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检索条件"任意字段=IEEE Data Compression Conference"

共 16714 条记录，以下是31-40 订阅

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Modified Unitary data Storage Method

Modified Unitary Data Storage Method

引用

2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Terekhov, Alexey N. Korchagina, Alla S. Moscow Technical University of Communications and Informatics Moscow Russia

ISBN: (纸本)9798331532635

The article is intended to consider a method of data storage based on the use of a modified relative unary coding system. During the research, it is planned to develop a data storage algorithm using relative unary code. It is planned to identify the disadvantages and advantages of the proposed algorithm. Suggest a method for the utilitarian application of the developed algorithm for relative information storage. Provide formulas for the utilitarian application of the developed algorithm for relative information storage. To test the functionality of the model, create software that implements the developed algorithm for relative information storage. Show that the information sequences used in the method are mutually orthogonal and belong to the orthonormal Euclidean space. Establish additional ways to reduce information redundancy that increase the compression ratio of the unary data encoding method. Adapt the data compression method by encoding the lengths of the series for use in the developed algorithm for relative information storage. Evaluate the efficiency of data storage in the relative storage algorithm. © 2025 ieee.

关键词： Storage efficiency

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Research on Intelligent Sensor Network for Laboratory data Acquisition and Processing

Research on Intelligent Sensor Network for Laboratory Data A...

引用

2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Kong, Fei Li, Junmei College of Art Jingchu University of Technology Jingmen China College of Artificial Intelligence Jingchu University of Technology Jingmen China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504205

This paper proposes a laboratory data acquisition and processing system based on intelligent sensor networks, aiming to improve the efficiency and accuracy of experimental data acquisition. The system collects experimental data in real time by deploying distributed sensor nodes, and uses Huffman coding algorithm for data compression and transmission. Huffman coding effectively reduces the amount of data transmission by optimizing the coding length, thereby improving the transmission efficiency of the system and saving network bandwidth. To ensure the efficient operation of the system, this paper designs a multi-level data processing algorithm based on intelligent sensor networks, which can quickly and accurately analyze and process data after data acquisition, thereby realizing efficient experimental data monitoring and control. Simulation results show that the system can significantly improve the speed of data acquisition and processing under different experimental conditions, and effectively reduce the delay of data transmission. When collecting 1000 data points, the data transmission rate after Huffman coding is increased by about 25%, and the transmission delay is reduced from the original 150ms to 120ms. At the same time, the data compression ratio reaches 50%. Compared with traditional transmission methods, the system can significantly reduce the occupancy of network bandwidth. Huffman coding shows high efficiency in compression ratio and decoding speed. In response to the processing needs of large-scale experimental data, Huffman coding effectively improves data processing capabilities and ensures the integrity of data transmission. © 2025 ieee.

关键词： data collection

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RAW data: A Key Component for Effective Deepfake Detection

RAW Data: A Key Component for Effective Deepfake Detection

引用

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

作者： Husseinil, Sahar Dugelay, Jean-Luc Department of Digital Security EURECOM Biot France Docaposte Biometrics Lab Biot France

ISBN: (纸本)9798350368741

Current deepfake detection methods are prone to overfitting to specific deepfake artifacts and often struggle with genuine images that have undergone compression and other image processing operations. These processes can obscure indicators of forgery, leading to inaccurate decisions. This paper aims to redefine the boundary between real and fake images by narrowing the definition of authentic samples to a stage closer to the radiance of the scene as captured by the sensor, prior to any transformations by an Image Signal Processor (ISP). Our proposed method bypasses ISP processing steps, such as denoising, white balance, and de-mosaicing, which are embedded in camera hardware. This unaltered preservation makes raw data an ideal starting point for deepfake detection. Given the scarcity of large-scale datasets designed for training on raw images, we propose a methodological approach to train our model on raw image data. Our method demonstrated state-of-the-art performance on the CDF dataset and showed competitive results across other RGB domain deepfake detection datasets. The model developed in this study is available at https://***/DeepFaux/Deepfake-Detection-with-RAW-data. © 2025 ieee.

关键词： deepfake detection image manipulation ISP

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SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous data

SADDLe: Sharpness-Aware Decentralized Deep Learning with Het...

引用

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

作者： Choudhary, Sakshi Aketi, Sai Aparna Roy, Kaushik Purdue University West LafayetteIN United States

ISBN: (纸本)9798331510831

Decentralized training enables learning with distributed datasets generated at different locations without relying on a central server. In realistic scenarios, the data distribution across these sparsely connected learning agents can be significantly heterogeneous, leading to local model over-fitting and poor global model generalization. Another challenge is the high communication cost of training models in such a peer-to-peer fashion without any central coordination. In this paper, we jointly tackle these two-fold practical challenges by proposing SADDLe, a set of sharpness-aware de-centralized deep learning algorithms. SADDLe leverages Sharpness-Aware Minimization (SAM) to seek a flatter loss landscape during training, resulting in better model generalization as well as enhanced robustness to communication compression. We present two versions of our approach and demonstrate its effectiveness through extensive experiments on various Computer Vision datasets (CIFAR-10, CIFAR-100, Imagenette, and ImageNet), model architectures, and graph topologies. Our results show that SADDLe leads to 1-20% improvement in test accuracy as compared to existing techniques while incurring a minimal accuracy drop ( 1%) in the presence of up to 4× compression. © 2025 ieee.

关键词： Federated learning

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Learning Structured Compressed Sensing with Automatic Resource Allocation

Learning Structured Compressed Sensing with Automatic Resour...

引用

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

作者： Wang, Han Pérez, Eduardo Huijben, Iris A.M. van Gorp, Hans van Sloun, Ruud Römer, Florian Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing IZFP Germany Department of Electrical Engineering Eindhoven University of Technology Netherlands Electronic Measurements and Signal Processing Ilmenau University of Technology Germany

ISBN: (纸本)9798350368741

Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software regarding data storage and processing. Rather than designing a single compression matrix as in conventional compressed sensing, structured compressed sensing yields dimension-specific compression matrices, reducing the number of optimizable parameters. Recent advances in machine learning (ML) have enabled task-based supervised learning of subsampling matrices, albeit at the expense of complex downstream models. Additionally, the sampling resource allocation across dimensions is often determined in advance through heuristics. To address these challenges, we introduce Structured COmpressed Sensing with Automatic Resource Allocation (SCOSARA) with an information theory-based unsupervised learning strategy. SCOSARA adaptively distributes samples across sampling dimensions while maximizing Fisher information content. Using ultrasound localization as a case study, we compare SCOSARA to state-of-the-art ML-based and greedy search algorithms. Simulation results demonstrate that SCOSARA can produce high-quality subsampling matrices that achieve lower Cramér Rao Bound values than the baselines. In addition, SCOSARA outperforms other ML-based algorithms in terms of the number of trainable parameters, computational complexity, and memory requirements while automatically choosing the number of samples per axis. © 2025 ieee.

关键词： Compressed Sensing Information Theory Subsampling Unsupervised Learning

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Efficient lightweight image compression approach for Multimedia Internet of Things 5

Efficient lightweight image compression approach for Multime...

引用

5th ieee Middle East and North Africa COMMunications conference, MENACOMM 2025

作者： Idrees, Sara Kadhum Couturier, Raphael Azar, Joseph Idrees, Ali Kadhum Gechter, Franck BelfortF-90000 France University of Babylon College of Information Technology Dept. of Information Networks Babylon Iraq Université Marie et Louis Pasteur CNRS Institut FEMTO-ST BelfortF-90000 France University of Applied Science and Arts Smart Edge Lab Faculty of Computer Science Dortmund Germany SIMBIOT Team Vandoeuvre-lès-NancyF-54506 France

ISBN: (纸本)9798331519957

The development of the Internet of Things (IoT) and communication technologies has led to the emergence of the multimedia IoT. The multimedia IoT contributes to a variety of multimedia applications, that can capture and transmit large amounts of images or video sequences over an IoT network. Due to restricted resources, multimedia IoT devices must transmit less multimedia data while retaining accuracy. This can save energy and memory use and increase multimedia IoT network performance. This paper proposes an efficient lightweight image compression approach (ELiCA) for Multimedia Internet of Things. The proposed ELiCA starts dividing the captured image into equal-sized blocks and the Discrete Cosine Transform (DCT) is applied to each of them to produce the DCT coefficients. Then, the DCT coefficients are quantized. The output of the quantization process is reordered using zigzag process. Finally, the output files of the zigzag are compressed using the error-bounded lossy compression technique known as SZ developed specifically for High-Performance Computing (HPC) systems. The compressed file will be transmitted by the MIoT device. The proposed ELiCA is evaluated using real sensed images and based on real IoT devices to show the efficiency of the proposed approach. The ELiCA presents better results compared with JPEG in terms of compression ratio, PSNR, and SSIM. © 2025 ieee.

关键词： Image compression

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

2025 IEEE International Conference on Acoustics, Speech, and...

引用

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

ISBN: (纸本)9798350368741

The proceedings contain 3303 papers. The topics discussed include: object-level data augmentation for visual 3D object detection in autonomous driving;language-queried target sound extraction without parallel training data;ToolFiVe: enhancing tool-augmented LLMs via tool filtering and verification;integrating multi-scale compression attention with edge detection for ultrasound tumor segmentation;G-Depth: an efficient graph method for robust depth completion;kernel-based anomaly detection using generalized hyperbolic processes;enhancing document-level relation extraction through entity-pair-level interaction modeling;towards HRTF personalization using denoising diffusion models;what does an audio deepfake detector focus on? a study in the time domain;enhancing incomplete multimodal learning via modal complementary recovering;and neighborhood attention transformer with progressive channel fusion for speaker verification.

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Image compression Algorithms - Comparative Analysis 3

Image Compression Algorithms - Comparative Analysis

引用

3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Niharika Reddy, G. Namrutha, M. Akshaya, Namratha Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508456

Due to the abundance of the new digital media data, the issue of image quality and volume of data requiring compression has become a significant factor of concern, especially in media storage and transmitting. This work affords a comparative analysis of different image compression techniques with focus on the compression ratio, quality preservation, and complexity. A new hybrid model of Predictive Coding, Run Length Coding and Quantum Entropy Coding (QEC) is proposed and shown to exhibit negligible quality loss with substantial space saving. The experimental outcomes show that the proposed method reduces space 80 percent and works better than previous methods for areas requiring high speed and relative accuracy. These insights are timely, as practical computing-communication trade-offs are paramount in the new generation of social networks, medicine, and multimedia streaming. © 2025 ieee.

关键词： Image compression

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A Novel Artistic Image compression Algorithm Based on Multi-Scale Adaptive Discrete Wavelet Transform

A Novel Artistic Image Compression Algorithm Based on Multi-...

引用

2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Li, Yijun China Academy of Art Yuhang District Zhejiang Hangzhou311100 China

ISBN: (纸本)9798331509828

How to compress information is a challenge in the digital age, and the image compression has a wide range of application scenarios. In order to solve the challenges of low compression efficiency and high loss rate of the traditional algorithms, this study proposes a compression algorithm based on multi-scale adaptive discrete wavelet transform. The core of the proposed algorithm is to make reasonable use of the multiresolution analysis characteristics of discrete wavelet transform. Based on the feature extraction, the image information is decomposed into high-frequency and low-frequency parts. This operation can stably and effectively implement frequency domain threshold processing and reduce redundant data. The proposed algorithm optimizes the encoding efficiency of image features to the greatest extent by introducing block difference features and designing a new iterative function system. In view of the high redundancy and high complexity of the artistic images, the traditional wavelet analysis is improved and optimized, and a multi-scale adaptive model is introduced. In the experiment, the test used multiple sets of artistic image test sets and compared them with mainstream algorithms (JPEG, VAE, etc.). In PSNR analysis, the proposed algorithm achieves better performance. © 2025 ieee.

关键词： Discrete wavelet transforms

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C4ECC: data compression for Bandwidth Efficiency Under ECC Protection in GPUs

C4ECC: Data Compression for Bandwidth Efficiency Under ECC P...

引用

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

作者： Park, Sanghyun Choi, Boyeol Kim, Jungrae Sungkyunkwan University Semiconductor Convergence Engineering Suwon Korea Republic of Semiconductor Systems Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331510756

Modern GPUs using Graphics DDR DRAM rely on in-band Error Correcting Code (ECC) for memory protection, but this reduces effective bandwidth and increases latency. This paper introduces data compression for Bandwidth Efficiency under ECC Protection in GPUs, called C4ECC. C4ECC compresses 32B blocks to fit both data and redundancy within a single transfer between the GPU's Last Level Cache and device memory. Our evaluation shows that while traditional in-band ECC increases memory traffic by 68%, C4ECC reduces this overhead to an average of just 1.4 %. This reduction significantly lowers the performance overhead caused by in-band ECC from 34% to 3%, particularly in memory-intensive workloads. © 2025 ieee.

关键词： Bandwidth compression

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