18 papers · ranked by Valyu relevance
Yuhang Wang, Weihua Chen, Linjing Song, Zhiping Xu + 6 more
With the rapid growth of data volume in sensor networks, lossy source coding systems achieve high-efficiency data compression with low distortion under limited transmission bandwidth. However, conventional compression algorithms rely on a two-stage framework with high computational complexity and frequently struggle to…
Huan Deng, Dan Song, Zhiping Xu, Yanglong Sun + 2 more
'Bal S. Virdee'] In the Internet of Things, sensor nodes collect environmental information and utilize lossy compression for saving storage space. To achieve this objective, high-efficiency compression of the continuous source should be studied. Different from existing schemes, lossy source coding is implemented based…
Jinkai Ren, Dan Song, Huihui Wu, Lin Wang + 3 more
'Jun Chen' 'Sadaf Salehkalaibar'] It is challenging to design an efficient lossy compression scheme for complicated sources based on block codes, especially to approach the theoretical distortion-rate limit. In this paper, a lossy compression scheme is proposed for Gaussian and Laplacian sources. In this scheme, a new…
Yibo Yang, Stephan Mandt, Lucas Theis
Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such…
Mohammad Hosseini
—Today, with the growing demands of information storage and data transfer, data compression is becoming increasingly important. Data Compression is a technique which is used to decrease the size of data. This is very useful when some huge files have to be transferred over networks or being stored on a data storage…
Bin Duan, Logan A Walker, Bin Xie, Wei Jie Lee + 3 more
Recent advances in microscopy have pushed imaging data generation to an unprecedented scale. While scientists benefit from higher spatiotemporal resolutions and larger imaging volumes, the increasing data size presents significant storage, visualization, sharing, and analysis challenges. Lossless compression typically…
Viktor Makarichev, Vladimir Lukin, Oleg Illiashenko, Vyacheslav Kharchenko + 1 more
Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as…
Dale Zhou, Sharon M. Noh, Nora C. Harhen, Nidhi V. Banavar + 3 more
The ability to discriminate similar visual stimuli has been used as an important index of memory function. This ability is widely thought to be supported by expanding the dimensionality of relevant neural codes, such that neural representations for the similar stimuli are maximally distinct, or “separated.” An…
Kavindu Jayasooriya, Sasha P. Jenner, Pasindu Marasinghe, Udith Senanayake + 5 more
Nanopore sequencing is an increasingly central tool for genomics. Despite rapid advances in the field, large data volumes and computational bottlenecks continue to pose major challenges. Here we introduce ex-zd, a new data compression strategy that helps address the large size of raw signal data generated during…
Alice Tor, Yuxin Wu, Stephen E Clarke, Lisa Yamada + 2 more
The complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying…
Hendrik Vincent Koops, Gianluca Micchi, Elio Quinton
Previous research contributions on blind lossy compression identification report near perfect performance metrics on their test set, across a variety of codecs and bit rates. However, we show that such results can be deceptive and may not accurately represent true ability of the system to tackle the task at hand. In…
Sven-Jannik Wöhnert, Kai Hendrik Wöhnert, Eldar Almamedov, Carsten Frank + 1 more
Associating meta information with images is common since the early days of the photography. This ranges from date, time and place where an image was taken up to semantics such as "Grandma with Peter at Christmas 1992" written on the back of the image. Nowadays, most images are taken with a digital device that…
Herbert J. Bernstein, Alexei S. Soares, Kimberly Horvat, Jean Jakoncic
New higher-count-rate, integrating, large area X-ray detectors with framing rates as high as 17,400 images per second are beginning to be available. These will soon be used for specialized MX experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some…
Alin-Adrian Alecu, Mohammad Ali Tahouri, Adrian Munteanu, Bujor Păvăloiu + 1 more
Near-lossless coding schemes traditionally rely on uniform quantization to control the maximum absolute error ( $L_{\infty}$ norm) of residual signals, often assuming a parametric model for the source distribution. This paper introduces a novel design framework for non-uniform, entropy-aware $L_{\infty}$-oriented…
Tiantian Li, Qunbing Xia, Yue Li, Ruixiao Guo + 1 more
Learning-based lossless image compression employs pixel-based or subimage-based auto-regression for probability estimation, which achieves desirable performances. However, the existing works only consider context dependencies in one direction, namely, those symbols that appear before the current symbol in raster order.…
Sinem Gümüş, Fatih Kamışlı
—This paper considers lossless image compression and presents a learned compression system that can achieve stateof-the-art lossless compression performance but uses only 59K parameters, which is more than 30x less than other learned systems proposed recently in the literature. The explored system is based on a learned…
Fajia Sun, Long Qian
DNA has been pursued as a compelling medium for digital data storage during the past decade. While large-scale data storage and random access have been achieved in artificial DNA, the synthesis cost keeps hindering DNA data storage from popularizing into daily life. In this study, we proposed a more efficient paradigm…
Chen-Hsiu Huang, Ja-Ling Wu, Jun Chen
End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational…