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

共 16713 条记录，以下是361-370 订阅

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Efficient compression Method in Video Reconstruction Using Video Vision Transformer 13

Efficient Compression Method in Video Reconstruction Using V...

引用

13th ieee Global conference on Consumer Electronic, GCCE 2024

作者： Kobayashi, Takumi Seo, Masataka Osaka Institute of Technology Graduate School of Robotics and Design Engineering Osaka Japan

ISBN: (纸本)9798350355079

In training image generators, autoencoders (AEs) are used to compress data into latent variables. However, when attempting to reconstruct video images, learning more complex distributions is necessary, and a simple model does not provide sufficient reconstruction accuracy or require a huge data-set and training time. Therefore, we explore the possibility of using video vision transformer (ViViT) as a video reconstructor. We report the model structure and various settings suitable for using ViViT as an AE in terms of compression ratio and reconstruction loss. © 2024 ieee.

关键词： Image reconstruction

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Generalized compression and compressive search of large datasets

Generalized compression and compressive search of large data...

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2024 ieee International conference on Big data, Bigdata 2024

作者： Prior, Morgan E. Howard, Thomas J. Light, Emily Ishaq, Najib Daniels, Noah M. Tufts University Dept. of Computer Science BostonMA United States University of Rhode Island Dept. of Computer Science and Statistics KingstonRI United States

ISBN: (纸本)9798350362480

The Big data explosion has necessitated the development of search algorithms that scale sub-linearly in time and memory. While compression algorithms and search algorithms do exist independently, few algorithms offer both, and those which do are domain-specific. We present panCAKES, a novel approach to compressive search, i.e., a way to perform k-NN and ρ-NN search on compressed data while only decompressing a small, relevant, portion of the data. panCAKES assumes the manifold hypothesis and leverages the low-dimensional structure of the data to compress and search it efficiently. panCAKES is generic over any distance function for which the distance between two points is proportional to the memory cost of storing an encoding of one in terms of the other. This property holds for many widely-used distance functions, e.g. string edit distances (Levenshtein, Needleman-Wunsch, etc.) and set dissimilarity measures (Jaccard, Dice, etc.). We benchmark panCAKES on a variety of datasets, including genomic, proteomic, and set data. We compare compression ratios to gzip, and search performance between the compressed and uncompressed versions of the same dataset. panCAKES achieves compression ratios close to those of gzip, while offering sub-linear time performance for k-NN and ρ-NN search. We conclude that panCAKES is an efficient, general-purpose algorithm for exact compressive search on large datasets that obey the manifold hypothesis. We provide an open-source implementation of panCAKES in the Rust programming language. © 2024 ieee.

关键词： Large datasets

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Lossless compression of Synchro-Waveform Measurements for Smart Grid Monitoring

Lossless Compression of Synchro-Waveform Measurements for Sm...

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ieee PES conference on Innovative Smart Grid Technologies (ISGT Middle East)

作者： Qiu, Wei Yin, He Wu, Yuru Chen, Chang Zeng, Chujie Zhan, Lingwei Liu, Yilu Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781665465434

Widespread implementations of high-frequency electronic equipment increase the demand for high-frequency synchronized measurement data. However, high-speed data streaming challenges the quality of communication and data server storage. To address this problem, this paper proposes an online lossless compression of synchro-waveform measurement for smart grid monitoring. The lossless compression will highly reduce the data transmission and storage burden while reserving all the critical information for both online and offline analysis. To this end, the proposed method is designed based on the delta-delta, Simple-8b, and Lempel-Ziv-Markov chain algorithm technologies, where the delta-delta consists of a two-order delta and one periodical delta. Then, the simulation experiments based on various noise levels, time windows, and sampling rates are presented and compared with some state-of-art methods. Moreover, the laboratory experiment is conducted to verify the validity of the synchro-waveform measurement compression method, where 53.1% to 77.3% of data space can be saved under 60dB to 120dB noise scenarios.

关键词： Online lossless compression synchro-waveform measurement smart grid monitoring high-speed data streaming

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Deep Learning-Based Point Cloud compression With Optimization by Point Density Ratio 13

Deep Learning-Based Point Cloud Compression With Optimizatio...

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13th ieee Global conference on Consumer Electronic, GCCE 2024

作者： Kimata, Hideaki Kogakuin University School of Informatics Tokyo Japan

ISBN: (纸本)9798350355079

compression coding of point clouds using deep learning has been studied. A large reduction of the code volume results in hole-like degradation. We propose new metrics and an optimization method to suppress such visually noticeable degradation. Experimental results show that the metrics based on the ratio of point density can be used to detect degradation where points are spatially missing, and that the proposed coding method can reduce the code volume in such a way that such degradation is suppressed. © 2024 ieee.

关键词： data compression ratio

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A Communication-Efficient Distributed Optimization Algorithm with Linear Convergence over Directed Graphs 14

A Communication-Efficient Distributed Optimization Algorithm...

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14th Asian Control conference (ASCC)

作者： Duan, Yuzhu Yang, Ziwen Zhu, Shanying Shanghai Jiao Tong Univ Dept Automat Minist Educ China Key Lab Syst Control & Informat Proc Shanghai Peoples R China

ISBN: (纸本)9798331540845;9789887581598

The Industrial Internet of Things (IIoT) is increasingly emerging as a novel paradigm for information exchange within industrial production environments. To facilitate the efficient operation of IIoT, there is an urgent requirement for communication-efficient methods to transmit extensive production data. This paper proposes a communication-efficient distributed optimization algorithm tailored for the strongly convex and smooth convex optimization problems over a directed graph. To encompass a broader class of compressors, the assumption of compressor is relaxed to contain both relative and absolute errors. To mitigate compression errors, we adopt differential compression technique and dynamic scaling factor in the algorithm. In particular, under this compression assumption, a non-uniform quantizer is introduced to enable finite-bit transmission over communication channels and an upper bound of the required bits with this quantizer is provided. Moreover, we prove the linear convergence of the proposed algorithm with compression, and numerical examples are provided to verify the performance of the proposed algorithm.

关键词： Distributed optimization compression communication linear convergence directed networks

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A Novel Reverse Random Hyperplane Projection Scheme and Its Effect on Mining Sensor Streams

A Novel Reverse Random Hyperplane Projection Scheme and Its ...

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2024 ieee International conference on Big data, Bigdata 2024

作者： Skevis, Antonios Klioumis, George Giatrakos, Nikos Technical University of Crete School of Ece Chania Greece

ISBN: (纸本)9798350362480

In this work we introduce a novel, reversible data summarization technique, namely the Reverse Random Hyperplane Projection (RRHP) scheme. RRHP is particularly useful in Wireless Sensor Network (WSN) settings because it enables individual sensors to compress their local data streams before transmitting them across the WSN. In that, RRHP saves communication and, thus, the residual energy of battery-powered sensors. Then, when the compressed sensor data streams reach a base station, the reversibility property of RRHP can be used to regain approximations of the original sensor streams to perform all kinds of data mining tasks. We provide formal theoretic guarantees on how RRHP directly trades the amount of compression for the approximation of original sensor streams' desired properties. We experimentally prove that RRHP is useful for performing various kinds of data mining tasks, over sensor data streams, by dramatically reducing the amount of communicated data, simultaneously achieving high accuracy. © 2024 ieee.

关键词： compression data mining data streams locality sensitive hashing sensor networks

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PCHM-Net: A New Point Cloud compression Framework for Both Human Vision and Machine Vision

PCHM-Net: A New Point Cloud Compression Framework for Both H...

引用

ieee International conference on Multimedia and Expo (ICME)

作者： Liu, Lei Hu, Zhihao Zhang, Jing Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ Sch Software Beijing Peoples R China

ISBN: (纸本)9781665468916

Recently, point cloud data has attracted increasing attention in various machine vision tasks like classification and detection. However, directly transmitting the raw point cloud for such machine vision tasks will bring a huge bit-rate cost. In this work, we propose a new point cloud compression framework called PCHM-Net for both human vision and machine vision. Our proposed PCHM-Net adopts a two-branch structure with the shared octree-based compression module. To better compress the point cloud data and save bit-rate for machine vision tasks, we use the point cloud selection module to select a sparse set of points before octree construction, which allows us to use deeper octree structure and thus better reconstruct the point cloud coordinates for more discriminative feature extraction. We further propose a global feature aggregation-based classification module to deal with the sparse point cloud classification task. Comprehensive experiments on various point cloud benchmark datasets (e.g., ModelNet, ShapeNet and ScanNet) demonstrate that our newly proposed PCHM-Net achieves promising coding performance for both human vision and machine vision.

关键词： Point Cloud compression Human Vision Machine Vision

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Computing the optimal BWT of very large string collections

Computing the optimal BWT of very large string collections

引用

data compression conference (DCC)

作者： Cenzato, Davide Guerrini, Veronica Liptak, Zsuzsanna Rosone, Giovanna Univ Verona Verona Italy Univ Pisa Pisa Italy

ISBN: (纸本)9798350347951

It is known that the exact form of the Burrows-Wheeler Transform (BWT) of a string collection depends, in most implementations, on the input order of the strings in the collection. Reordering strings of an input collection affects the number of equal-letter runs r, arguably the most important parameter of BWT-based data structures, such as the FM-index or the r-index. Bentley, Gibney, and Thankachan [ESA 2020] introduced a linear-time algorithm for computing the permutation of the input collection which yields the minimum number of runs of the resulting BWT. In this paper, we present the first tool that guarantees a Burrows-Wheeler Transform with minimum number of runs (optBWT), by combining i) an algorithm that builds the BWT from a string collection (either SAIS-based [Boucher et al., SPIRE 2021] or BCR [Bauer et al., CPM 2011]);ii) the SAP array data structure introduced in [Cox et al., Bioinformatics, 2012];and iii) the algorithm by Bentley et al. We present results both on real-life and simulated data, showing that the improvement achieved in terms of r with respect to the input order is significant and the overhead created by the computation of the optimal BWT negligible, making our tool competitive with other tools for BWT-computation in terms of running time and space usage. In particular, on real data the optBWT obtains up to 31 times fewer runs with only a 1.39x slowdown. Source code is available at https://***/davidecenzato/***.

关键词： Burrows Wheeler Transform data compression number of runs optimal BWT repetitiveness measures string collections

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Lossy Image compression with Quantized Hierarchical VAEs 23

Lossy Image Compression with Quantized Hierarchical VAEs

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

作者： Duan, Zhihao Lu, Ming Ma, Zhan Zhu, Fengqing Purdue Univ W Lafayette IN 47907 USA Nanjing Univ Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781665493468

Recent work has shown a strong theoretical connection between variational autoencoders (VAEs) and the rate distortion theory. Motivated by this, we consider the problem of lossy image compression from the perspective of generative modeling. Starting from ResNet VAEs, which are originally designed for data (image) distribution modeling, we redesign their latent variable model using a quantization-aware posterior and prior, enabling easy quantization and entropy coding for image compression. Along with improved neural network blocks, we present a powerful and efficient class of lossy image coders, outperforming previous methods on natural image (lossy) compression. Our model compresses images in a coarse-to-fine fashion and supports parallel encoding and decoding, leading to fast execution on GPUs. Code is made available online.

关键词： Adaptation models Computer vision Image coding Quantization (signal) Computational modeling Neural networks Rate distortion theory

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An FPGA-Based Top-K Gradient compression Accelerator for Distributed Deep Learning Training 17

An FPGA-Based Top-K Gradient Compression Accelerator for Dis...

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17th ieee International conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Wang, Ziyao Zhang, Jiayu Li, Kunyue Sun, Jialei Dong, Feng Chen, Ke Qiao, Yong Jiang, Jianfei National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai China Beijing iQlYI Science & Technology Co.. Ltd.. Shanghai China

ISBN: (纸本)9798350361834

In distributed neural network training with multiple machines and devices, communication limitations often create efficiency bottlenecks due to the frequent exchange of model parameters and gradient information between computing nodes. This paper proposes an FPGA-based accelerator leveraging the gradient compression algorithm Top-K sparsification, which enhances performance by offloading computationally intensive compression operations to the FPGA. Experimental results demonstrate that the FPGA compression accelerator designed in this study achieves superior computing performance and compression efficiency compared to compression algorithms implemented on CPUs and GPUs. Specifically, the FPGA compresses the same amount of data 3.3-3.7 times faster than parallel solutions on CPUs and 1.3-1.8 times faster than on GPUs. © 2024 ieee.

关键词： Computation offloading

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