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检索条件"主题词=Compression algorithms"

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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

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Yijun Li China Academy of Art Yuhang District Hangzhou Zhejiang China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

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.

关键词： Image coding Wavelet transforms Transforms Discrete wavelet transforms Feature extraction Wavelet analysis Wavelet domain compression algorithms Frequency-domain analysis Collaborative intelligence

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An Efficient Lossless Gradient compression Technique for Distributed Training Systems

An Efficient Lossless Gradient Compression Technique for Dis...

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Data compression Conference (DCC)

作者： Shiyan Yi Xinlin Wei Libo Lin Peng Zhang Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Peng Cheng Laboratory Shenzhen China Peking University Advanced Institute of Information Technology Hangzhou China

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

Distributed training improves the accuracy of large models by scaling parameter sizes. However, this scalability introduces communication bottlenecks between nodes. Gradient compression can alleviate these bottlenecks, but most existing methods are lossy, potentially compromising convergence and accuracy. This paper proposes a novel lossless compression algorithm for the floating-point gradient data, significantly improving compression efficiency compared to traditional lossless float compression algorithms while overcoming the shortcomings of lossy compression. First, new distance-run-length encoding and exponent prediction encoding using dynamic Huffman coding (***.) are designed based on the distinct statistical characteristics of zero and non-zero gradient data, effectively representing data values and zero locations. Second, an innovative hybrid Huffman coding (***.) is introduced, combining the advantages of dynamic and static Huffman coding. It enhances compression efficiency via local data adaptability, and simultaneously improves computational efficiency and hardware implementation friendliness with multiple fixed Huffman code tables. We also present a K-means-based probability vector clustering algorithm to effectively construct multiple prebuilt Huffman code tables. To evaluate the effectiveness of the proposed compression method, we selected two different deep neural network (DNN) learning tasks: (1) image classification, using ResNet50 and VGG16 for CIFAR-100 training, and ViT for ImageNet; and (2) language modeling, employing BERT on the IMDB dataset. The results are presented in Table 1. Furthermore, we tested mixed-precision training compression using the BF16 format. Experimental results demonstrate that our method achieves a lossless compression ratio of 0.4931 on typical gradient data, representing a significant 16.61% improvement over the state-of-the-art lossless float data compression approach, ndzip.

关键词： Training Image coding Codes Accuracy Scalability Encoding Vectors Huffman coding compression algorithms Residual neural networks

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Behavior Expression Based Policy compression Scheme for Cloud-Edge Collaborative Network

Behavior Expression Based Policy Compression Scheme for Clou...

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IEEE Conference on Wireless Communications and Networking

作者： Sijia Li Weihong Wu Jiahao Li Jiang Liu Beijing University of Posts and Telecommunications Beijing China University of Electronic Science and Technology Chengdu China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The excessively long Segment Identifier (SID) list in SRv6 challenges the promotion of SRv6 network slicing in cloudedge collaborative network. In this paper, we studied the problem of packet overhead during SRv6 SID processing in SRv6 network slicing services. We first design a new mechanism called Behavior Expression Slicing (BES) mechanism to establish the feasibility of dynamically virtual address allocation as well as SID compression based on adjacency information. Then we propose an algorithm to optimize compression efficiency in proposed BES mechanism. The simulation results indicate that the compression rate of the proposed mechanism and algorithm can reach over 75%.

关键词： 6G mobile communication Cloud computing Federated learning Network slicing Heuristic algorithms Simulation Collaboration Resource management compression algorithms

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Irreversible compression of Medical Images with a Rigorous Error Bound

Irreversible Compression of Medical Images with a Rigorous E...

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Data compression Conference (DCC)

作者： Basar Koc Ziya Arnavut Hüseyin Koçak Dept. of Computer Science Stetson University DeLand FL USA Dept. of Computer Science SUNY Fredonia Fredonia NY USA Dept. of Computer Science University of Miami Coral Gables FL USA

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

For the archiving of mammograms, the FDA requires lossless image compression. Using near-lossless compressors for temporary examination of medical images could be advantageous “if the interpreting physician deems that acceptable”. However, the FDA [1] states, “we do not believe there is consensus on what loss of information is acceptable”. To address this concern, we present a new near-lossless compression algorithm for grayscale medical images that guarantees a minimum PSNR value.

关键词： Image coding Data compression Medical services Gray-scale Mammography Compressors compression algorithms

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Evaluation of Trade-Off Between compression Ratio and Hardware Cost for Adaptive Bandwidth compression Hardware Platform

Evaluation of Trade-Off Between Compression Ratio and Hardwa...

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IEEE Cool Chips

作者： Tomohiro Ueno Kaito Kitazume Masato Kiyama Kazutomo Yoshii Kento Sato Norihisa Fujita Ryohei Kobayashi Taisuke Boku Kentaro Sano Center for Computational Science Riken Kobe Japan Degree Programs in Systems and Information Engineering University of Tsukuba Tsukuba Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Mathematics and Computer Science Argonne National Laboratory Lemont IL USA Center for Computational Sciences University of Tsukuba Tsukuba Japan Supercomputing Research Center Institute of Integrated Research Institute of Science Tokyo Kanagawa Japan

ISBN: (数字)9798331534844

ISBN: (纸本)9798331534851

Hardware-based, high-throughput data compression is a promising approach to reduce data movement costs in largescale systems and networks, thereby improving overall performance and power efficiency. However, since a data compression algorithm is only effective if it is suitable for the characteristics of the target data, data compression hardware with a fixed algorithm is unrealistic for use in general-purpose environments. To address this challenge, we have been researching adaptive bandwidth compression (ABC) hardware that flexibly provides an effective algorithm depending on the input data. This paper presents the design and the Chisel-based implementation of the ABC hardware platform for encoding compressed data and generating output blocks, as well as introducing a quantization parameter to reduce the circuit area. Our evaluation shows that the proposed quantization parameter can not only reduce the hardware cost, but also control the trade-off between the effective compression ratio and the hardware cost. In addition, based on the evaluation results, we discuss the design optimization of the ABC hardware platform.

关键词： Performance evaluation Costs Quantization (signal) Data compression Bandwidth Hardware Space exploration Resource management compression algorithms Field programmable gate arrays

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Considerations and algorithms for compression of Sets

Considerations and Algorithms for Compression of Sets

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Data compression Conference (DCC)

作者： Larsson, N. Jesper IT Univ Copenhagen DK-2300 Copenhagen Denmark

ISBN: (纸本)9781467360371

We consider compression of unordered sets of distinct elements, focusing particularly on compressing sets of fixed-length bit strings in the presence of statistical information. We address previous work, and outline a novel compression algorithm that allows transparent incorporation of various estimates for probability distribution. Experiments allow the conclusion that set compression can benefit from incorporating statistics, using our method or variants of previously known techniques.

关键词： compression compression algorithms algorithms SETTING statistical information variants fixed length Unordered

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Alternative algorithms Involving Alphabetical Coding and Limited Length Encoding in Lossless Data compression of Text Files

Alternative Algorithms Involving Alphabetical Coding and Lim...

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International Symposium on Fundamentals of Electrical Engineering (ISFEE)

作者： Radescu, Radu Univ Politehn Bucuresti Fac Elect Telecommun & Informat Technol Appl Elect & Informat Engn Dept 1-3 Iuliu Maniu Blvd RO-061071 Bucharest Romania

ISBN: (纸本)9781467395755

Coding algorithms are generally aimed at minimizing the output code length encoding speed once the code has been designed. Moreover, most codes use a binary alphabet. This paper examines other issues related to coding, such as additional constraints imposed on the channel. Code generation will be considered where there is a limit on code words. Limits the application of such systems is a practical example of data compression where fast decoding is essential. When all code words correspond to a single word in memory (usually 32 bits, but there are situations that take 64-bit) can be used canonical decoding. If the deadline cannot be guaranteed, however, required the use of slower decoding methods. This paper also deals with the alphabetic code generation, where lexicographic arrangement of words by their code symbols must correspond to the original order in which the symbols were taken coding system. When an alphabetic code is used to compress a database that can be sorted in the same, order they would have had if the database records were first decompressed and then sorted. It also corresponds to alphabetic code trees binary search trees, which have applications in a wide variety of search problems. Assumption that the symbols are sorted by probability is not suitable for this scenario. The problem of finding codes for non-binary alphabets channel will be examined in detail. The subsequent experimental results cover the problem of alphabetic coding and of limited length coding.

关键词： Data compression Source coding compression algorithms Adaptive coding Text processing

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Pattern Matching Based algorithms for Graph compression 4

Pattern Matching Based Algorithms for Graph Compression

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4th IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

作者： Chatterjee, Amlan Shah, Rushabh Jitendrakumar Sen, Soumya Calif State Univ Dominguez Hills Dept Comp Sci Carson CA 90747 USA Univ Calcutta AK Choudhury Sch Informat Technol Kolkata WB India

ISBN: (纸本)9781538676387

Graphs can be used to represent a wide variety of data belonging to different domains. Graphs can capture the relationship among data in an efficient way, and have been widely used. In recent times, with the advent of Big Data, there has been a need to store and compute on large data sets efficiently. However, considering the size of the data sets in question, finding optimal methods to store and process the data has been a challenge. Therefore, we study different graph compression techniques and propose novel algorithms to do the same in this paper. Specifically, given a graph G = (V, E), where V is the set of vertices and E is the set of edges, and vertical bar V vertical bar = n, we propose techniques to compress the adjacency matrix representation of the graph. Our algorithms are based on finding patterns within the adjacency matrix data, and replacing the common patterns with specific markers. All the techniques proposed here are lossless compression of graphs. Based on the experimental results, it is observed that our proposed techniques achieve almost 70% compression as compared to the adjacency matrix representation. The results show that large graphs can be efficiently stored in smaller memory and exploit the parallel processing power of compute nodes as well as efficiently transfer data between resources.

关键词： Graph compression Patterns Quadtree Big Data compression compression algorithms

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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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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

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

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

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.

关键词： Cloud computing Costs Scalability Distributed databases Computer architecture Stability analysis Partitioning algorithms Resource management compression algorithms System implementation

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Two-Stage Debanding Method Based on cGAN Network

Two-Stage Debanding Method Based on cGAN Network

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Hui-Shen Chang Chang Hong Lin Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Banding artifacts, marked by the staircase-like contours in smooth areas, are mainly caused by quantization or compression algorithms and can severely degrade image quality and structure. Traditional debanding methods, such as the dithering quantization, are limited by fixed parameters and struggle with large regional artifacts. Recent techniques using conditional generative adversarial networks (cGANs) aim to address these issues across entire images. However, without proper guidance, cGANs can be disrupted by color distortions in non-flat regions, leading to unnatural colors in flat areas. To overcome this, we propose a two-stage network: first, a flat area segmentation network generates a mask for flat regions, which then guides the debanding network. This approach effectively removes banding in flat areas while avoiding unnatural colors and preserving details in non-flat regions. Our method demonstrates substantial improvements both visually and quantitatively over existing techniques.

关键词： Measurement Image quality Image segmentation Quantization (signal) Image color analysis Generative adversarial networks Distortion Consumer electronics compression algorithms

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