed λ. On DCVC-DC and DCVC-SDD, we achieved 4.9% and 8.8% bdrate reduction with our method.

关键词： Training image coding Fluctuations visual communication Rate-distortion Video compression Decoding Video codecs Computational complexity Periodic structures

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