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

共 16774 条记录，以下是741-750 订阅

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ENHANCED COLOR PALETTE MODELING FOR LOSSLESS SCREEN CONTENT compression 49

ENHANCED COLOR PALETTE MODELING FOR LOSSLESS SCREEN CONTENT ...

引用

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

作者： Och, Hannah Uddehal, Shabhrish Reddy Strutz, Tilo Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Erlangen Germany Coburg Univ Appl Sci & Arts Dept Elect Engn & Comp Sci Coburg Germany

ISBN: (纸本)9798350344868;9798350344851

Soft context formation is a lossless image coding method for screen content. It encodes images pixel by pixel via arithmetic coding by collecting statistics for probability distribution estimation. Its main pipeline includes three stages, namely a context model based stage, a color palette stage and a residual coding stage. Each subsequent stage is only employed if the previous stage can not be applied since necessary statistics, e.g. colors or contexts, have not been learned yet. We propose the following enhancements: First, information from previous stages is used to remove redundant color palette entries and prediction errors in subsequent stages. Additionally, implicitly known stage decision signals are no longer explicitly transmitted. These enhancements lead to an average bit rate decrease of 1:07% on the evaluated data. Compared to VVC and HEVC, the proposed method needs roughly 0.44 and 0.17 bits per pixel less on average for 24-bit screen content images, respectively.

关键词： soft context formation screen content compression probability distribution modeling

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data-Efficient Adaptive Global Pruning for Convolutional Neural Networks in Edge Computing

Data-Efficient Adaptive Global Pruning for Convolutional Neu...

引用

ieee International conference on Communications (ieee ICC)

作者： Gao, Zhipeng Sun, Shan Mo, Zijia Rui, Lanlan Yang, Yang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781538674628

Deep convolutional neural networks are hindered from empowering resource-constrained devices due to their demanding computational and storage resources. Structured pruning effectively removes the redundant components from neural networks and obtains compact models. Previous pruning methods usually evaluate the importance of filters from a layer-wise perspective, which is deprived of global guidance. We propose an adaptive pruning algorithm based on relevance scores to evaluate the contribution of each channel by calculating its relevance score from the back-propagation of the neural network's output. Our method identifies and removes channels with low contribution from a global perspective. Unlike previous methods that manually set the pruning rate for each pruning iteration, our method adaptively adjusts the pruning rate. In addition, our method performs satisfactorily with limited data for one-shot pruning in the absence of fine-tuning. The ability to obtain compact models through one-shot pruning with limited data is ideally suited for edge computing scenarios. We validate the effectiveness of our method with multiple combinations of convolutional neural networks and datasets. Our approach outperforms existing pruning methods in scenarios with limited data.

关键词： Edge computing convolutional neural networks structured pruning data-efficient model compression

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A Lossless compression Pipeline for Petabyte-scale Whole Genome Sequencing data 30

A Lossless Compression Pipeline for Petabyte-scale Whole Gen...

引用

30th International conference on High Performance Computing, data, and Analytics (HiPC)

作者： Bhat, Ajeya Chadalavada, Sai Manasa Jammula, Nagakishore Jain, Chirag Simmhan, Yogesh Indian Inst Sci Bengaluru India Synopsys Inc Irvine CA USA

ISBN: (纸本)9798350383225

Whole genome sequencing (WGS) technologies have enabled high-throughput cost-effective genome sequencing at the population scale. A single WGS instrument can sequence millions of DNA molecules simultaneously, leading to the generation of massive datasets. GenomeIndia is an ongoing national project aimed at sequencing the genomes of 10,000 Indian individuals. The GenomeIndia sequencing centers are completing the generation of petabyte-scale genomic data. This has raised an urgent need for scalable lossless compression software to facilitate cost-effective storage and exchange of data. By default, each WGS file produced in the GenomeIndia project is stored in the standard unmapped BAM (uBAM) format. A uBAM file saves the DNA sequences as well as metadata associated with the sequencing experiment. We have developed an open-source software pipeline that enables parallel lossless compression and decompression of uBAM files. It produces compressed output that is approximately 5x smaller than the input uBAM files. We carefully engineered the pipeline by integrating different bioinformatics tools such as SPRING, Picard, SAMtools, and PySAM. We evaluated the parallel efficiency of our approach using thorough performance profiling and strong-scaling experiments.

关键词： Open source software

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An Evaluation of Arithmetic and Huffman Coding in data compression & Source Coding

An Evaluation of Arithmetic and Huffman Coding in Data Compr...

引用

2024 International conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Prakash, Febin Singh, Vaishali Saxena, Ashendra Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

This paper considers the trouble of records compression and supply coding by evaluating the use of arithmetic and Huffman coding. Arithmetic coding is a good, most reliable coding approach used for statistics compression. It assigns shorter message lengths to symbols that appear extra often in the statistics movement and can reach better compression ratios than maximum different coding strategies. Huffman coding then uses a Tree information shape to manner the source facts and assigns a unique binary code to every symbol within the facts. It is quicker and easier to put in force than arithmetic coding and yields exact average compression ratios. Each coding strategy is compared and discussed in-depth in phrases of compression ratio overall performance, velocity of encode/decode, and complexity. From the assessment and dialogue, it's miles concluded that mathematics coding is a slightly better desire for records compression/supply coding than Huffman coding, with overall higher performance and compression ratio. In contrast, Huffman's coding change into discovered to be quicker and less complicated. © 2024 ieee.

关键词： compression easier lengths quicker strategy

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SCNET: SPARSE compression NETWORK FOR MUSIC SOURCE SEPARATION 49

SCNET: SPARSE COMPRESSION NETWORK FOR MUSIC SOURCE SEPARATIO...

引用

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

作者： Tong, Weinan Zhu, Jiaxu Chen, Jun Kang, Shiyin Jiang, Tao Li, Yang Wu, Zhiyong Meng, Helen Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Skywork AI PTE LTD Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Deep learning-based methods have made significant achievements in music source separation. However, obtaining good results while maintaining a low model complexity remains challenging in super wide-band music source separation. Previous works either overlook the differences in subbands or inadequately address the problem of information loss when generating subband features. In this paper, we propose SCNet, a novel frequency-domain network to explicitly split the spectrogram of the mixture into several subbands and introduce a sparsity-based encoder to model different frequency bands. We use a higher compression ratio on subbands with less information to improve the information density and focus on modeling subbands with more information. In this way, the separation performance can be significantly improved using lower computational consumption. Experiment results show that the proposed model achieves a signal to distortion ratio (SDR) of 9.0 dB on the MUSDB18-HQ dataset without using extra data, which outperforms state-of-the-art methods. Specifically, SCNet's CPU inference time is only 48% of HT Demucs, one of the previous state-of-the-art models.

关键词： Music separation frequency domain subband sparse compression

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Improving Prediction-Based Lossy compression Dramatically via Ratio-Quality Modeling 38

Improving Prediction-Based Lossy Compression Dramatically vi...

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38th ieee International conference on data Engineering (ICDE)

作者： Jin, Sian Di, Sheng Tian, Jiannan Byna, Suren Tao, Dingwen Cappello, Franck Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA USA

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Error-bounded lossy compression is one of the most effective techniques for reducing scientific data sizes. However, the traditional trial-and-error approach used to configure lossy compressors for finding the optimal trade-off between reconstructed data quality and compression ratio is prohibitively expensive. To resolve this issue, we develop a general-purpose analytical ratio-quality model based on the prediction-based lossy compression framework, which can effectively foresee the reduced data quality and compression ratio, as well as the impact of lossy compressed data on post-hoc analysis quality. Our analytical model significantly improves the prediction-based lossy compression in three use-cases: (1) optimization of predictor by selecting the best-fit predictor;(2) memory compression with a target ratio;and (3) in-situ compression optimization by fine-grained tuning error-bounds for various data partitions. We evaluate our analytical model on 10 scientific datasets, demonstrating its high accuracy (93.47% accuracy on average) and low computational cost (up to 18.7x lower than the trialand-error approach) for estimating the compression ratio and the impact of lossy compression on post-hoc analysis quality. We also verify the high efficiency of our ratio-quality model using different applications across the three use-cases. In addition, our experiment demonstrates that our modeling-based approach reduces the time to store the 3D RTM data with HDF5 by up to 3.4x with 128 CPU cores over the traditional solution.

关键词： Analytical models Solid modeling Three-dimensional displays data integrity Computational modeling Predictive models data engineering

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Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly 23

Sim2real Transfer Learning for Point Cloud Segmentation: An ...

引用

23rd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Wu, Chengzhi Bi, Xuelei Pfrommer, Julius Cebulla, Alexander Mangold, Simon Beyerer, Juergen Karlsruhe Inst Technol Inst Anthropomat & Robot Karlsruhe Germany Fraunhofer Inst Optron Syst Technol & Image Explo Karlsruhe Germany Fraunhofer Inst Optron Syst Technol & Image Explo Karlsruhe Germany Karlsruhe Inst Technol Wbk Inst Prod Sci Karlsruhe Germany

ISBN: (纸本)9781665493468

On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossible. A common strategy for solving this problem is to apply simulation-to-reality (sim2real) approaches with the help of simulated scenes. While the majority of current robotics vision sim2real work focuses on image data, we present an industrial application case that uses sim2real transfer learning for point cloud data. We provide insights on how to generate and process synthetic point cloud data in order to achieve better performance when the learned model is transferred to real-world data. The issue of imbalanced learning is investigated using multiple strategies. A novel patch-based attention network is proposed additionally to tackle this problem.

关键词： Point cloud compression Learning systems Computer vision Service robots Transfer learning Pipelines data models

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Efficiency Across datasets: Insights into Video compression Techniques 5

Efficiency Across Datasets: Insights into Video Compression ...

引用

5th International conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Sreeram, A. Chandra, B. Umesh Swetha, G. Sai Bagade, Shilpa Electronics and Communication Engineering GRIET Hyderabad India

ISBN: (纸本)9798350367171

In recent times, there has been notable progress in deep learning-based image compression, showcasing enhanced coding efficiency and subjective quality. However, comparatively less attention has been devoted to video compression employing deep neural networks. This paper introduces DeepLVCnet is a deep predictive video compression network that operates from end to end. With multiple framework theories, multiple scale arrangements, and a temporal context-adaptive entropy model, the method makes use of mode-selective uni-and bi-directional predictions. DeepLVCnet jointly compresses motion information and residual data through feature transformation layers within the multi-scale structure. A mode-selective architecture with uni-directional and bidirectional predictive modes is incorporated into DeepLVCnet, in contrast to earlier deep learning-based video compression techniques limited to P-frame or B-frame usage. Furthermore, utilizing temporal context information from reference frames during current frame coding, a unique temporal-context-adaptive entropy model is developed. This model is designed to enable parallel processing, offering computational and architectural advantages over autoregressive entropy models used in some CNN-based video compression methods. The overall objective is to enhance coding efficiency and subjective video enlargement quality through these innovations. © 2024 ieee.

关键词： Spatio-temporal data

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Research on Header compression Technology and Application of IP Bearer Network Evolving to SRv6 11

Research on Header Compression Technology and Application of...

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2023 ieee 11th International conference on Information, Communication and Networks, ICICN 2023

作者： Zhu, Lin Wang, Guangquan Wang, Haijun Zhang, Xueru Research Institute China Unicom Beijing China

ISBN: (纸本)9798350314014

Facing the requirement of IP Bearer Network evolving to SRv6, this paper analyzes the SRv6 header compression technology upon the application requirements of ISP IP Bearer networks, focuses on the comparison of two typical header compression schemes at present, uSID and GSID, and their header compression efficiency. data indicates that for both header compression schemes, packet size is negatively correlated with the overall trend of compression efficiency, while the number of hops is positively correlated with the overall trend of compression efficiency, the header compression efficiency can exceed 10% if the packet travels through more than 13 devices. Furthermore, the compression efficiency of uSID is higher than that of GSID when each 128 bit carries the same amount of information, and the difference of compression efficiency within 16 hops is 1.23% 2.86%. Given which, this paper provides strategies and suggestions for the application of SRv6 header compression technology in the process of network evolution based on current ISP IP Bearer Network. © 2023 ieee.

关键词： Efficiency

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Lossy compression of Gaussian Source Using Low Density Generator Matrix Codes

Lossy Compression of Gaussian Source Using Low Density Gener...

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data compression conference (DCC)

作者： Zhu, Tingting Liang, Jifan Ma, Xiao Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

ISBN: (纸本)9781665478939

We present a tandem scheme for Gaussian source compression, where a dead-zone quantizer is concatenated with a ternary low density generator matrix (LDGM) code. Both theoretical analysis and simulation results show that the LDGM codes can be universally optimal for near-lossless compression of ternary sources. Consequently, the distortion with the tandem scheme is mainly caused by the quantization, which can be negligible for high-rate quantizer. The most distinguished feature of the proposed scheme is its flexibility. The dead-zone quantizer can choose a suitable quantization level according to the distortion allowed, while the LDGM codes can adapt the code rate to approach the entropy of the quantized sequence. This is helpful to trade off between bandwith and distortion. In the meanwhile, the proposed scheme is robust when combined with the channel codes for transmission over noisy channels because of the fixed-length feature.

关键词： Codes Quantization (signal) Source coding Simulation Distortion Generators Entropy

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