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

共 16713 条记录，以下是171-180 订阅

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

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

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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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SuperDelta: Multiple Referenced Base Chunks Scheme for Fine-grained Deduplication Backup Storage System

SuperDelta: Multiple Referenced Base Chunks Scheme for Fine-...

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data compression conference (DCC)

作者： Tan, Haoliang Zou, Xiangyu Wan, Binzhaoshuo Gu, Zhaoquan Xia, Wen Harbin Inst Technol Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Guangdong Prov Key Lab Novel Secur Intelligence T Shenzhen Peoples R China

ISBN: (纸本)9798350385885;9798350385878

Deduplication-based techniques are popular in backup storage systems for reducing data volume. To maximize data reduction, existing fine-grained deduplication approaches not only eliminate duplicate chunks but also delta-compress non-duplicate chunks as delta relative to their similar (base) chunks. However, each chunk may have multiple similar chunks, and delta compression usually only selects one of them as the base chunk, i.e., a one-to-one scheme. This scheme benefits the restore performance because it needs to read only one (instead of multiple) base chunk in decompressing delta chunks, while it also wastes the potential compressibility among other similar chunks. In this paper, we propose SuperDelta to further exploit compressibility across multiple similar chunks and to preserve the restore performance advantage of the one-to-one scheme as much as possible. It is based on three techniques. (1) To further eliminate redundancy among similar chunks, SuperDelta applies a "Multiple Referenced Base Chunks" (MRBC) scheme instead of the one-to-one scheme. It combines several similar pairs of chunks in delta encoding to recover possibly lost compressibility in "boundary shift" problems. (2) To avoid the negative side effects of MRBC on restore performance, SuperDelta introduces a rebase scheme to rebuild simple reference paths among duplicate and similar chunks. It significantly simplifies the restore workflow, but also costs slightly more storage space because of impacting the workflow of redundancy detection. (3) To compensate for the additional storage cost, SuperDelta applies a space-recycle scheme to remove derived data when they become old while ensuring the optimized restore performance of the latest backups. Experiments on four real-world backup datasets show that SuperDelta increases the overall compression ratio by 1.05 similar to 2.40 times than the traditional one-to-one fine-grained deduplication without significantly affecting the backup and restore thro

关键词： Backup Storage compression Deduplication Delta compression

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ELECTROENCEPHALOGRAM SENSOR data compression USING AN ASYMMETRICAL SPARSE AUTOENCODER WITH A DISCRETE COSINE TRANSFORM LAYER 49

ELECTROENCEPHALOGRAM SENSOR DATA COMPRESSION USING AN ASYMME...

引用

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

作者： Zhu, Xin Pan, Hongyi Rong, Shuaiang Cetin, Ahmet Enis Univ Illinois Dept Elect & Comp Engn Chicago IL 60680 USA Northwestern Univ Machine & Hybrid Intelligence Lab Evanston IL 60208 USA

ISBN: (纸本)9798350344868;9798350344851

Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete cosine transform (DCT) layer is proposed to compress EEG signals. The encoder module of the autoencoder has a combination of a fully connected linear layer and the DCT layer to reduce redundant data using hard-thresholding nonlinearity. Furthermore, the DCT layer includes trainable hard-thresholding parameters and scaling layers to give emphasis or de-emphasis on individual DCT coefficients. Finally, the one-by-one convolutional layer generates the latent space. The sparsity penalty-based cost function is employed to keep the feature map as sparse as possible in the latent space. The latent space data is transmitted to the receiver. The decoder module of the autoencoder is designed using the inverse DCT and two fully connected linear layers to improve the accuracy of data reconstruction. In comparison to other state-of-the-art methods, the proposed method significantly improves the average quality score in various data compression experiments.

关键词： EEG signal sensor data compression asymmetrical sparse autoencoder discrete cosine transform transform domain layer

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Point Cloud Geometry compression via Density-Constrained Adaptive Graph Convolution

Point Cloud Geometry Compression via Density-Constrained Ada...

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data compression conference (DCC)

作者： Wang, Dan Wang, Jin Shi, Yunhui Ling, Nam Yin, Baocai Beijing Univ Technol Beijing 100124 Peoples R China Santa Clara Univ Santa Clara CA 95053 USA

ISBN: (纸本)9798350347951

Recently, point-based point cloud geometry compression has attracted great attention due to its superior performance at low bit rates. However, lacking an efficient way to represent the local geometric correlation well, most existing methods [1, 2, 3] can hardly extract fine local features accurately. Thus it's difficult for them to obtain high-quality reconstruction of local geometry of point clouds. © 2023 ieee.

关键词： Geometry

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compression of Respiratory Sound Signals via TranscoderQVAE

Compression of Respiratory Sound Signals via TranscoderQVAE

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2024 Biomedical Circuits and Systems conference

作者： Zhai, Jinglei Zhao, Wenfeng Sun, Biao Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China SUNY Binghamton Dept Elect & Comp Engn Binghamton NY USA

ISBN: (纸本)9798350354966;9798350354959

Respiratory diseases are one of the most common causes of death worldwide. At present, digital stethoscopes are valuable tools for diagnosing respiratory diseases, but they face limitations in storage, computation, and transmission capabilities. Therefore, developing respiratory sound compression algorithms is crucial. In this work, we propose TranscoderQVAE (TQVAE). TQVAE adopts the idea of variational autoencoder, non-uniform quantizer and Transformer to perform compression and recovery tasks on respiratory sounds Based on the concept of compressive sensing, we use an encoder to reduce data points and a non-uniform quantizer to compress the signal to 4 bits and stored which can reduce the storage space. Finally, we use a simplified Transformer as a decoder named Transcoder to recover signals. Meanwhile, a denoising algorithm based on wavelet transformation is applied to the recovered signal to remove noise. Through performing experiments of 3554 pieces of data, the correlation coefficient between the recovered signal and the raw signal can reach 0.98. And the compression ratio can reach up to 256. Other evaluation metrics are also terrific. The algorithm execution time is less than 13 seconds per signal.

关键词： respiratory sounds compressive sensing variational autoencoder non-uniform quantizer Transformer network

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RNA secondary structures: from ab initio prediction to better compression, and back

RNA secondary structures: from ab initio prediction to bette...

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data compression conference (DCC)

作者： Onokpasa, Evarista Wild, Sebastian Wong, Prudence W. H. Univ Liverpool Liverpool Merseyside England

ISBN: (纸本)9798350347951

In this paper, we use the biological domain knowledge incorporated into stochastic models for ab initio RNA secondary-structure prediction to improve the state of the art in joint compression of RNA sequence and structure data (Liu et al., BMC Bioinformatics, 2008). Moreover, we show that, conversely, compression ratio can serve as a cheap and robust proxy for comparing the prediction quality of different stochastic models, which may help guide the search for better RNA structure prediction models. Our results build on expert stochastic context-free grammar models of RNA secondary structures (Dowell & Eddy, BMC Bioinformatics, 2004;Nebel & Scheid, Theory in Bio-sciences, 2011) combined with different (static and adaptive) models for rule probabilities and arithmetic coding. We provide a prototype implementation and an extensive empirical evaluation, where we illustrate how grammar features and probability models affect compression ratios.

关键词： grammar based compression RNA secondary structure prediction

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Residual Block Fusion in Low Complexity Neural Network-Based In-loop Filtering for Video compression

Residual Block Fusion in Low Complexity Neural Network-Based...

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data compression conference (DCC)

作者： Shao, Tong Shingala, Jay N. Shyam, Ajay Yin, Peng Suneja, Ajat Badya, Siddarth P. Arora, Arjun McCarthy, Sean Dolby Labs Inc 432 Lakeside Dr Sunnyvale CA 94085 USA Ittiam Syst Pvt Ltd Consulate-11 Richmond Rd Bengaluru 560025 India

ISBN: (纸本)9798350385885;9798350385878

In this paper, a novel low complexity residual block fusion (RBF) based split luma chroma architecture is proposed to improve coding efficiency of neural network-based in-loop filter in video compression. The residual block in this architecture consists of a 1x1 convolution layer with wide activation and a regular 3x3 convolutional layer decomposed into 1x1 pointwise convolutions and 1x3/3x1 separable convolutions via Canonical Polyadic (CP) decomposition to reduce complexity. By adjusting the location of the skip connection in each residual block, the fusion of adjacent 1x1 pointwise convolutions is performed. The RBF backbone consists of a new wide activation that directly starts with PReLU and is followed by a 1x1 convolution, while the 1x1 layers after CP decomposition are fully fused. This new fusion design reduces the complexity from 17.05 kMac/Pixel to 16.56 kMac/Pixel and the number of convolutional layers by 13%. The experimental results show that new RBF architecture's BD-Rate is {-0.11%, -0.31%, -0.33%} under All Intra (AI) and {-0.14%, 0.66%, -1.56%} under Random Access (RA) compared to existing residual block design, while the BD-Rate of the proposed RBF loop filer compared to VTM anchor is {-4.77%, -9.14%, -9.13%} under AI and {-5.46%, -9.31%, -9.20%} under RA. The actual decoding time is reduced by around 5% after residual block fusion. The BD-Rate and kMac/Pixel plot also shows superior trade-off between complexity and coding gain compared to state-of-the-art filters.

关键词： Convolution

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Compressing 3D Gaussian Splatting via a Generalizable Neural Coder

Compressing 3D Gaussian Splatting via a Generalizable Neural...

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2024 conference on Visual Communications and Image Processing

作者： Zhang, Junteng Chen, Tong Zhu, Hao Wang, Dong Ding, Dandan Ma, Zhan Nanjing Univ Nanjing Peoples R China OPPO Inc Nanjing Peoples R China Hangzhou Normal Univ Hangzhou Peoples R China

ISBN: (纸本)9798331529543;9798331529550

As a promising technique for 3D representation, 3D Gaussian Splatting (3DGS) offers fast rendering speed and high fidelity while generating large data volumes. This challenges storage and transmission, so an efficient compression solution is required. Existing implicit methods require pre-scene optimization (online), leading to a long optimization time. By contrast, this paper regards the 3DGS as a point cloud and pre-trains a generalizable (offline) neural coder for compression. After obtaining the 3DGS representation, we focus on the data compression process, which is friendly to applications already equipped with a PCC codec. The neural coder employed is extended from a typical AI-based point cloud compression method, which uses a multiscale and multistage framework to exploit spatial correlations across scales and stages for conditional coding. Experimental results show that our method significantly outperforms existing 3DGS representations without compromising fidelity, achieving more than 39x and 6.8x compression ratio compared to the original 3DGS and SOTA Scaffold-GS, respectively. More importantly, our approach does not require additional time to optimize the compression model.

关键词： 3D Gaussian Splatting compression Conditional Coding

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Joint compression Ratio and User Association for Multi-cell Probabilistic Semantic Communication

Joint Compression Ratio and User Association for Multi-cell ...

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ieee/CIC International conference on Communications in China (ICCC)

作者： Li, Lingyuxiu Dai, Jianxin Yang, Zhaohui Yang, Qianqian Huang, Chongwen Zhang, Zhaoyang Nanjing Univ Posts & Telecommun Sch Sci Nanjing Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Prov Key Lab Info Proc Commun & Netw IPCAN Hangzhou Peoples R China

ISBN: (纸本)9798350377675;9798350377682

In this paper, the sum rate maximization problem for a multi-cell downlink probalistic semantic communication (PSCom) system is investigated. In the considered model, each base station (BS) employs semantic information extraction techniques to compress raw data before transmitting it to the user. At the receiving side, the user recovers the compressed information through a knowledge base shared with the served BS. Our model encapsulates large data within a comprehensive knowledge graph and leverages a shared probabilistic graph between users and BSs for efficient semantic compression. Taking into account the total power budget, semantic resource limitation constraints, and user association parameters, the sum semantic rate maximization problem is formulated through jointly optimizing the semantic compression ratio, transmit power, and user association. This joint optimization problem balances transmission efficiency and computational complexity. To tackle this optimization problem, an iterative algorithm is proposed where the optimal solution for a specific user's semantic compression ratio is determined in each iteration. Numerical results demonstrate the effectiveness of our approach.

关键词： Semantic communication knowledge base sum rate maximization

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Using Streaming Telemetry Transport Protocol to Send Time Synchronized Real Time data at Scale

Using Streaming Telemetry Transport Protocol to Send Time Sy...

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International conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)

作者： Carroll, J. Ritchie Lackner, Christoph Robertson, F. Russell Murphy, Sean Grid Protect Alliance Chattanooga TN 37402 USA PingThings Inc Washington DC USA

ISBN: (纸本)9798350312874;9798350312881

This paper describes the Streaming Telemetry Transport Protocol (STTP) used for streaming real-time synchronized measurements. The paper provides background on the protocol, and compression algorithms used and describes results obtained from streaming large amounts of synchronized data suing STTP. The focus of this paper is to describe and quantify the performance of STTP when sending large amounts of time synchronized data across networks.

关键词： Streaming data STTP ieee 2664 compression Throughput

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