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

共 16713 条记录，以下是491-500 订阅

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A Compressed Post-Processing Algorithm for Online data Detection Model Based on Knowledge Distillation 5

A Compressed Post-Processing Algorithm for Online Data Detec...

引用

5th International conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Zhang, Xin Jia, Fengru Xu, Yinfei Xing, Zhigang Hangzhou Electric Power Equipment Manufacturing Co. Ltd Hangzhou China

ISBN: (纸本)9798331528911

This paper puts forward a compressed post-processing algorithm for the online data detection model based on knowledge distillation. By employing a new model to purify the eigenvalue of the input original image. The online detection model for the surface quality of workpieces is trained. The scaling factor in the BN layer is utilized to conduct sparse training of the local network space. And both channel simplify and layer simplify are carried out on the sparse model. YOLOV4 lacks the accuracy of location detection and identification is enhanced, and the autoregressive processing of compressed data is improved through the online data detection algorithm based on knowledge distillation. The simulation results indicate that without the knowledge distillation method, the optimal accuracy of the model is 96.05% after 100 iterations, which is 1.29% lower than that before compression. After 90 passes, the accuracy of the model attains 97.41%, which is essentially in line with that before compression. The end of tests reveal that the model condensing can accurately simplify the accuracy of model and guarantee the accuracy for the surface quality of workpieces. © 2024 ieee.

关键词： CSP Darknet53 data compression Knowledge distillation extraction Target detection YOLOv4 algorithm

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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...

引用

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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Communication-efficient Decentralised Federated Learning via Low Huffman-coded Delta Quantization Scheme 20

Communication-efficient Decentralised Federated Learning via...

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20th ieee International Wireless Communications and Mobile Computing (ieee IWCMC) - Green and Intelligent Communications

作者： Barhoush, Mandi Ayad, Ahmad Kohankhaki, Mohammad Schmeink, Anke RWTH Univ Chair Informat Theory & Data Analyt INDA Aachen Germany

ISBN: (纸本)9798350361261;9798350361278

Federated Learning (FL) revolutionizes distributed machine learning, enabling clients to learn collaboratively while keeping data private. In contrast, Decentralized Federated Learning (DFL) offers direct communication between clients without a central server, improving fault tolerance and network efficiency, but communication overhead remains a challenge. To address this, we propose a new scheme called Low Huffman-coded Delta Quantization (LHDQ) which achieves a remarkable quantization rate of 5 3 bits per parameter. We evaluate LHDQ within the DFL architecture under two proposed transmission protocols and compare it against conventional quantization schemes under various communication channel conditions. Despite a slight reduction in accuracy, LHDQ offers compelling advantages as alleviating communication bottlenecks, reducing transmitted bits, and accelerating training and convergence processes.

关键词： Federated Learning Decentralized Learning 6G Delta Quantization data compression Huffman Coding

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Value-Compressed Sparse Column (VCSC): Sparse Matrix Storage for Single-cell Omics data

Value-Compressed Sparse Column (VCSC): Sparse Matrix Storage...

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2024 ieee International conference on Big data, Bigdata 2024

作者： Wolfgang, Seth Ruiter, Skyler Tunnell, Marc Triche, Timothy Carrier, Erin Debruine, Zachary University of Indiana Department of Intelligent Systems Engineering BloomingtonIN United States Purdue University Department of Computer Science West LafayetteIN United States Van Andel Institute Department of Epigenetics Grand RapidsMI United States Grand Valley State University College of Computing AllendaleMI United States

ISBN: (纸本)9798350362480

Genomics datasets, such as single-cell transcriptomics, are often very large and highly sparse, posing significant challenges for both storage and computation. As the scale of data generation accelerates, efficiently compressing these datasets becomes crucial. Current compression methods, like the popular Compressed Sparse Column (CSC) format, capitalize only on sparsity but overlook other properties like redundancy, which can offer additional opportunities for compression. Genomics data, especially single-cell assays, often exhibit high redundancy within columns, making traditional sparse formats inefficient for in-core computation. In this paper, we present two extensions to CSC: (1) Value-Compressed Sparse Column (VCSC) and (2) Index-and Value-Compressed Sparse Column (IVCSC). VCSC takes advantage of high redundancy within a column to further compress data up 1.9-fold over CSC on real data, without significant negative impact to performance characteristics. IVCSC extends VCSC by compressing index arrays through delta encoding and byte-packing, achieving up to a 4.4-fold decrease in memory usage over CSC on real data. Our benchmarks show that VCSC and IVCSC can be used in compressed form with little added computational cost. These formats represent a step forward in balancing the growing demands of data storage and processing in the era of large-scale genomics. © 2024 ieee.

关键词： data redundancy matrix compression sparse data

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LEARNED VIDEO compression WITH SPATIAL-TEMPORAL OPTIMIZATION 49

LEARNED VIDEO COMPRESSION WITH SPATIAL-TEMPORAL OPTIMIZATION

引用

49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wang, Yiming Huang, Qian Tang, Bin Liu, Wenting Shan, Wenchao Xu, Qian Hohai Univ Coll Comp Sci & Software Engn Nanjing Peoples R China Hohai Univ Key Lab Water Big Data Technol Minist Water Resou Nanjing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Previous optical flow based video compression is gradually replaced by unsupervised deformable convolution (DCN) based method. This is mainly due to the fact that the motion vector (MV) estimated by the existing optical flow network is not accurate and may introduce extra artifacts. However, DCN based method is difficult for training owing to the lack of explicit guidance in the feature space. In this work, we propose a learned video compression with spatial-temporal optimization. Specifically, we first propose the spatial-temporal motion refinement module to improve the accuracy of MV estimated by the optical flow network for prediction. Then, we propose the In-loop filter module to remove compression artifacts and improve the reconstructed frame quality. Finally, comprehensive experimental results demonstrate our proposed method outperforms the recent learned methods on three benchmark datasets. Moreover, our method also beats the H.266/VVC in terms of MS-SSIM metrics.

关键词： learned video compression motion vector spatial-temporal motion refinement In-loop filter

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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.

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A Method for CNC Machining Trajectory compression Based on Deep Q-Network (DQN) 13

A Method for CNC Machining Trajectory Compression Based on D...

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13th International conference of Information and Communication Technology, ICTech 2024

作者： Zhang, Lipeng Yu, Dong Hu, Yi Wang, Jingru He, Wuwei Zhou, Zheng Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350376258

Aiming at the problems of low data transmission efficiency, ample storage space required, and performance degradation when executing G-code caused by the massive small segment machining code (G01 code) of CNC system, a data compression method of CNC system machining G01 code based on deep Q network (DQN) was proposed. This method first parses the G01 machining data program of the CNC system into a 3D data point sequence, treats the data compression process as a sequential decision problem, and uses the DQN method to learn the sequence decision problem to achieve data compression. It can also achieve adaptive adjustment of the compression rate by adjusting the algorithm parameters of the DQN, which has the advantages of high compression efficiency and fast execution speed. At the end of the paper, an experimental case is given to demonstrate the effectiveness of the proposed method by selecting key points in the G-code file of lamp cover modeling. ©2024 ieee.

关键词： data transfer

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A Low-complexity Neural Network for Compressed Video Post-processing in HEVC

A Low-complexity Neural Network for Compressed Video Post-pr...

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data compression conference (DCC)

作者： Liu, Zheng Qi, Honggang Han, Yu Cui, Guoqin Zhang, Yundong Univ Chinese Acad Sci Beijing Peoples R China Vimicro Chip Technol Co Ltd Beijing Peoples R China

In this work, we propose a low-complexity convolution neural network for compressed video post-processing. The main process can be expressed as follows:

ISBN: (纸本)9781665478939

In this work, we propose a low-complexity convolution neural network for compressed video post-processing. The main process can be expressed as follows:

关键词： Convolution Neural networks data compression

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Design of Canonical Huffman Encoder in VLSI for data compression 1

Design of Canonical Huffman Encoder in VLSI for Data Compres...

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1st International conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Ewins Pon Pushpa, S. Jayasree, T. Pavithra, P. Mariammal, K. Pushpalatha, V. Sittalatchoumy, R. Anna University Department of ECE CEG Guindy Chennai India Madras Institute of Technology Chrompet Anna University Department of Electronics Engg. Chennai India

ISBN: (纸本)9798350364590

Design of Canonical Huffman Encoder is an emerging technology in Very Large-Scale Integration (VLSI). The proposed design comprises three key stages: Frequency Generation, Code Size Computing, and Sorting and Code Size Limiting. This canonical Huffman encoding approach assigns variable length codes to symbols according to their frequencies, simplifying the encoding process. This frequency data constructing a canonical Huffman tree, optimizing binary codes for symbols and effectively reducing the overall data sizes. When implemented in Verilog 2017.4, it generates encoding bits as output, representing canonical code-sizes and canonical code-words. The results indicated that the proposed architecture decreased encoding time by 46.09%. Furthermore, a reduction in area of 60% and a power saving of 70% were observed in the Huffman encoder. The hardware implementation of LZ77 encoder and dynamic huffman encoder in data compression are designed for effective implementation of GZIP compression. Above the scenario has been executed using the Nexys A7-100T Artix-7 Series FPGA Evaluation Board. GZIP, an extensively utilized compression algorithm recognized for its effective compression ratios, is targeted to achieve a compression ratio of 88.92%. © 2024 ieee.

关键词： data compression ratio

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