Results 21 to 30 of about 931,486 (319)
TopoSZ: Preserving Topology in Error-Bounded Lossy Compression [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2023 Existing error-bounded lossy compression techniques control the pointwise error during compression to guarantee the integrity of the decompressed data. However, they typically do not explicitly preserve the topological features in data.
Lin Yan, Xin Liang, Hanqi Guo, Bei Wang
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CUSZ-i: High-Ratio Scientific Lossy Compression on GPUs with Optimized Multi-Level Interpolation [PDF]
International Conference for High Performance Computing, Networking, Storage and Analysis, 2023 Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. Compared to CPU-based compressors, GPU-based compressors exhibit substantially higher throughputs, fitting better for today’s HPC applications ...
Jinyang Liu +10 more
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Optimizing Scientific Data Transfer on Globus with Error-Bounded Lossy Compression [PDF]
IEEE International Conference on Distributed Computing Systems, 2023 The increasing volume and velocity of science data necessitate the frequent movement of enormous data volumes as part of routine research activities. As a result, limited wide-area bandwidth often leads to bottlenecks in research progress.
Yuanjian Liu +4 more
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Polar Lattices for Lossy Compression [PDF]
IEEE Transactions on Information Theory, 2021 Polar lattices, which are constructed from polar codes, have recently been proved to be able to achieve the capacity of the additive white Gaussian noise (AWGN) channel. In this work, we propose a new construction of polar lattices to solve the dual problem, i.e., achieving the rate-distortion bound of a memoryless Gaussian source, which means that ...
Ling Liu, Jinwen Shi, Cong Ling
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Black-box statistical prediction of lossy compression ratios for scientific data [PDF]
The international journal of high performance computing applications, 2023 Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement.
Robert Underwood +5 more
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Lossy Image Compression with Conditional Diffusion Models [PDF]
Neural Information Processing Systems, 2022 This paper outlines an end-to-end optimized lossy image compression framework using diffusion generative models. The approach relies on the transform coding paradigm, where an image is mapped into a latent space for entropy coding and, from there, mapped
Ruihan Yang, Stephan Mandt
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Dynamic Quality Metric Oriented Error Bounded Lossy Compression for Scientific Datasets [PDF]
International Conference for High Performance Computing, Networking, Storage and Analysis, 2022 With ever-increasing execution scale of the high performance computing (HPC) applications, vast amount of data are being produced by scientific research every day.
Jinyang Liu +5 more
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Performance analysis of compression algorithms for information security: A Review [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2020 Data compression is a vital part of information security, since compressed data is much more secure and convenient tohandle. Effective data compression technique creates an effective, secure, easy communicable & redundant data.
Neha Sharma, Usha Batra
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QARV: Quantization-Aware ResNet VAE for Lossy Image Compression [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 This paper addresses the problem of lossy image compression, a fundamental problem in image processing and information theory that is involved in many real-world applications.
Zhihao Duan +4 more
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Effect of Lossy JPEG Compression of an Image with Chromatic Aberrations on Target Measurement Accuracy [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 This paper reports an experiment conducted to investigate the effect of lossy JPEG compression of an image with chromatic aberrations on the measurement accuracy of target center by the intensity-weighted centroid method.
R. Matsuoka
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