Results 261 to 270 of about 931,486 (319)

Universal Rate-Distortion-Perception Representations for Lossy Compression

IEEE Transactions on Information Theory, 2021
In the context of lossy compression, Blau and Michaeli adopt a mathematical notion of perceptual quality and define the information rate-distortion-perception function, generalizing the classical rate-distortion tradeoff.
George Zhang, Jingjing Qian, Jun Chen, A. Khisti   +3 more
semanticscholar   +1 more source

Lossy Scientific Data Compression With SPERR

IEEE International Parallel and Distributed Processing Symposium, 2023
As the need for data reduction in high-performance computing (HPC) continues to grow, we introduce a new and highly effective tool to help achieve this goal—SPERR.
Shaomeng Li, P. Lindstrom, J. Clyne
semanticscholar   +1 more source

Ultrafast Error-bounded Lossy Compression for Scientific Datasets

IEEE International Symposium on High-Performance Parallel Distributed Computing, 2022
Today's scientific high-performance computing applications and advanced instruments are producing vast volumes of data across a wide range of domains, which impose a serious burden on data transfer and storage.
Xiaodong Yu, S. Di, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, F. Cappello   +6 more
semanticscholar   +1 more source

Deep-PCAC: An End-to-End Deep Lossy Compression Framework for Point Cloud Attributes

IEEE transactions on multimedia, 2021
The large data volume of point clouds poses severe challenges for efficient storage and transmission in recent years. In this paper, we propose the first--to our best knowledge--end-to-end deep framework for compressing point cloud attributes ...
Xihua Sheng, Li Li, Dong Liu, Zhiwei Xiong, Zhu Li, Feng Wu   +5 more
semanticscholar   +1 more source

OptZConfig: Efficient Parallel Optimization of Lossy Compression Configuration

IEEE Transactions on Parallel and Distributed Systems, 2022
Lossless compressors have very low compression ratios that do not meet the needs of today’s large-scale scientific applications that produce vast volumes of data.
Robert Underwood, Jon C. Calhoun, S. Di, A. Apon, F. Cappello   +4 more
semanticscholar   +1 more source

Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data

Proceedings of the VLDB Endowment, 2022
Today's scientific simulations and instruments are producing a large amount of data, leading to difficulties in storing, transmitting, and analyzing these data. While error-controlled lossy compressors are effective in significantly reducing data volumes
Pu Jiao, S. Di, Hanqi Guo, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, F. Cappello   +7 more
semanticscholar   +1 more source

Optimizing Error-Bounded Lossy Compression for Scientific Data With Diverse Constraints

IEEE Transactions on Parallel and Distributed Systems, 2022
Vast volumes of data are produced by today's scientific simulations and advanced instruments. These data cannot be stored and transferred efficiently because of limited I/O bandwidth, network speed, and storage capacity.
Yuanjian Liu, S. Di, Kai Zhao, Sian Jin, Cheng Wang, K. Chard, Dingwen Tao, I. Foster, F. Cappello   +8 more
semanticscholar   +1 more source

Compressive Sampling and Lossy Compression

IEEE Signal Processing Magazine, 2008
Recent results in compressive sampling have shown that sparse signals can be recovered from a small number of random measurements. This property raises the question of whether random measurements can provide an efficient representation of sparse signals in an information-theoretic sense.
V.K. Goyal, A.K. Fletcher, S. Rangan
openaire   +1 more source

Lossy compression of noisy images

IEEE Transactions on Image Processing, 1998
Noise degrades the performance of any image compression algorithm. This paper studies the effect of noise on lossy image compression. The effect of Gaussian, Poisson, and film-grain noise on compression is studied. To reduce the effect of the noise on compression, the distortion is measured with respect to the original image not to the input of the ...
Al-Shaykh, Osama K., Mersereau, Russell M.   +1 more
openaire   +3 more sources

Lossy Compression with Pretrained Diffusion Models

International Conference on Learning Representations
We apply the DiffC algorithm (Theis et al. 2022) to Stable Diffusion 1.5, 2.1, XL, and Flux-dev, and demonstrate that these pretrained models are remarkably capable lossy image compressors.
Jeremy Vonderfecht, Feng Liu
semanticscholar   +1 more source