检索结果-内蒙古大学图书馆

2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Kapoor, Ayshika Kumar, Dheeraj Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee India

ISBN: (纸本)9798350368741

Federated learning (FL) is a decentralized framework for collaborative model training while preserving data privacy. However, high communication overhead between clients and central server hinders FL scalability. This paper introduces K-HashFed, a novel gradient compression approach that integrates K-means clustering and locality-sensitive hashing (LSH) to minimize communication costs while preserving model accuracy. The proposed method clusters gradient updates and uses LSH to further merge similar centroids, to significantly reduce data transmission. Experiments conducted on benchmark datasets such as MNIST, Fashion MNIST, CIFAR-10, and CIFAR-100, demonstrates a significant reduction in data transmission-by orders of magnitude-compared to state-of-the-art methods, with minimal accuracy loss. The results validate K-HashFed as a communication-efficient, scalable solution for real-world FL systems. © 2025 ieee.

关键词： communication efficiency Federated learning hashing model compression weight clustering

来源：评论

学校读者我要写书评

暂无评论

Quantum Run-length Encoding: Optimizing data compression on Quantum Computers with Exponential Resource Efficiency

Quantum Run-length Encoding: Optimizing Data Compression on ...

引用

2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Jiale Che, Xilong Jin, Shiyong Pan, Kaifan Peng, Shun Hu, Juncheng College of Computer Science and Technology Jilin University Changchun China Computer Network Information Center University of Chinese Academy of Science Beijing China

ISBN: (纸本)9798350368741

Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE's application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM's superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware. © 2025 ieee.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud compression Framework

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compressio...

引用

2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, ZhaoYi Han, Dong Song, Cao Nan, FangZhe Yang, Bailin School of Computer Science and Technolgy Zhejiang Gongshang University China School of Statistics and Mathematics Zhejiang Gongshang University China Economic Forecasting and Policy Simulation Laboratory Zhejiang Gongshang University China Collaborative Innovation Center of Statistical Data Engineering Technology & Application Zhejiang Gongshang University China

ISBN: (纸本)9798350368741

The increasing data volume and the demand for real-time transmission highlight the necessity for efficient compression of dynamic point cloud data. Existing methods primarily focus on reducing inter-frame redundancy by calculating per-point motion information, overlooking the computational and storage costs involved. In this paper, we propose a novel motion proxy-based dynamic point cloud compression framework to enhance the efficiency and accuracy of motion information utilization. Specifically, we introduce a feature proxy module to adaptively locate proxy points, which represent the overall motion through the motion of proxy points. Additionally, a motion enhancement module is employed to refine motion details and prevent local information loss caused by dense motion trajectories. Extensive experiments demonstrate the superiority of our approach. Compared with baseline methods, it achieves an average BD-rate improvement of 12.07% (D1) and 11.90% (D2). © 2025 ieee.

关键词： compression deep learning neural network point cloud

来源：评论

学校读者我要写书评

暂无评论

LNeRV: Learnable Hierarchical Encoding Improve Neural Representation Video Codec

LNeRV: Learnable Hierarchical Encoding Improve Neural Repres...

引用

2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Jiahong Liu, Xiang Chen, Bin An, Baoyi Tao, Dai Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University China Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory China Shenzhen University China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9798350368741

Existing Implicit Neural Representation (INR) video compression techniques have opened up new avenues in the field of video compression. NeRV maps the temporal coordinates to high-resolution images using neural networks, providing a more flexible and efficient encoding method for video data. However, NeRV implicitly stores all video information in the network, requiring post network compression techniques such as pruning. To integrate explicit compression and implicit representation into an end-to-end framework, this study proposes a novel neural representation-based video compression paradigm called Latent code based Neural representation video compression (LNeRV). Specifically, LNeRV consists of hierarchy feature grids, synthesis network and entropy coding network. With a single-stage training process, LNeRV achieves video compression and dynamically allocates bits according to video complexity, better fitting dynamic videos. We provide a comprehensive compression-to-decompression workflow for our approach. Extensive experimental results verify the effectiveness of our LNeRV. © 2025 ieee.

关键词： Implicit Neural Representation (INR) Video compression

来源：评论

学校读者我要写书评

暂无评论

FMD: Comprehensive data compression in Medical Domain via Fused Matching Distillation

FMD: Comprehensive Data Compression in Medical Domain via Fu...

引用

2025 ieee/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Son, Ju Heon Choi, Jang-Hwan Ewha Womans University Korea Republic of

ISBN: (纸本)9798331510831

Medical datasets are often large and contain sensitive information, presenting significant challenges for data sharing and storage. To address these issues, this paper introduces a novel method called Fused Matching Distillation (FMD), which combines multiple dataset distillation techniques to achieve both data compression and enhanced privacy. FMD synthesizes representative subsets that capture the essential information from the original dataset while anonymizing sensitive details during the distillation process. The proposed approach integrates two complementary methods: parameter matching,a technique that aligns the training trajectories of a teacher network trained on real data with those of a student network trained on synthetic data, and feature distribution matching, which ensures that the synthetic dataset closely approximates the feature distribution of the original data. By fusing these techniques, FMD maximizes the information density within each pixel of the distilled dataset, achieving a balance between compression and performance. Experimental evaluations on medical datasets, including COVID chest X-ray and Pancreas cancer CT, demonstrate that FMD achieves superior accuracy and privacy compared to existing methods. Furthermore, the proposed method is evaluated using metrics for both model performance and anonymity, showing that FMD not only maintains high diagnostic accuracy but also effectively anonymizes the data. This makes FMD a promising tool for secure and efficient medical data sharing. The code is available at the provided link11https://***/juheonewha/***. © 2025 ieee.

关键词： Anonymity

来源：评论

学校读者我要写书评

暂无评论

All-in-One Image compression and Restoration

All-in-One Image Compression and Restoration

引用

2025 ieee/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Zeng, Huimin Li, Jiacheng Zheng, Ziqiang Xiong, Zhiwei University of Science and Technology of China China The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798331510831

Visual images corrupted by various types and levels of degradations are commonly encountered in practical image compression. However, most existing image compression methods are tailored for clean images, therefore struggling to achieve satisfying results on these images. Joint compression and restoration methods typically focus on a single type of degradation and fail to address a variety of degradations in practice. To this end, we propose a unified framework for all-in-one image compression and restoration, which incorporates the image restoration capability against various degradations into the process of image compression. The key challenges involve distinguishing authentic image content from degradations, and flexibly eliminating various degradations without prior knowledge. Specifically, the proposed framework approaches these challenges from two perspectives: i.e., content information aggregation, and degradation representation aggregation. Extensive experiments demonstrate the following merits of our model: 1) superior rate-distortion (RD) performance on various degraded inputs while preserving the performance on clean data;2) strong generalization ability to real-world and unseen scenarios;3) higher computing efficiency over compared methods. Our code is available at https://***/ZeldaM1/All-in-one. © 2025 ieee.

关键词： Image compression

来源：评论

学校读者我要写书评

暂无评论

Accelerating Deep Neural Networks with a Low-Cost Lossless compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

引用

2025 International conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seongwook Byeon, Gwangeun Lee, Seungeui Bae, Youngseok Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Advanced R&D Center Hanwha Systems Co. Ltd. Korea Republic of Agency for Defense Development Emerging Science and Technology Directorate Korea Republic of

ISBN: (纸本)9798331510756

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value compression (ZVC) and Base-Delta-Immediate compression (BDI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement, © 2025 ieee.

关键词： data handling

来源：评论

学校读者我要写书评

暂无评论

RT3DHVC: A Real-Time Human Holographic Video Conferencing System With a Consumer RGB-D Camera Array

引用

ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第5期35卷 4226-4241页

作者： Jiang, Yuqi Li, Jing Dai, Yanran Qin, Haidong Zhou, Xiaoshi Zhang, Yong Liu, Hongwei Yan, Kefan Yang, Tao Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Northwestern Polytech Univ Sch Comp Sci Natl Engn Lab Integrated Aerosp Ground Ocean Big D SAIIP Xian 710129 Peoples R China

In this paper, we present an end-to-end holographic video conferencing system that enables real-time high-quality free-viewpoint rendering of participants in different spatial regions, placing them in a unified virtual space for a more immersive display. Our system offers a cost-effective, complete holographic conferencing process, including multiview 3D data capture, RGB-D stream compression and transmission, high-quality rendering, and immersive display. It employs a sparse set of commodity RGB-D cameras that capture 3D geometric and textural information. We then remotely transmit color and depth maps via standard video encoding and transmission protocols. We propose a GPU-parallelized rendering pipeline based on an image-based virtual view synthesis algorithm to achieve real-time and high-quality scene rendering. This algorithm uses an on-the-fly Truncated Signed Distance Function (TSDF) approach, which marches along virtual rays within a computed precise search interval to determine surface intersections. We then design a multiweight projective texture mapping method to fuse color information from multiple views. Furthermore, we introduce a method that uses a depth confidence map to weight the rendering results from different views, which mitigates the impact of sensor noise and inaccurate measurements on the rendering results. Finally, our system places conference participants from different spaces into a virtual conference environment with a global coordinate system through coordinate transformation, which simulates a real conference scene in physical space, providing an immersive remote conferencing experience. Experimental evaluations confirm our system's real-time, low-latency, high-quality, and immersive capabilities.

关键词： Rendering (computer graphics) Real-time systems Three-dimensional displays Cameras Streaming media Image color analysis Videoconferences Point cloud compression Image reconstruction Hardware 3D video conferencing holographic telepresence multiview RGB-D video view-dependent rendering

来源：评论

学校读者我要写书评

暂无评论

Extremum Encoding for Joint Baseband Signal compression and Time-Delay Estimation for Distributed Systems

Extremum Encoding for Joint Baseband Signal Compression and ...

引用

2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Weiss, Amir Kochman, Yuval Wornell, Gregory W. Faculty of Engineering Bar-Ilan University Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Research Laboratory of Electronics Massachusetts Institute of Technology United States

ISBN: (纸本)9798350368741

The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "extremum encoding" compression-estimation scheme, we address the critical extension to complex-valued signals, suitable for radio-frequency (RF) baseband processing. This extension introduces new challenges, e.g., due to unknown phase of the signal of interest and random phase of the noise, rendering a naïve application of the original scheme inapplicable and irrelevant. In the face of these challenges, we propose a judiciously adapted, though natural, extension of the scheme, paving its way to RF applications. While our extension leads to a different statistical analysis, including extremes of non-Gaussian distributions, we show that, ultimately, its asymptotic behavior is akin to the original scheme. We derive an exponentially tight upper bound on its error probability, corroborate our results via simulation experiments, and demonstrate the superior performance compared to two benchmark approaches. © 2025 ieee.

关键词： compression for estimation data compression distributed estimation max-index estimator Time-delay estimation

来源：评论

学校读者我要写书评

暂无评论

Networked ISAC Beamforming Design with Capacity-Limited Fronthaul Links

Networked ISAC Beamforming Design with Capacity-Limited Fron...

引用

2025 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Kexin Xu, Yanqing Chang, Tsung-Hui School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Science & Engineering The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

ISBN: (纸本)9798350368741

This study investigates a networked integrated sensing and communication (ISAC) system, where a central processor coordinates multiple ISAC transmitters and a sensing receiver through capacity-limited fronthaul links. The system aims to serve multiple mobile users and simultaneously sense a point target. The primary objective is to minimize the total transmit power while meeting the minimum signal-to-interference-plus-noise ratio (SINR) requirements for both communication and sensing, as well as adhering to the fronthaul capacity constraints. This leads to a complicated joint fronthaul compression and beamforming design (J-FCBD) problem. Although the J-FCBD problem is challenging to solve, we demonstrate that the optimal fronthaul compression variables can be determined in closed form alongside the beamformers. This novel finding has not been previously reported in the literature. Leveraging this insight, we prove that the remaining problem can be globally solved using the semidefinite relaxation (SDR) technique. Simulation results verify the effectiveness of the proposed design. © 2025 ieee.

关键词： beamforming design fronthaul compression Networked integrated sensing and communication

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：