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

共 16713 条记录，以下是421-430 订阅

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Comparative Evaluation of Various Wavelet Techniques for Image compression 4

Comparative Evaluation of Various Wavelet Techniques for Ima...

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4th ieee International conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Nisa, Hidayat Un Mehraj, Samrah Parah, Shabir A. University of Kashmir Dept. of Electronics and Instrumentation Technology Srinagar India

ISBN: (纸本)9798331528171

Image compression is a critical component of digital systems, as it reduces the file size of images while preserving quality, thereby facilitating minimal storage requirements and seamless transmission. Wavelet transforms have emerged as powerful tools in image compression for providing both time and frequency information, making them particularly effective in capturing essential image features. Despite numerous advancements, there is still significant demand for image compression techniques that achieve higher compression ratios with minimal loss of quality. This paper presents a comprehensive analysis of various lossy image compression techniques, focusing on the strengths of wavelet-based methods. Techniques such as Discrete Wavelet Transform, Lifting Wavelet Transform, and Integer Wavelet Transform are examined in detail, with key metrics including Peak Signal-to-Noise Ratio (PSNR), compression ratio, and time efficiency analyzed to provide a thorough understanding of each technique's effectiveness. The timing analysis is particularly crucial for assessing the practical applicability of these methods in real-world scenarios. This study aims to offer valuable insights for developing more advanced image compression methods that meet the growing demands of modern data management systems. © 2024 ieee.

关键词： Information management

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data-Free Quantization via Pseudo-label Filtering

Data-Free Quantization via Pseudo-label Filtering

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Fan, Chunxiao Wang, Ziqi Guo, Dan Wang, Meng Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Anhui Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Quantization for model compression can efficiently reduce the network complexity and storage requirement, but the original training data is necessary to remedy the performance loss caused by quantization. The data-Free Quandensity tization (DFQ) methods have been proposed to handle the absence of original training data with synthetic data. However, there are differences between the synthetic and original training data, which affects the performance of the quantized network, but none of the existing methods considers the differences. In this paper, we propose an efficient data-free quantization via pseudo-label filtering, which is the first to evaluate the synthetic data before quantization. We design a new metric for evaluating synthetic data using self-entropy, which indicates the reliability of synthetic data. The synthetic data can be categorized with the metric into high- and low-reliable datasets for the following training process. Besides, the multiple pseudo-labels are designed to label the synthetic data with different reliability, which can provide valuable supervision information and avoid misleading training by low-reliable samples. Extensive experiments are implemented on several datasets, including CIFAR-10, CIFAR-100, and ImageNet with various models. The experimental results show that our method can perform excellently and outperform existing methods in accuracy.

关键词： data assimilation

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A Hierarchical compression Technique for 3D Gaussian Splatting compression

A Hierarchical Compression Technique for 3D Gaussian Splatti...

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

作者： Huang, He Huang, Wenjie Yang, Qi Xu, Yiling Li, Zhu Shanghai Jiao Tong University China University of Missouri-Kansas City United States

ISBN: (纸本)9798350368741

3D Gaussian Splatting (GS) demonstrates excellent rendering quality and generation speed in novel view synthesis. However, substantial data size poses challenges for storage and transmission, making 3D GS compression an essential technology. Current 3D GS compression research primarily focuses on developing more compact scene representations, such as converting explicit 3D GS data into implicit forms. In contrast, compression of the GS data itself has hardly been explored. To address this gap, we propose a Hierarchical GS compression (HGSC) technique. Initially, we prune unimportant Gaussians based on importance scores derived from both global and local significance, effectively reducing redundancy while maintaining visual quality. An Octree structure is used to compress 3D positions. Based on the 3D GS Octree, we implement a hierarchical attribute compression strategy by employing a KD-tree to partition the 3D GS into multiple blocks. We apply farthest point sampling to select anchor primitives within each block and others as non-anchor primitives with varying Levels of Details (LoDs). Anchor primitives serve as reference points for predicting non-anchor primitives across different LoDs to reduce spatial redundancy. For anchor primitives, we use the region adaptive hierarchical transform to achieve near-lossless compression of various attributes. For non-anchor primitives, each is predicted based on the k-nearest anchor primitives. To further minimize prediction errors, the reconstructed LoD and anchor primitives are combined to form new anchor primitives to predict the next LoD. Our method notably achieves superior compression quality and a significant data size reduction of over 4.5× compared to the state-of-the-art compression method on small scenes datasets. © 2025 ieee.

关键词： 3D Gaussian Splatting compression

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compression Algorithm Selection for Column-Oriented databases Based on Stacking 11

Compression Algorithm Selection for Column-Oriented Database...

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11th Joint International Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Tian, Fuwen Xu, Meng College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350333664

Column-oriented databases have emerged as effective solutions for handling massive amounts of data, and data compression plays a crucial role. Attribute columns are divided into blocks and stored in separate files, and records within the same attribute column often exhibit high similarity, providing favorable conditions for data compression. Researchers have proposed numerous compression algorithms, but these algorithms can only achieve optimal compression results when applied to data with specific features. Therefore, it is crucial to select the compression algorithm that performs best based on the features of the data. We propose a novel method for selecting compression algorithms using Stacking, a technique that effectively enhances the prediction ability of classification models in machine learning. Our experimental results demonstrate that the stacked model achieves an accuracy of 92.9%, surpassing other models in terms of F1-Score and Kappa coefficient. Furthermore, our compression algorithm selection method outperforms other existing methods in terms of compression performance. © 2023 ieee.

关键词： classification model column-oriented database data compression Stacking

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WD: A Sliding Window based Time Series compression algorithm 5

WD: A Sliding Window based Time Series compression algorithm

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5th International conference on Machine Learning, Big data and Business Intelligence, MLBDBI 2023

作者： Meng, Jingwen Liu, Liang Liu, Yulei Wang, Ning Nanjing University of Aeronautics and Astronautics Nuaa College of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350359930

The fast generation speed and large volume of time series data make the efficient storage and transmission of time series challenging. In addition, time series is also characterized by instability, however, many advanced time series compression algorithms are not able to resist such data fluctuations, and thus failing to achieve efficient compression in such scenarios. Therefore, for the data scenarios where the metric values change frequently, this paper proposes a sliding window-based time series compression, Window Delta. It aims to weaken the data volatility by a sliding window, so that the impact of data fluctuation on the compression results can be effectively solved. In the experimental stage, we evaluated the compression performance of Window Delta and observed a significant improvement in compression ratio compared to the state-of-the-art methods, ranging from 7% to 98%. © 2023 ieee.

关键词： Big data

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An Image Security System for Digital Private Image Exchange Using Redundant Watermarks for Authentication, Information Hiding, Tampering Detection, and data Self-Recovery with Partial Carrier Capacity Utilization 76

An Image Security System for Digital Private Image Exchange ...

引用

76th Annual ieee National Aerospace and Electronics conference (NAECON)

作者： Chaugule, Swapnil S. Megherbi, Dalila B. Univ Massachusetts Lowell Francis Coll Engn CMINDS Res Ctr Dept Elect & Comp Engn Lowell MA 01854 USA

ISBN: (纸本)9798350367638;9798350367621

The demand for secure sharing of hidden private image data on the Internet and other communication mediums is on the rise across various applications, such as distributed networked collaborative systems and multi-sensor monitoring. As Internet bandwidth increases, secure private information exchange and authentication have become paramount. In this paper, we present a blind discrete wavelet-transform watermarking and information-hiding technique where, for added security, the carrier image is known only at the sending end and is not available on the Internet. Unlike traditional approaches, the proposed scheme is redundant concerning the embedded watermarks while offering the flexibility to utilize only a carrier-partial capacity, leaving the rest of the carrier capacity for other users' needs. The paper details the process of embedding and extracting image data, blind watermarking, information hiding, tampering localization, and automatic tampering self-recovery and validation. We also demonstrate the resilience of our proposed authentication scheme to unauthorized tampering and self-recovery, including cropping, blurring, noise, pixel tampering, data compression, and image encryption.

关键词： Watermarking Information Hiding and Authentication Chaotic Map DWT Arnold Transform Tampering Recovery

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Telemetry data compression Algorithm Based on Balanced Cyclic Neural Network and Deep Learning

Telemetry Data Compression Algorithm Based on Balanced Cycli...

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2023 ieee International conference on Image Processing and Computer Applications, ICIPCA 2023

作者： Huang, Min School of Computer Sichuan Technology and Business University Chengdu620000 China

ISBN: (纸本)9798350314670

With the rapid development of telemetry and telecontrol technology, the amount of information for acquiring telemetry data is increasing, which requires a large amount of storage space when storing, a large bandwidth when transmitting, and data processing as fast as possible. Therefore, data compression technology is needed to solve this problem. The redundancy of telemetry data is also very large. Statistics show that the redundancy of data transmitted in the standard telemetry system reaches more than 90%. In order to reduce the signal space and relieve the pressure of data processing, it is necessary and important to use data compression technology in telemetry system. Considering the space performance and time performance of the lossless compression algorithm, several commonly used lossless compression algorithms are compared for telemetry noise data. The compression space performance of each algorithm still needs to be improved. Considering that the measured engineering data is distributed in a typical balanced way, we studied the anomaly detection problem of time series data with inter class balance, and proposed an anomaly detection problem based on recurrent neural network (RNN). Finally, the overall test of the developed data acquisition and compression system is carried out. The test results show that the system has completed the real-time acquisition and compression of six channels of rapidly varying parameters, and the compression ratio is more than 1.8:1. All indicators meet the design requirements. © 2023 ieee.

关键词： Redundancy

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Analysing Lossless Image compression Techniques for IoT Devices 3

Analysing Lossless Image Compression Techniques for IoT Devi...

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3rd International conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Challapalli, Kausik Kothamasu, Vivek Garikapati, Hasha Charan, Roop Sai Thota, ManiSankar Anamalamudi, Satish Srm University Ap Computer Science and Engineering Mangalagiri India

ISBN: (纸本)9798350369083

Lossless image compression is the technique now that allows to keep the quality high as long as the file size is being minimized to meet the needs of uploading or archiving [1]. The other category consists of Deflate whose shortcomings are fast speed and lower efficiency [2]. By considering this, smaller files size might never be the case but it is the best alternative for quality images and this forms part of formats like PNG and ZIP [3]. On the other hand, the BROTLI admirably shines with its extremely high compression ratios, when dealing with repeated pattern of information [4]. On the contrast, it pertains to the extremely resource intensive, which may eventually need more computing power, as the ratio is in favor of the compression [5]. Regardless of the chosen technique, the outcome remains consistent: for a compressed file whose data original size has been truncated, having a size that ranges from pixels to a file which can work on them without giving a distorted image, targeting those who need high-end images but small file sizes [6]. © 2024 ieee.

关键词： data compression ratio

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Multi-Layer Hiding Scheme for Image data Using Crypto-Stego with compression Technique 11

Multi-Layer Hiding Scheme for Image Data Using Crypto-Stego ...

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11th International conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Arwasaputra, Lugaya Mukti Hadiana, Asep Id Umbara, Fajri Rakhmat Universitas Jenderal Achmad Yani Faculty of Science and Informatics Department of Informatics Cimahi Indonesia

ISBN: (纸本)9798350355314

In this modern era, the size of image data grows significantly due to increasing data needs. However, as the data flows become faster, data security concerns should receive more attention. This paper proposes a Crypto-Stego technique to enhance image data security and reduce data size using compression technique. The size of image data is reduced using Discrete Wavelet Transform (DWT) compression. Then, the compressed secret image will be encrypted with Advanced Encryption Standard using Cipher Block Chaining (AES-CBC). Lastly, the Least Significant Bit technique is used to embed the encrypted secret image inside the cover image. The results are illustrated by numerous testing simulation achieving an average score 37.9dB in the PSNR metric and 0.96 in SSIM metric. © 2024 ieee.

关键词： compression Crypto-stego Encryption Image steganography PSNR Steganography

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Batch Selection and Communication for Active Learning with Edge Labeling 59

Batch Selection and Communication for Active Learning with E...

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59th Annual ieee International conference on Communications (ieee ICC)

作者： Croisfelt, Victor Pandey, Shashi Raj Simeone, Osvaldo Popovski, Petar Aalborg Univ Connect Sect Dept Elect Syst Aalborg Denmark Kings Coll London Dept Engn Kings Commun Learning & Informat Proc KCLIP Lab Ctr Intelligent Informat Proc Syst London WC2R 2LS England

ISBN: (纸本)9798350304060;9798350304053

Conventional retransmission (ARQ) protocols are designed with the goal of ensuring the correct reception of all the individual transmitter's packets at the receiver. When the transmitter is a learner communicating with a teacher, this goal is at odds with the actual aim of the learner, which is that of eliciting the most relevant label information from the teacher. Taking an active learning perspective, this paper addresses the following key protocol design questions: (i) Active batch selection: Which batch of inputs should be sent to the teacher to acquire the most useful information and thus reduce the number of required communication rounds? (ii) Batch encoding: Can batches of data points be combined to reduce the communication resources required at each communication round? Specifically, this work introduces Communication-Constrained Bayesian Active Knowledge Distillation (CC-BAKD), a novel protocol that integrates Bayesian active learning with compression via a linear mix-up mechanism. Comparisons with existing active learning protocols demonstrate the advantages of the proposed approach.

关键词： Active knowledge distillation Bayesian machine learning compression

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