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

共 16714 条记录，以下是21-30 订阅

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Exploiting Differential-Based data Encoding for Enhanced Query Efficiency 25

Exploiting Differential-Based Data Encoding for Enhanced Que...

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30th Asia and South Pacific Design Automation conference, ASP-DAC 2025

作者： Liu, Fangxin Wang, Zongwu Xu, Peng Huang, Shiyuan Jiang, Li Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Qi Zhi Institute China MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China

ISBN: (纸本)9798400706356

Storing large-scale high-dimensional data, which is rapidly generated by both industry and academia, poses substantial challenges, primarily in terms of storage and maintenance costs. While data compression techniques offer a potential solution to these challenges, they must overcome two critical hurdles: 1) preserving data integrity within lossless bounds and 2) maintaining query performance on compressed *** address the above challenges, we propose a novel approach based on differential techniques that combine high-dimensional data compression with an efficient query engine. Specifically, Our methodology begins by employing vector quantization to transform high-dimensional vectors into compact integer sequences, known as quantization codes - -a classical and versatile compression method. Subsequently, we introduce DCQ, which builds upon the differential concept to perform lossless compression on quantization codes. DCQ organizes quantization codes into a tree structure and constructs a hierarchy of trees by analyzing the dissimilarity between two quantization codes. To minimize the impact on query performance, we have developed an adaptive reconstruction algorithm that partitions and reconstructs the original tree structure. This algorithm effectively balances the workload for searching each sub-tree, optimizing the compression-performance trade-off. Finally, we adopt a storage format based on multiple subtrees for rapid searching, thereby enabling differential storage to facilitate efficient queries. Extensive experiments on various large-scale real-world datasets show that DCQ achieves a compression ratio of up to 2.5 on the quantization codes and achieves > 50× performance improvement compared to the state-of-the-art general-purpose lossless compression techniques. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Trees (mathematics)

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Multi-modal Dynamic Point Cloud Geometric compression Based on Bidirectional Recurrent Scene Flow

Multi-modal Dynamic Point Cloud Geometric Compression Based ...

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

作者： Nan, Fangzhe Li, Frederick Wang, Zhuoyue Tam, Gary K.L. Jiang, Zhaoyi Zheng, Dong Yang, Bailin School of Statistics and Mathematics Zhejiang Gongshang University China School of Computer Science and Technolgy Zhejiang Gongshang University China University of Durham United Kingdom University of California Berkeley United States Swansea University United Kingdom Universal Ubiquitous Technology Co. Ltd.

ISBN: (纸本)9798350368741

Deep learning methods have recently shown significant promise in compressing the geometric features of point clouds. However, challenges arise when consecutive point clouds contain holes, resulting in incomplete information that complicates motion estimation. To our knowledge, most existing dynamic point cloud compression methods have largely overlooked this critical issue. Moreover, these methods typically employ a multi-scale single-pass approach for motion estimation, performing only one estimation at each scale. This limits accuracy and adversely impacts compression performance. To address these challenges, we propose a dynamic point cloud compression model called M2BRDPCC (Multi-Modal Multi-Scale Bidirectional Recursion for Dynamic Point Cloud compression). Our method introduces two key innovations. First, we integrate both point cloud and image data as inputs, leveraging a multi-modal feature representation completion (MFRepC) approach to align information across modalities. This addresses the issue of missing data in point clouds by using complementary information from images. Second, we implement a multi-scale bidirectional recursive (MSBR) motion estimation method. This module iteratively refines motion flows in both forward and backward directions, progressively enhancing point cloud features and improving motion estimation accuracy. Experimental results on widely used datasets, including MVUB and 8iVFB, demonstrate the effectiveness of our approach. Compared to existing methods, M2BR-DPCC achieves superior performance, with an average BD-rate improvement of 95.23% over V-PCC, 12.92% over D-DPCC, and 16.16% over patchDPCC. These results underscore the potential of leveraging multi-modal data and bidirectional refinement for dynamic point cloud compression. © 2025 ieee.

关键词： Dynamic point cloud geometric compression Multi-modal Multi-scale bidirectional recurrent

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Learned Video compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

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

作者： Huang, Qian Liu, Wenting Li, Xin Wang, Yiming College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China

ISBN: (纸本)9798350368741

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR. © 2025 ieee.

关键词： coordinate network cross attention flow pyramid learned video compression

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Enhancing data Security and Efficiency: A Hybrid Cryptography Approach (AES + ECC) Integrated with Steganography and compression Algortihm 3

Enhancing Data Security and Efficiency: A Hybrid Cryptograph...

引用

3rd International conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Badhan, Akash Malhi, Simarjit Singh School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331527549

The paper presents a novel approach for enhancing data security and efficiency using a hybrid cryptography model that is Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) with steganography and compression techniques to conceal the encrypted data within image file, also compression technique applied to reduce storage usage. The system employs AES for secure data encryption due to its fast encryption time and reliability, while ECC is utilized for encrypting AES keys, benefiting from ECC’s lower computational demands and smaller key sizes, which offer secure key exchange. Further to mask the encrypted data, steganography is implemented with inverted least significant bit (LSB) technique. Additionally, the model incorporates the WebP compression algorithm to reduce the overall data size, to facilitate data transmission efficiently without losing data integrity or security. Experimental results of research indicate that the system not only secures data effectively but also preserves the visual quality of digital carrier images. The steganalysis shows the performance of the proposed system performed is quantified by a Peak Signal-to-Noise Ratio (PSNR) of 68.90 and a Mean Squared Error (MSE) OF 0.0083, reflecting minimal and acceptable degradation in image quality. These metrics show the system’s capability to maintain excellent image fidelity post encryption and embedding, confirming its reliability for secure communication applications where maintaining data integrity and efficiency is crucial. © 2025 ieee.

关键词： data reliability

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Research on Efficient Processing Algorithm for Engineering Cost data Using Cloud Computing Platform

Research on Efficient Processing Algorithm for Engineering C...

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

作者： Li, Dantong Dalian University of Finance and Economics Dalian China

ISBN: (纸本)9798331504205

This paper proposes a research scheme for efficient processing algorithm of engineering cost data based on cloud computing platform, aiming to improve data processing efficiency by utilizing the high-performance computing capability and distributed characteristics of cloud computing. The algorithm design takes distributed compression algorithm as the core, combines data partitioning, parallel computing and storage optimization technology to achieve efficient storage and fast access to large-scale cost data. In the system implementation, a parallel algorithm architecture that adapts to multi-node environment is designed. The algorithm performance is evaluated by building a simulation model. The experimental results show that the method proposed in this paper is superior to traditional algorithms in data compression rate, processing speed and resource utilization. When processing 1TB cost data, the compression rate reaches 85%, and the processing efficiency is improved by about 60%. data analysis further verifies the stability and scalability of the algorithm, and provides theoretical basis and technical support for intelligent data processing in the field of engineering cost. This study opens up a new path for engineering cost data processing, and the proposed algorithm and platform architecture can be widely used in other complex data scenarios. © 2025 ieee.

关键词： Cloud platforms

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Comparative Analysis of Deep Learning Techniques for Underwater Multimedia compression 6

Comparative Analysis of Deep Learning Techniques for Underwa...

引用

6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Priyakanth, R. Simon, Judy Department of ECE SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331522667

Recent advancements in underwater multimedia compression, particularly deep learning techniques, are explored to address the increasing data demands of the Internet of Underwater Things (IoUT). Efficient compression is critical due to the limited bandwidth and greater transmission error rates in underwater acoustic channels. Deep learning models such as the UAT-SSIC scheme, which employs semantic segmentation with joint source-channel coding, and the DWT–CNN framework, which combines discrete wavelet transform with convolutional neural networks, have demonstrated superior compression performance. Advanced techniques like convolutional autoencoders (CAEs) and UCSNet leverage adaptive sampling and underwater priors to enhance image reconstruction and reduce errors. Additionally, approaches like ESPC-BCS-Net and RFD-ECNet achieve efficient compression by integrating compressed sensing and multi-scale feature extraction. These methods consistently deliver high PSNR and SSIM values, minimize spatial redundancy, and optimize energy use, demonstrating their significant potential for improving underwater communication and data processing. © 2025 ieee.

关键词： Semantic Segmentation

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A High-Speed compression and Secure Authentication Framework for data Sharing Cloud-Enabled Big data Environments

A High-Speed Compression and Secure Authentication Framework...

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

作者： Abirami, I. Selvi, S. Lalitha, R. Julian, Anitha Rmk Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509828

With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-speed compression and secure authentication come forth playing a very crucial role. The two significant challenges addressed in this framework are data compression for efficient storage and transmission and robust authentication for secure access and integrity of shared data. This way, the framework compresses data size significantly without losing its quality, leading to efficient, faster data transfers and reduced considerably storage costs on cloud infrastructures. At the same time, secure authentication mechanisms [1] such as cryptography techniques and multi-factor authentication ensure that only authorized users access sensitive data, thus protected from potential unauthorized access or cyber-attacks. Integration of both capabilities speed compression and secure authentication-functionalizes big data environments to their peak performance and security. This framework is highly suitable for high-scale data-oriented industries like healthcare, finance, and IoT, which necessitate high-speed, secure, and reliable data sharing. Ultimately, this approach improves scalability and security in cloud-enabled big data systems by enabling organizations to keep pace with growing needs for highly efficient and secure data sharing in a highly dynamic environment. © 2025 ieee.

关键词： data integrity

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Efficient Progressive Image compression with Variance-Aware Masking

Efficient Progressive Image Compression with Variance-Aware ...

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

作者： Presta, Alberto Tartaglione, Enzo Fiandrotti, Attilio Grangetto, Marco Cosman, Pamela University of Turin Italy Ltci Télécom Paris Institut Polytechnique de Paris France Uc San Diego Dept. of Electrical and Computer Engineering CA United States

ISBN: (纸本)9798331510831

Learned progressive image compression is gaining momentum as it allows improved image reconstruction as more bits are decoded at the receiver. We propose a progressive image compression method in which an image is first represented as a pair of base-quality and top-quality latent representations. Next, a residual latent representation is encoded as the element-wise difference between the top and base representations. Our scheme enables progressive image compression with element-wise granularity by introducing a masking system that ranks each element of the residual latent representation from most to least important, dividing it into complementary components, which can be transmitted separately to the decoder in order to obtain different reconstruction quality. The masking system does not add further parameters or complexity. At the receiver, any elements of the top latent representation excluded from the transmitted components can be independently replaced with the mean predicted by the hyperprior architecture, ensuring reliable reconstructions at any intermediate quality level. We also in-troduced Rate Enhancement Modules (REMs), which refine the estimation of entropy parameters using already decoded components. We obtain results competitive with state-of-the-art competitors, while significantly reducing computational complexity, decoding time, and number of parameters. © 2025 ieee.

关键词： Image compression

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Research on Optimization Algorithm of Embedded AI Real-Time Deep Learning Model in IoT Devices 5

Research on Optimization Algorithm of Embedded AI Real-Time ...

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5th ieee International conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Zheng, Yifan School of Big Data Fuzhou University of International Studies and Trade Fuzhou Fujian China

ISBN: (纸本)9798331533694

This paper focuses on the optimization algorithm of embedded AI real-Time deep learning models in IoT devices, aiming to solve the problem of efficient and low-latency inference in resource-constrained environments. In order to solve the problems of high computational complexity and large memory occupation of existing models when running on embedded devices, this paper proposes a multi-stage optimization framework, which combines model compression, quantization and acceleration techniques to significantly improve the real-Time performance of the model. We have introduced a structured pruning approach that reduces the amount of model parameters and computational overhead by removing redundant neurons and channels. Secondly, converting the floating-point number into a low-bit integer representation, which further reduces the memory occupation and computational complexity. In addition, an adaptive inference scheduling algorithm was designed to dynamically adjust the inference path according to the characteristics of the input data, which improved the response speed and energy efficiency ratio of the model. The optimized model in this paper achieves more than 50% improvement in inference speed in multiple IoT application scenarios, while maintaining a high accuracy rate. © 2025 ieee.

关键词： adaptive inference scheduling embedded AI Internet of Things model compression real-Time optimization

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Next-Gen Genome compression: Learning-Based Solutions for Reference-Free DNA Storage

Next-Gen Genome Compression: Learning-Based Solutions for Re...

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2025 ieee SoutheastCon, SoutheastCon 2025

作者： Sikder, Sadia Noor, Dewan Fahim Auburn University Department of Chemical Engineering AuburnAL United States Tuskegee University Ece Department TuskegeeAL United States

ISBN: (纸本)9798331504847

With the rise of precision medicine, genomic data is being shared and analyzed at an unprecedented scale. However, the massive size of DNA sequences poses significant storage and transmission challenges. Existing genome compression techniques remain in early development compared to well-established image and video coding methods. This paper presents a machine learning-based approach for reference-free Deoxyribonucleic Acid (DNA) sequence compression. The method leverages codon-level mapping to American Standard for Information Interchange (ASCII) symbols, aligning with the probability mass function of standard English text. A neural network-based compression model is then applied to eliminate redundancy in the codon sequence. Experimental results demonstrate significant improvements in compression efficiency over existing state-of-the-art methods, particularly with PAQ-based encoding. This approach enhances genomic data accessibility, accelerates genetic marker identification, and optimizes drug discovery processes for precision medicine applications. Future research will explore deep learning advancements to further improve compression rates and computational efficiency. © 2025 ieee.

关键词： Image coding

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