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

共 16774 条记录，以下是811-820 订阅

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Improving Image compression Performance by Spatial-Channel Context Adaptive Model 4

Improving Image Compression Performance by Spatial-Channel C...

引用

4th International conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Wang, Hao Wang, Huifen Xue, Junda Lu, Enmin Wang, Hanming Wu, Zijun Song, Yunlong Institute of Big Data and Artificial Intelligence China Telecom Corporation Limited Beijing Research Institute Beijing China

ISBN: (纸本)9798350314496

The significance of enhancing image compression efficiency for machine vision, analysis, and comprehension tasks has gained increasing recognition. In response to this need, we propose and implement a novel method called ELIC (Efficient Learned Image compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding) to achieve high compression efficiency. Our method is evaluated on the classic OpenImage V6 Common Test Condition (CTC) eval datasets, and its performance is compared to baseline methods for machine vision tasks. The results of our study demonstrate a substantial enhancement in compression efficiency, suggesting that the ELIC technique holds promise for pushing the boundaries of state-of-the-art visual compression for vision tasks. Furthermore, we believe that our approach can promote the application of learning-based image compression. © 2023 ieee.

关键词： Image compression

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Research on the Preprocessing Method of Switchgear Operation data Based on Clustering Algorithm* 7

Research on the Preprocessing Method of Switchgear Operation...

引用

7th ieee International Electrical and Energy conference, CIEEC 2024

作者： Cao, Na Li, Xiaowen Guo, Bing Song, Aolong Gao, Yuan Xiu, Xianlong Shandong University of Science and Technology Dept. College of Electrical Engineering and Automation Qingdao China Shandong Jinrui Electric Company Dept. Technical Department. Taian China

ISBN: (纸本)9798350359558

Considering the high requirements for the safety of switchgear equipment and the extremely large amount of data in the process of equipment condition monitoring, a preprocessing method based on K-means clustering of switchgear equipment operation data is proposed. The method firstly selects evaluation parameters from multiple fault problem aspects to ensure the reliability of condition monitoring;then it considers the characteristics of different data volumes, quantifies the non-data volume information, and forms a unified data format;finally, it concentrates the massive operation data through K-means clustering algorithm. Using this method to analyze the switchgear operation data of a city in North China, an accuracy index is introduced to evaluate the performance of the algorithm in the clustering task, and the experimental results show that the method can realize the normalization and effective compression of switchgear equipment operation data. © 2024 ieee.

关键词： Condition monitoring

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DAGC: data-Aware Adaptive Gradient compression 42

DAGC: Data-Aware Adaptive Gradient Compression

引用

42nd ieee International conference on Computer Communications, INFOCOM 2023

作者： Lu, Rongwei Song, Jiajun Chen, Bin Cui, Laizhong Wang, Zhi Tsinghua Shenzhen International Graduate School Tsinghua-Berkeley Shenzhen Institute China Dalian University of Technology School of Software Technology China Harbin Institute of Technology Shenzhen China Shenzhen University College of Computer Science and Software Engineering China China Peng Cheng Laboratory China

ISBN: (纸本)9798350334142

Gradient compression algorithms are widely used to alleviate the communication bottleneck in distributed ML. However, existing gradient compression algorithms suffer from accuracy degradation in Non-IID scenarios, because a uniform compression scheme is used to compress gradients at workers with different data distributions and volumes, since workers with larger volumes of data are forced to adapt to the same aggressive compression ratios as others. Assigning different compression ratios to workers with different data distributions and volumes is thus a promising solution. In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers;This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios1 to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradient compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication. © 2023 ieee.

关键词： Nonlinear programming

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Analysis of Lossless Compressors Applied to Integer and Floating-Point Astronomical data

Analysis of Lossless Compressors Applied to Integer and Floa...

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data compression conference (DCC)

作者： Maireles-Gonzalez, Oscar Bartrina-Rapesta, Joan Hernandez-Cabronero, Miguel Serra-Sagrista, Joan Univ Autonoma Barcelona Cerdanyola Valles Bellaterra 08193 Spain

ISBN: (纸本)9781665478939

In this work, lossless compression algorithms are evaluated on a variety of real, current astronomical images. The test dataset comprises raw (integer) and processed (floating-point) images of discrete and extensive astronomical objects, captured by spatial or terrestrial telescopes. compression techniques herein analyzed are chosen to be representative of the most recent algorithms devised for astronomical data, as well as the most commonly employed compressors employed in real observatories. Experimental results suggest that coding techniques such as RICE and HCOMPRESS, typically employed in world-class observatories such as Roque de los Muchachos, do not produce the best possible lossless compression results. Instead, JPEG-LS, LZMA and NDZIP yield the best compression ratio results for 16-bit data (2.72), floating-point data (2.38) and radio data (1.81), respectively. Therefore, the efficiency with which data are stored and transmitted by these observatories could be significantly improved by selectively employing the aforementioned algorithms.

关键词： Observatories Image coding data compression Compressors Encoding compression algorithms

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Point Cloud compression Method Based on Curvature Grading and Inter-point compression for Industrial Products 15

Point Cloud Compression Method Based on Curvature Grading an...

引用

15th International conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

作者： Wang, Lei Huang, Wei Chen, Yiping Chen, Ran Nanjing University of Posts and Telecommunications School of Computer Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350308693

The traditional point cloud compression methods fail to meet the compression requirements of complex surface point cloud files in the industrial field. This paper proposes a point cloud compression method suitable for industrial products with defective and complex surfaces, which compresses the defect and non-defect parts separately using different compression approaches. A curvature-based compression method is designed for the defect points, and an inter-point compression method is developed for the non-defect part, achieving compression of both components of the point cloud. The experimental results show the advantages of the proposed compression method through a comparative analysis of time efficiency, compression ratio, and reconstruction accuracy for industrial point cloud compression. © 2023 ieee.

关键词： Grading

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NoCASC: A Novel Optimized Cost-Complexity Pruning for AdaBoost Model compression on Edge Computing-Based Smart Lighting 13

NoCASC: A Novel Optimized Cost-Complexity Pruning for AdaBoo...

引用

13th ieee International conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Putrada, Aji Gautama Abdurohman, Maman Perdana, Doan Nuha, Hilal Hudan Telkom University School of Computing Bandung Indonesia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350368086

K-nearest neighbor (KNN) is an effective method for smart lighting control that promotes user comfort through the classification-integrated moving average (CIMA) method. However, KNN is a lazy learner, where if the training data size is large, it will affect the KNN model size, which contradicts the principles of edge computing-based smart lighting (EdgeSL). Our research aim is to use cost-complexity pruning as a compression model for adaptive boosting (AdaBoost) in the EdgeSL environment. In the first step, we carried out an analysis of the smart lighting data that we had used in several previous studies. We designed AdaBoost and implemented a Pareto front grid search for AdaBoost hyperparameter optimization. Then, we compared the performance of AdaBoost and KNN in smart lighting control prediction. In the last step, we leverage the cost-complexity pruning for model compression on AdaBoost. We use a novel method called normalized-complemented accuracysize curve (NoCASC) to balance between reducing the model size and maintaining accuracy loss. The test results show that grid search with Pareto front found that max-depth =9 and n-estimators =30 were the optimum hyperparameters for AdaBoost on the CIMA dataset. Our optimal AdaBoost model has a better recall than the legacy method, with a value of 0.9999. The size of the AdaBoost model after cost-complexity pruning is 281 kB. This size is 2.70 × smaller than the original AdaBoost, then 6.93 × smaller than KNN. AdaBoost with cost-complexity pruning is the most optimal model for CIMA in EdgeSL. © 2024 ieee.

关键词： Adaptive boosting

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Does Deflate Outperform Huffman Lossless compression? 9

Does Deflate Outperform Huffman Lossless Compression?

引用

9th ieee International conference on Computing, Engineering and Design, ICCED 2023

作者： Sutomo, Vincent Dava Cenon, Neo Kang, Aloysius Bandjar, I. Dewa Agung Kayana Abhipraya Putra Kurniadi, Felix Indra Riccosan Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350370126

Lossless compression is a compression technique used to store data with higher quality compared to its counterpart, lossy compression. This study explores two specific lossless compression techniques to investigate and address the research questions that motivated this experiment. Deflate, an algorithm derived from Huffman and LZ77, is one of the techniques under examination. This algorithm is particularly intriguing due to the amalgamation of the two preceding compression techniques. Consequently, a crucial inquiry arises: does Deflate surpass its original algorithm? In this paper, Huffman and Deflate are compared to analyze whether combining algorithms yields superior or inferior results. The conducted test provides evidence supporting the superiority of Deflate over Huffman. Deflate demonstrates advantages in terms of compression ratio and, notably, compression and decompression speed. However, it is important to note that this case is not conclusively closed, as the experiment can still be extended to include other algorithms. The present paper exclusively focuses on Deflate and does not guarantee that all combination-based algorithms outperform the algorithms they incorporate. Nevertheless, this comparative analysis makes a valuable contribution to the future development of lossless compression techniques. Additionally, it fulfills the objective of understanding the factors that enable an algorithm like Deflate to effectively implement multiple compression techniques. © 2023 ieee.

关键词： Deflate Huffman code lossless compression

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Optimizing Federated Learning with Heterogeneous Edge Devices 3

Optimizing Federated Learning with Heterogeneous Edge Device...

引用

3rd International conference on Computing and Machine Intelligence (ICMI)

作者： Islam, Mohammad Munzurul Alawad, Mohammed Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

ISBN: (纸本)9798350372977;9798350372984

In the era of Internet of thing (IoT), data is massively generated by the distributed sensors. Federated learning (FL) has emerged as a privacy-preserving and secure DL framework to incorporate knowledge from data stored in different locations and train a DL model on the combined data without revealing the local data to a centralized server. This approach is limited by several challenges including the computation and communication cost. In this paper, we propose a novel FL framework that reduces the computation and communication cost through model pruning. It also, considers the device and data heterogeneity in IoT devices. This paper considers the problem of how to stochastically measure a large and complex information field (i.e., the criticality of parameters in local DL models based on local datasets) with optimally few observations. We adopt sparsity-promoting transform domain regularization and integrate compressive sensing with Bayesian learning to improve the measurement accuracy of criticality values in large-scale information fields while minimizing the overall computational efforts and time. The proposed approach is evaluated on two model architectures and two datasets. We demonstrate that our FL framework effectively addresses the heterogeneity present in local devices while surpassing the performance of state-of-the-art FL approaches, both with IID (independent and identically distributed) and non-IID (non-independent and identically distributed) data distributions.

关键词： federated learning model compression compressive sensing

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data Embedding with Dynamic Slice Quantisation in Versatile Video Coding

Data Embedding with Dynamic Slice Quantisation in Versatile ...

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2023 ieee International conference on Computing, ICOCO 2023

作者： Joan, Hau Tan, Li Peng Tew, Yiqi Tunku Abdul Rahman University of Management and Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350302684

The rising creation and consumption of digital video data have led to further issues regarding content reliability, protection, and digital rights administration. This study presents an embedding data strategy in H.266/Versatile Video Coding (VVC) compressed video streams. We are researching the utilisation of the Quantisation Parameter (QP) within a video slice for data embedding purposes, focusing on assessing its impact on the overall work, all while ensuring the preservation of high-quality video production and efficient compression. Preliminary findings indicate that the video generated by the algorithm in question may exhibit a slightly diminished quality compared to the original video, which upholds a high standard of video production quality. Furthermore, specific outcomes from video quality assessments suggest that the produced video can achieve superior quality compared to the original video. The proposed technique effectively embeds the data while minimising any noticeable impact on video quality and ensuring acceptable compression efficiency. © 2023 ieee.

关键词： Computer graphics

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Privacy-Assured and Multi-Prior Recovered Compressed Sensing for Image compression-Encryption Applications

Privacy-Assured and Multi-Prior Recovered Compressed Sensing...

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data compression conference (DCC)

作者： Huang, Hui Xiao, Di Li, Min Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)9781665478939

Compressed sensing (CS), a popular signal processing technique, can achieve compression and encryption simultaneously. Therefore, it has extension applications in various fields. However, CS is vulnerable to cryptographic attacks for its linear encoding process. To solve this problem, a permutation-diffusion structure is designed and embedded to the CS encoding process. In addition, it can increase the key space while compressing. Since the permutation-diffusion structure reduces the sparseness, superior recovery performance cannot be achieved. Therefore, the multi-prior regularization recovery strategy is designed to improve the recovery performance, where the multi-prior regularization term denotes l(1) norm, total variation (TV) and low rank. The simulation results and analyses demonstrate that the proposed encoding scheme can resist cryptographic attacks, increase the key space while compressing, and achieve 1.54dB PSNR gain on average in comparison with the existing schemes.

关键词： Image coding TV Simulation data compression Resists Signal processing Encoding

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