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

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PixelDenoise: Purifying Adversarial Images in a Black-Box Way 4

PixelDenoise: Purifying Adversarial Images in a Black-Box Wa...

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4th International conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Zhu, Tianyu Sun, Heming Fan, Yibo Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai China Yokohama National University Faculty of Engineering Yokohama Japan

ISBN: (纸本)9798331528850

Despite the outstanding compression quality of learned image compression (LIC), its vulnerability to adversarial attacks cannot be ignored. Our experiments show up to a 19.15 times increase in bits per pixel (bpp) under the PGD attack. Although the adversarial examples are unrecognizable to human eyes, we find PixelCNN++ effective at detecting and modifying them back to the original data distribution. Thus, we first propose PixelDenoise, which leverages PixelCNN++ to improve the performance of LIC models in a black-box way. This innovation significantly decreases the bpp change from 19.15 times to 2.56 times without worsening the reconstruction quality. Since PixelDenoise requires little detail about the attack or the model, it is more universal than white-box defense methods like adversarial training. Additionally, we simplified the sampling process of PixelDenoise by minimizing the context size, reducing the processing time from 9 hours to 5 minutes per full-resolution image, with memory consumption dropping from 24GB to 2GB. © 2024 ieee.

关键词： Image compression

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SZOps: Scalar Operations for Error-bounded Lossy Compressor for Scientific data

SZOps: Scalar Operations for Error-bounded Lossy Compressor ...

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2024 Workshops of the International conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Agarwal, Tripti Di, Sheng Huang, Jiajun Huang, Yafan Gopalakrishnan, Ganesh Underwood, Robert Zhao, Kai Liang, Xin Li, Guanpeng Cappello, Franck University of Utah Salt Lake CityUT United States Argonne National Laboratory LemontIL United States University of California RiversideCA United States University of Iowa Iowa CityIA United States Florida State University TallahasseeFL United States University of Kentucky LexingtonKY United States

ISBN: (纸本)9798350355543

Error-bounded lossy compression has been a critical technique to significantly reduce the sheer amounts of simulation datasets for high-performance computing (HPC) scientific applications while effectively controlling the data distortion based on user-specified error bound. In many real-world use cases, users must perform computational operations on the compressed data. However, none of the existing error-bounded lossy compressors support operations, inevitably resulting in undesired decompression costs. In this paper, we propose a novel error-bounded lossy compressor (called SZOps), which supports not only error-bounding features but efficient computations (including negation, scalar addition, scalar multiplication, mean, variance, etc.) on the compressed data without the complete decompression step, which is the first attempt to the best of our knowledge. We develop several optimization strategies to maximize the overall compression ratio and execution performance. We evaluate SZOps compared to other state-of-the-art lossy compressors based on multiple real-world scientific application datasets. © 2024 ieee.

关键词： Error-bounded Lossy compression Scientific Application

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Efficient Image compression Through Differential Encoding of Super-Resolution Images

Efficient Image Compression Through Differential Encoding of...

引用

2024 International conference on Electronics, Information, and Communication, ICEIC 2024

作者： Kim, YongHwan Lee, Minhyeok Lee, Jungho Lee, Sangyoun Yonsei University Electrical and Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9798350371888

Neural network image compresssion methods have experienced significant progress during recent years. However, one of the persistent challenges in these methods is the considerable time taken for encoding and decoding. This paper presents an innovative approach to address this issue by using Super-Resolution. The primary objective of our method is to reduce encoding and decoding times while ensuring the preservation of image quality, as measured by peak signal-to-noise ratio (PSNR) and multiscale structural similarity (MS-SSIM). Using the Kodak dataset, we demonstrate a significant reduction in encoding and decoding times and minimal changes in reconstruction data size while maintaining image quality. © 2024 ieee.

关键词： Image compression

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Distributed Functional compression for Independent Component Analysis in Wireless Networks

Distributed Functional Compression for Independent Component...

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2024 ieee Global Communications conference, GLOBECOM 2024

作者： Alamoudi, A. Saidutta, Y. Fekri, F. University of Jeddah College of Engineering Department of Electrical and Electronic Engineering Jeddah Saudi Arabia Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States

ISBN: (纸本)9798350351255

In this paper, we consider distributed Independent Component Analysis (ICA) in wireless networks, where data from several geographically distributed wireless nodes (nodes) must be transmitted to a central server (server) to extract original sources through ICA. However, transmitting the vast amount of data over wireless channels to the server poses significant challenges due to limited bandwidth and privacy concerns. Our research addresses how to encode node data to meet channel rate constraints while providing maximally relevant information for ICA. Particularly, we propose a distributed functional compression framework for learning ICA over orthogonal AWGN channels. The framework leverages the Information Bottleneck (IB) principle to encode and compress the data to meet the channel rate constraint while maximally preserving the functionally relevant information for ICA. We train both neural encoders at the nodes and a neural decoder at the server in an unsupervised manner using the IB principle. We consider ICA for both linear and nonlinear mixing setups. Compared to the state-of-the-art, over real dataset, our proposed framework demonstrates a remarkable improvement of up to approximately 43% in accurately estimating the source signals in ICA while meeting the channels' rate constraints. Finally, we propose a three-stage training algorithm, where the raw sensory data never leaves the nodes either for training or inference, to reduce the communication overhead. We show that our proposed training algorithm notably reduces channel use compared to the traditional cloud-based method, where the observed data from the nodes are compressed and transmitted to the cloud for learning ICA. © 2024 ieee.

关键词： Channel coding

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Features of the Implementation of a Streaming data Exchange System in a Spatially Distributed System of Small-Sized Radar Stations

Features of the Implementation of a Streaming Data Exchange ...

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2024 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2024

作者： Nenashev, S.A. Sentsov, A.A. Nenashev, V.A. St.-Petersburg State University Of Aerospace Instrumentation Saint-Petersburg Russia

ISBN: (纸本)9798350364989

This paper examines the features of implementing intensive exchange of streaming data in a spatially distributed system of small-sized airborne radars integrated into a network that provides control, communication and integrated data processing. Effective use of information requires the concepts of a conceptual model of specialized radio channels that prevent unauthorized interference in the operation of the system and ensure high requirements for reliability, capacity, range, information transmission speed, noise immunity and noise immunity. The features of presentation, storage, masking and compression of streaming information (due to its large volume) are also considered, taking into account the specifics of its formation in an intermediate radio relay station and the characteristics of the observed areas and objects during its transmission to the processing and control point, where decisions are made that allow solve problems of an operational nature. When solving scientific research problems, the methodology for constructing multi-position onboard radar systems, the theory of high-resolution frame transmission, using special masking and compression algorithms without significant loss of useful information based on the use of a matrix apparatus, as well as methods that ensure the distribution of calculation of streaming data, were used © 2024 ieee.

关键词： Metadata

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Proceedings - 2024 ieee Winter conference on Applications of Computer Vision, WACV 2024

Proceedings - 2024 IEEE Winter Conference on Applications of...

引用

2024 ieee Winter conference on Applications of Computer Vision, WACV 2024

ISBN: (纸本)9798350318920

The proceedings contain 845 papers. The topics discussed include: partial binarization of neural networks for budget-aware efficient learning;GC-MVSNet: multi-view, multi-scale, geometrically-consistent multi-view stereo;contrastive viewpoint-aware shape learning for long-term person re-identification;ARNIQA: learning distortion manifold for image quality assessment;improving fairness using vision-language driven image augmentation;VCISR: blind single image super-resolution with video compression synthetic data;PIDiffu: pixel-aligned diffusion model for high-fidelity clothed human reconstruction;continuous adaptation for interactive segmentation using teacher-student architecture;learning to recognize occluded and small objects with partial inputs;and reverse knowledge distillation: training a large model using a small one for retinal image matching on limited data.

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Improved Deep Image compression with Joint Optimization of Cross Channel Context Model And Generalized Loop Filter

Improved Deep Image Compression with Joint Optimization of C...

引用

data compression conference (DCC)

作者： Ma, Changyue Wang, Zhao Liao, Ru-Ling Ye, Yan Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781665478939

Among the recent deep image compression frameworks, transform coding together with a context-adaptive entropy model is the most representative approach to achieve the best coding performance. For entropy model, 2D mask convolution is widely utilized to capture the spatial context, which omits the correlations along channel dimension. To complement to the spatial context, a cross channel context model is proposed. For transform, if given more network layers to improve its representation ability, how to allocate these network layers in forward and inverse transform is investigated. After analyzing the scheme of deep image compression connected with loop filter, we find this investigation can be regarded as a more generalized loop filter. The proposed cross channel context model and generalized loop filter (CCCMGLF) are integrated into the deep image compression framework and jointly optimized to improve the coding performance. Experimental results demonstrate that, using PSNR as distortion metric, the proposed CCCMGLF outperforms VTM-11.0 by 1.20%, 10.82% and 5.38% in terms of BD-rate reductions for Y, U and V components, respectively, for the Kodak dataset. For the JVET CTC sequences, the proposed method outperforms VTM-11.0 by 1.44% for Y but has a coding performance loss of 24.74% and 11.91% for U and V, respectively. Over the baseline deep compression framework, the proposed method provides 7.80%, 12.66% and 11.15% performance improvement for Y, U, and V, respectively, for the Kodak dataset;9.10%, 12.27%, and 12.68% performance improvement for Y, U and V, respectively, for the JVET CTC sequences. The proposed approaches are applicable in both image compression and intra coding in video compression.

关键词： Video coding Image coding Transform coding Transforms Video compression Distortion Solids

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JPEG-XL based compression of DICOM Images for Reduced Storage and Transmission Costs 3

JPEG-XL based Compression of DICOM Images for Reduced Storag...

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3rd ieee International conference on Intelligent Technologies, CONIT 2023

作者： Jebaraj, Sam Devavaram Srinidhi, N. Sri Sivasubramaniya Nadar College of Engineering Department of ECE Chennai India Sri Sivasubramaniya Nadar College of Engineering Department of BME Chennai India

ISBN: (纸本)9798350338607

Medical images are largely available in the form of DICOM. These files occupy large disk space and take much time to be transferred for diagnoses purposes. compression algorithms help in reducing the file size and the data transfer rate. Some common DICOM compression algorithms are: Joint Photographic Experts Group (JPEG), a lossy compression algorithm, JPEG 2000 and Run-length encoding (RLE). With the recent emergence of JPEG XL, the algorithm's performance outperforms the existing algorithms and is aimed to replace them. JPEG XL can compress in both lossless as well as lossy. This paper provides a comparative analysis of Image Quality Metrics like RMSE, PSNR, SSIM within the lossy and lossless modes of JPEG XL algorithms as well as a comparison between the compression ratios of JPEG XL and RLE algorithms. Hence, this paper suggests an emerging Lossless compression algorithm for a universal replacement for medical file size reduction. © 2023 ieee.

关键词： Diagnosis

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MEC-EnergySaver: Unleashing Efficiency Through D2D and data compression 16

MEC-EnergySaver: Unleashing Efficiency Through D2D and Data ...

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16th International conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Ghosh, Anindita Mukherjee, Poulomi De, Tanmay National Institute of Technology Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350383119

The shift from basic cell phones to smartphones has ushered in intelligent devices, alongside a surge in mobile apps that reshape daily life. Simultaneously, technologies like data analytics, AI and IoT have enabled smart homes and advanced transport systems, demanding substantial computational power from mobile devices. To tackle this challenge, mobile-edge computing (MEC) has emerged as a solution. Unlike remote cloud computing, MEC offers computing services at the network's edge, allowing mobile users to transfer their computing tasks to MEC servers located nearby. However, MEC systems face challenges in efficient computation offloading and distributing resources in a way that reduces energy use and processing times. The evolution of mobile devices and the rise of data-intensive applications have led to the need for efficient computing solutions like MEC, with the potential to integrate data compression for energy savings. This paper introduced a two-phase approach for request servicing in MEC systems. The first phase prioritizes Device-to-Device (D2D) connections based on device proximity, efficiently pairing devices for data exchange. Unpaired devices are directed to the nearest MEC node. In the second phase, requests not fulfilled via D2D connections are handled by strategically positioned MEC nodes, ensuring comprehensive coverage and minimizing network energy consumption. © 2024 ieee.

关键词： D2D communication data compression energy efficiency MEC

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Lightweight Decentralized Federated Learning Framework for Heterogeneous Edge Systems

Lightweight Decentralized Federated Learning Framework for H...

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ieee International conference on Web Services (ieee ICWS)

作者： Yang, Changsong Wang, Jianran Ding, Yong Li, Chunhai Liang, Hai Liu, Zhen Guilin Univ Elect Technol Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Guilin Univ Elect Technol Guangxi Engn Res Ctr Ind Internet Secur & Blockc Guilin Peoples R China Guangdong Prov Key Lab Novel Secur Intelligence T Shenzhen Peoples R China HKCT Inst Higher Educ Inst Cyberspace Technol Hong Kong Peoples R China

ISBN: (纸本)9798350368567;9798350368550

Edge computing serves as a potent solution for distributed learning tasks, offering benefits such as low latency, reduced network load, enhanced data security and privacy, adaptability to IoT development, and flexible deployment options. Introducing greater randomness and stability through the random selection of clients for communication in each iteration round enhances overall system performance. To solve the problem of high communication cost in traditional federated learning and model performance degradation in heterogeneous environments, we propose a lightweight decentralized federated learning framework, which uses soft threshold ternary quantization to compress local models. Soft threshold ternary quantization is a method to compress weight parameters into discrete values, which significantly reduces the complexity of parameter representation, thus saving storage space and communication costs. We devise an algorithm for selecting clients with similar data distributions to enhance model accuracy and expedite convergence in heterogeneous environments. Our framework ensures model performance while minimizing communication costs and safeguarding user privacy. Experiments show that compared with the existing ternary quantization federated learning method, the accuracy of the model we trained is improved.

关键词： Decentralized Federated Learning Model compression Ternary Quantization

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