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

共 16713 条记录，以下是441-450 订阅

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LOW-COMPLEXITY VECTOR SOURCE CODING FOR DISCRETE LONG SEQUENCES WITH UNKNOWN DISTRIBUTIONS 49

LOW-COMPLEXITY VECTOR SOURCE CODING FOR DISCRETE LONG SEQUEN...

引用

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

作者： Woldemariam, Leah Liu, Hang Scaglione, Anna Cornell Univ Dept Elect & Comp Engn Cornell Tech Ithaca NY 14850 USA

ISBN: (纸本)9798350344868;9798350344851

In this paper, we propose a source coding scheme that represents data from unknown distributions through frequency and support information. Existing encoding schemes often compress data by sacrificing computational efficiency or by assuming the data follows a known distribution. We take advantage of the structure that arises within the spatial representation and utilize it to encode run-lengths within this representation using Golomb coding. Through theoretical analysis, we show that our scheme yields an overall bit rate that nears entropy without a computationally complex encoding algorithm and verify these results through numerical experiments.

关键词： data compression run-length coding vector coding bit analysis

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Gradient Linear Model for Chroma Intra Prediction

Gradient Linear Model for Chroma Intra Prediction

引用

data compression conference (DCC)

作者： Kuo, Che-Wei Li, Xinwei Xiu, Xiaoyu Jhu, Hong-Jheng Yan, Ning Wang, Xianglin Ye, Yan Chen, Jie Liao, Ru-Ling Kwai Inc San Diego CA 92103 USA Alibaba Grp Beijing Peoples R China

ISBN: (纸本)9798350347951

In Versatile Video Coding (VVC), Cross-component Linear Model (CCLM) predicts chroma samples by assuming a linear relationship between luma and chroma components. In performing CCLM for video in YUV 4:2:0 chroma format, collocated luma samples are firstly downsampled by a low-pass filter to match luma resolution with chroma, and one linear model of luma-chroma sample pairs is applied on the reconstructed luma samples to generate the predicted chroma samples. However, the low-pass downsampling procedure ignores relative spatial variations among luma samples in proximity, such as edge and gradient information. To solve this issue, a new coding technique, namely gradient linear model (GLM), is proposed for further compression efficiency exploration beyond VVC. Instead of using a low-pass filter in CCLM, the GLM utilizes high-pass gradient filters to generate the downsampled luma values. In this paper, two GLM schemes are provided with different trade-offs between coding gain and complexity, including: 1) a 2-parameter scheme that shares the CCLM module framework but replaces the downsampling filter with high-pass gradient filters;2) a 3-parameter scheme that further combines the luma gradients with the low-pass downsampled luma values. Based on the enhanced compression model (ECM-5.0) software from the joint video experts team (JVET), simulation results show that the 2-parameter GLM achieves average Bjentegaard delta-rate (BD-rate) savings of {1.01%, 1.66%, 1.81%} and {0.69%, 0.95%, 1.12%} for {Y, U, V} components under the All Intra and Random Access configurations, respectively, and the 3-parameter GLM provides {1.28%, 3.23%, 3.28%} and {0.92%, 2.19%, 2.26%} BD-rate savings for {Y, U, V} components under the All Intra and Random Access configurations, respectively. Both of the proposed GLM schemes have been adopted to the ECM software platform.

关键词： video coding intra prediction Versatile Video Coding (VVC) enhanced compression model (ECM) cross-component linear model (CCLM)

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ECG data compression Analysis by Employing Different Transforms in Orthogonal Transformation Technique 1

ECG Data Compression Analysis by Employing Different Transfo...

引用

1st International conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Kaushal, Dhwani Singh, Vivek Jain, Priyanka Delhi Technological University Department of Electronics and Communication Delhi India

ISBN: (纸本)9798350364590

Using the orthogonal transformation method, this paper presents the performance analysis of FFT, DHT, DCT, and DST for ECG data compression. When the heart muscle contracts or expands as a response to the command signals coming from electrical stimulus in its electro-conduction system, an ECG signal is generated. In today's fast-paced life, ECG data compression is essential as today's lifestyle leads to many health problems. More storage space is required for research purposes or data transmission, and data compression utilizes memory storage effectively. The performance of each transform has been recorded by calculating compression ratio (CR) to measure the compression acquired, signal-to-noise ratio (SNR) to measure signal strength to the background noise, and percent rms difference (PRD) for error measurement. The parameters have been measured using Python and has been compared with the literature. The Discrete Cosine transform performed well compared to the rest in the orthogonal transformation method with a PRD of 4.80 at CR of 5.14 and an SNR of 26.36. © 2024 ieee.

关键词： Electrocardiography

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A Comprehensive Framework of Combined Lossless Image compression Algorithm for Light-Field Hyperspectral Images 5

A Comprehensive Framework of Combined Lossless Image Compres...

引用

5th ieee International conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Anjaneya, P. Rajini, R.G. School of Electronics Engineering VIT University TamilNadu Vellore India

ISBN: (纸本)9798350338287

Light field hyperspectral imaging captures the spatial, spectral and angular information of a scene, resulting in a massive amount of high-dimensional data. The storage and transmission of this data require high bandwidth and storage capacity, which can be both time-consuming and costly. Image compression can reduce data volume without compromising image quality. Lossless compression is a difficult undertaking for light field hyperspectral images due to their high dimensionality and complexity. Existing lossless compression techniques, such as JPEG-LS and PNG, are not efficient in compressing light field hyperspectral images. These techniques either fail to achieve sufficient compression ratios or require a long encoding and decoding time. Moreover, the existing techniques are not optimized for light field hyperspectral images and do not exploit the specific characteristics of these images. Existing lossless compression techniques for hyperspectral data suffer from several limitations, including high computational complexity, low compression ratios, and loss of spectral and spatial information. Therefore, there is a need for the development of a more efficient and effective lossless compression system for hyperspectral data. In this work, we focus on to address the challenges posed by large amounts of hyperspectral data. © 2023 ieee.

关键词： Image compression

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Image compression via Alternating Least Squares

Image Compression via Alternating Least Squares

引用

2024 International conference on IT and Industrial Technologies, ICIT 2024

作者： Zeeshan, Muhammad Department of Electrical and Computer Engineering Air University Islamabad Pakistan

ISBN: (纸本)9798331504465

In numerous application areas such as medical imaging and remote sensing, image compression methods play an important role in managing data storage and transmission costs. In this paper, we present an empirical study on a low-rank approximation using the alternating least squares (ALS) method for image compression. For high-dimensional data, we demonstrate the advantage of a matrix decomposition-based implementation that avoids explicit matrix inversion applied in the direct implementation in terms of computational efficiency. The performance of the ALS method is compared with the singular value decomposition in terms of information loss, and compression ratio. © 2024 ieee.

关键词： Singular value decomposition

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Computationally-Efficient Neural Image compression with Shallow Decoders

Computationally-Efficient Neural Image Compression with Shal...

引用

ieee/CVF International conference on Computer Vision (ICCV)

作者： Yang, Yibo Mandt, Stephan Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9798350307184

Neural image compression methods have seen increasingly strong performance in recent years. However, they suffer orders of magnitude higher computational complexity compared to traditional codecs, which hinders their real-world deployment. This paper takes a step forward in closing this gap in decoding complexity by adopting shallow or even linear decoding transforms. To compensate for the resulting drop in compression performance, we exploit the often asymmetrical computation budget between encoding and decoding, by adopting more powerful encoder networks and iterative encoding. We theoretically formalize the intuition behind, and our experimental results establish a new frontier in the trade-off between rate-distortion and decoding complexity for neural image compression. Specifically, we achieve rate-distortion performance competitive with the established mean-scale hyperprior architecture of Minnen et al. (2018) at less than 50K decoding FLOPs/pixel, reducing the baseline's overall decoding complexity by 80%, or over 90% for the synthesis transform alone. Our code can be found at https: //***/mandt-lab/shallow-ntc.

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Applying Practical Parallel Grammar compression to Large-scale data

Applying Practical Parallel Grammar Compression to Large-sca...

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data compression conference (DCC)

作者： Matsushita, Masaki Inoguchi, Yasushi Japan Adv Inst Sci & Technol Asahidai 1-1 Nomi Ishikawa 9231292 Japan

ISBN: (纸本)9781665478939

Re-pair is a grammar-based compression algorithm. In the poster session of the data compression conference 2021, we propose the basic concepts of Parallel Re-pair, a parallel variant of Re-pair that achieves shorter compression time with multi-core CPUs. However, our experimental results show it achieves only 1.6 to 2.4 times faster with 32 processors. In this poster session, we propose practical implementation of Parallel Re-pair with Intel Threading Building Blocks.

关键词： Program processors data compression Grammar compression algorithms

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A Novel PV data Stream compression and Reconstruction Approach with Signal Residual Observation Matrix 14

A Novel PV Data Stream Compression and Reconstruction Approa...

引用

14th ieee International conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Guo, Huifang Geng, Longmei Li, Bin Sun, Yi You, Daning Zhang, Yuanpeng North China Electric Power University School of Electric and Electronic Engineering Beijing China State Grid Shandong Electric Power Research Institute Shandong Jinan China

ISBN: (纸本)9798350361889

At present, when user-side distributed PV participates in the multi-level aggregation regulation of power grid, it involves the collection and interaction of massive multiple heterogeneous data, and the data redundancy and data scale are constantly growing, occupying a lot of network resources. In this paper, a distributed photovoltaic data compression algorithm based on signal residual observation matrix (SROM) is proposed, which effectively reduces the data transmitted by the system. Experiments show that this method has a stable reconstruction effect under different distributed PV power generation waveforms. © 2024 ieee.

关键词： Smart power grids

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Comparative Analysis of Deep Learning Techniques for Underwater Multimedia compression 6

Comparative Analysis of Deep Learning Techniques for Underwa...

引用

6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Priyakanth, R. Simon, Judy Department of ECE SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331522667

Recent advancements in underwater multimedia compression, particularly deep learning techniques, are explored to address the increasing data demands of the Internet of Underwater Things (IoUT). Efficient compression is critical due to the limited bandwidth and greater transmission error rates in underwater acoustic channels. Deep learning models such as the UAT-SSIC scheme, which employs semantic segmentation with joint source-channel coding, and the DWT–CNN framework, which combines discrete wavelet transform with convolutional neural networks, have demonstrated superior compression performance. Advanced techniques like convolutional autoencoders (CAEs) and UCSNet leverage adaptive sampling and underwater priors to enhance image reconstruction and reduce errors. Additionally, approaches like ESPC-BCS-Net and RFD-ECNet achieve efficient compression by integrating compressed sensing and multi-scale feature extraction. These methods consistently deliver high PSNR and SSIM values, minimize spatial redundancy, and optimize energy use, demonstrating their significant potential for improving underwater communication and data processing. © 2025 ieee.

关键词： Semantic Segmentation

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An Efficient compression Method for Collaborative 3D Mapping in Confined Space with Limited Resources 2

An Efficient Compression Method for Collaborative 3D Mapping...

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2nd ieee International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Piao, Zhengquan Feng, Fuyong Dang, Ruina Wang, Zhenhao Li, Tianming Qiu, Zhewei Wang, Di China North Artificial Intelligence & Innovation Research Institute Beijing China Collective Intelligence & Collaboratioon Laboratory Beijing China

ISBN: (纸本)9798331515669

LiDAR is a key sensor for 3D mapping, and as the measurement accuracy of the sensor continues to improve, the order of magnitude and the memory required to acquire 3D point cloud data are getting larger and larger. However, due to the limited computational resources of unmanned platforms for collaborative 3D mapping in confined space, the large memory occupancy of the point cloud data will bring challenges to the subsequent transmission, storage and collaboration. In order to save storage space and improve transmission and processing efficiency, this paper expresses the 3D point cloud based on the voxel octree structure. We use cropping to remove the irrelevant point cloud information first, and further optimize the compression algorithm of the voxel octree by detecting the point cloud resolution and then setting the voxel filter in partitions. Experimental results verify the proposed method compress the point cloud as much as possible while reducing the loss of key information. © 2024 ieee.

关键词： Cloud platforms

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