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

共 16714 条记录，以下是1-10 订阅

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Proceedings of ieee data compression conference (DCC'94)

Proceedings of IEEE Data Compression Conference (DCC'94)

引用

data compression conference (DCC)

Presents the front cover of the proceedings record.

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来源：评论

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Real-World Evaluation of Compressed Sensing for data compression in Smart Grids

Real-World Evaluation of Compressed Sensing for Data Compres...

引用

2025 ieee Texas Power and Energy conference, TPEC 2025

作者： Varnousfaderani, Sepideh Alaei Andani, Mansour Taheri Yousefpour, Mohammad Alaei, Sahar Andani, Majid Taheri Azad University Iran Arizona State University United States Data Engineer United States Software Engineer United States

ISBN: (纸本)9798331541125

The smart grid integrates digital technologies for efficient power delivery and real-time monitoring, but managing its large data volumes remains challenging. This study investigates Compressed Sensing (CS) as a solution for data compression in smart grids. Using real-world data, we demonstrate that CS significantly reduces data volume while preserving signal accuracy, offering a practical approach to efficient, real-time data management. © 2025 ieee.

关键词： data compression

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APLASE: compression using Adaptive Piecewise Linear Approximation and Sparse Encoding

APLASE: Compression using Adaptive Piecewise Linear Approxim...

引用

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

作者： Saxena, Ravi Raj Prabhakar, T.V. Kuri, Joy Murthy, Chandra R. Dept. of Electronic System Engineering Indian Institute of Science Bengaluru India Dept. of Electrical Communication Engineering Indian Institute of Science Bengaluru India

ISBN: (纸本)9798350368741

This work focuses on compressing vast amounts of time series data from IoT sensors while achieving low reconstruction error, at a low compression ratio (ratio of output data size to input data size), for efficient storage and transmission. We investigate two lossy compression techniques: Adaptive Piecewise Linear Approximation (APLA) and Sparse Encoding (SE). APLA reduces data volume by leveraging the temporal correlation in time series data, while SE captures small, complex variations that APLA might miss. We identify a signal characteristic parameter, denoted by σd, which is used to derive error bounds for SE using σd, the dictionary used, and the input compression parameters. We also introduce APLASE, a novel compression technique that combines the two techniques by applying SE to the reconstruction error generated by APLA. We evaluate APLA, SE and APLASE using data from the NASA flight data recorder database consisting of recordings from 116 sensors. Our results show that APLASE consistently outperforms both APLA and SE individually, as well as the recent QoZ algorithm, particularly at lower compression ratios. © 2025 ieee.

关键词： compression Piecewise Linear Approximation Sparse Encoding

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Adaptive Lossless compression for Genomics data by Multiple (s, k)-mer Encoding and XLSTM

Adaptive Lossless Compression for Genomics Data by Multiple ...

引用

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

作者： Sun, Hui Ding, Yanfeng Yi, Liping Ma, Huidong Xie, Haonan Wang, Gang Liu, Xiaoguang College of Computer Science NBJL ICIC TMCC SysNet DISSec GTIISC Nankai University Tianjin300350 China Nanyang Technological University Nanyang639798 Singapore School of Electrical Engineering Guangxi University Nanning530004 China

ISBN: (纸本)9798350368741

Learning-based lossless compressors have been validated to have competitive advantages in genomics data (GD) compression. However, learning-based GD-dedicated compressors typically need to be pre-trained on multi-source data and then are directly used to compress another target data, we denote them as static compressors, and they often face two challenges: limited compression ratios and bad-performed generalization due to data distribution variations. To solve these problems, we propose AGDLC, a novel Adaptive Genomics data Lossless Compressor. It includes two critical designs: 1) We design a multiple (s, k)-mer mixer for extracting GD redundancy from multiple dimensions to improve compression ratios. 2) We introduce a recently popular XLSTM model as the backbone, which adaptively compresses GD while updating parameters, without pre-training, improving compression ratios and compression generalization at the same time. We compare AGDLC with 13 baselines on 7 real-world datasets, and the experimental results demonstrate that it achieves the best compression ratio with an average improvement of 2.162%-69.436%. The codes can be found at https://***/dingyanfeng/AGDLC. © 2025 ieee.

关键词： (s, k)-mer encoding genomics data learning-based compressor lossless compression XLSTM

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Multi-source data Lossless compression via Parallel Expansion Mapping and xLSTM

Multi-source Data Lossless Compression via Parallel Expansio...

引用

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

作者： Ma, Huidong Sun, Hui Yi, Liping Liu, Xiaoguang Wang, Gang College of Computer Science NBJL ICIC TMCC SysNet DISSec GTIISC Nankai University Tianjin300350 China Nanyang Technological University Nanyang639798 Singapore

ISBN: (纸本)9798350368741

Explosive growth of multi-source data (MSD) poses challenges in data transmitting and storing. Neural Network (NN)-based lossless compressors are an important type of compression approaches to alleviate these problems. However, existing NN-based lossless compressors suffer from poor compression ratio and high time cost at the same time. To address these issues, we propose a novel MSD Lossless Compressor (MSDLC) with two compression stages: 1) We propose a Parallel Expansion Mapper (PEM) to map redundant pieces in MSD into unused alphabet values, which not only compresses MSD but also saves time for the next stage's NN-based lossless compression. 2) With the mapped MSD as input, we design a NN-based lossless compressor to further improve compression ratio, where we introduce the state-of-the-art xLSTM model and design a Deep Spatial Gating Module (DSGM) as the backbone of NN. We compare MSDLC with 11 baselines on 6 real-world datasets and the results validate that MSDLC obtains the best average compression ratio and time cost. Compared with baselines, compression ratios are improved by 1.103%∼113.897%, and the time costs are improved by 41.367%∼73.891%. The codes can be available at https://***/mhuidong/MSDLC. © 2025 ieee.

关键词： Deep Spatial Gating Module Lossless compression Multi-source data Parallel Expansion Mapping xLSTM

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A Hierarchical compression Technique for 3D Gaussian Splatting compression

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

引用

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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Optimizing Bandwidth Utilization Through Word Based compression in Main Memories 38

Optimizing Bandwidth Utilization Through Word Based Compress...

引用

38th International conference on VLSI Design, VLSID 2025

作者： Aswathy, N.S. Verma, Harsh Kapoor, Hemangee K. IIT Guwahati Department of CSE India

ISBN: (纸本)9798331522445

Memory compression maximizes memory capacity, lowers bandwidth requirements, and reduces energy consumption. State-of-the-art compression techniques mostly focus on enhancing memory capacity, which involves complex reasoning for memory-based data retrieval. In this paper, we propose conditional frequent word compression (Cond-FWC) to reduce bandwidth requirements and energy consumption. The proposed Cond-FWC, as its name implies, eliminates the words that appear the most frequently from the cache line to compress the block. Furthermore, the method accurately places compressed data and metadata for compression with the help of an existing rank subsetting policy. Cond-FWC yields a 16.32% performance improvement and 55% energy reduction compared to an uncompressed non-sub-ranked baseline through rank subsetting and frequent word compression. © 2025 ieee.

关键词： Bandwidth compression

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Preserving Diagnostic Quality of Image compression using Block Adaptive Models

Preserving Diagnostic Quality of Image Compression using Blo...

引用

2025 ieee International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Chadhar, Rammilan Tripathi, Satyendra Jain, Rakhee Madhyanchal Professional University School of Electronics and Communication Engineering Bhopal India Tcs Department of Research and Development Indore India Manit Department of Electronics and Communication Engineering Bhopal India

ISBN: (纸本)9798331529833

Information compression is shown to pack the size of data accumulated in advanced documents. We have proposed a block adaptive model (BAM) compression strategy to pack the size of image while keeping the decompression overhead insignificant. In versatile lossless word reference based image compression, when compression proportion is looked at among word reference based methodology. To inspect the presentation of the Lossless and Lossy compression of images and to get high pressure proportion for both lossless and lossy image compression. The fundamental intention is to give acceptable compression proportion which limits the testing existence intricacy. BAM methods produce better improvement in compression effectiveness without adding any extra usefulness or region overhead. The proposed compression strategies will be looked at with other existing techniques regarding quality. The proposed method is implementing MATLAB 2015 and calculated PSNR, MSE and CR. © 2025 ieee.

关键词： Image compression

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Lossy compression of Point Clouds Using Coding Rate Reduction Compressed Sparse Representation 5

Lossy Compression of Point Clouds Using Coding Rate Reductio...

引用

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

作者： Li, Yinghao Wang, Yanjie Wang, Wenmeng Li, Yafei Yao, Mengyuan Li, Jingke School of Cyberspace Security Zhengzhou University Henan Zhengzhou China Henan Technical College of Construction Henan Zhengzhou China

ISBN: (纸本)9798331533694

Point cloud compression is a technique that aims to address the challenge of storing and transmitting large-scale 3D data by reducing the size of point cloud data while maintaining sufficient useful information. However, the large amount of data in point clouds and their irregular spatial arrangement complicates storage and transmission. To address this issue, a novel lossy point cloud compression method is proposed in this paper. Our strategy treats point cloud compression as an iterative optimization process, where each layer performs an optimization step to achieve the sparse compression goal. An autoencoder structure is used to enhance the point cloud compression task with coding rate reduction transformer and geometric attribute information. In the encoding phase, a novel self-encoder is used to learn the compressed and sparse representation and geometric features of the point cloud to be used as a compressed embedding of the point cloud. In the decoding stage, point-wise split deconvolution is designed for upsampling the point cloud in order to better reconstruct the point cloud. Experimental results show that the proposed method achieves better reconstruction quality and geometry at the same bit rate as other state-of-the-art compression methods. © 2025 ieee.

关键词： data compression

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On-the-fly Image compression Using Overfitted Shallow CNN Autoencoders 6

On-the-fly Image Compression Using Overfitted Shallow CNN Au...

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6th ieee International conference on Image Processing, Applications and Systems, IPAS 2025

作者： El Gallab, John Ganna, Abdelwahab Kassem, Abdallah Makram, Hend Muawad, Martina Mikhael, Mina Elbatt, Tamer The American University in Cairo Computer Science and Engineering Cairo Egypt

ISBN: (纸本)9798331506520

In the era of pervasive Internet use, managing large volumes of image data becomes crucial. To mitigate storage and bandwidth costs, image compression plays a pivotal role. Traditional image compression techniques like JPEG and PNG, while widely used, may suffer from limited compression rates. In this work, we propose a novel approach using shallow Convolutional Neural Network (CNN) autoencoders for on-the-fly image compression. Our model aims to achieve high compression rates with improved image quality compared to classical methods. Additionally, it supports decompression on the CPU in realtime, making no assumptions about client-side computational resources. We present a comprehensive methodology, including architecture design, experiments, and performance metrics. Our results demonstrate the effectiveness of the proposed approach, providing a competitive alternative for online content compression and decompression that outperforms JPEG in image quality assessment metrics at 33% compression rate. We also outperform other Learned Image compression (LIC) techniques in both the decompression time and number of trainable parameters with an improvement in the order of 10-times. © 2025 ieee.

关键词： Convolutional neural networks

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