Results 271 to 280 of about 35,403 (321)

Lossy DICOM conversion may affect AI performance. [PDF]

Sci Rep
Mayer RS, Fliedner F, Mathisen IF, Laib A, Bein J, Eichelberg M, Wild PJ, Flinner N.   +7 more
europepmc   +1 more source

State-of-the-Art Trends in Data Compression: COMPROMISE Case Study. [PDF]

Entropy (Basel)
Podgorelec D, Strnad D, Kolingerová I, Žalik B.   +3 more
europepmc   +1 more source

Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing

Franck Cappello, Mario Acosta, Emmanuel Agullo, Hartwig Anzt, Jon C. Calhoun, Sheng Di, Luc Giraud, Thomas Grützmacher, Sian Jin, Kentaro Sano, Kento Sato, Amarjit Singh, Dingwen Tao, Jiannan Tian, Tomohiro Ueno, Robert Underwood, Frédéric Vivien, Xavier Yepes, Kazutomo Yoshii, Boyuan Zhang   +19 more
openalex   +2 more sources

Deep learning based medical image compression using cross attention learning and wavelet transform. [PDF]

Sci Rep
Dai F.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

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 Audio Compression via Compressed Sensing

2010 Data Compression Conference, 2010
We propose a Compressed Sensing application to audio signals and analyze its audio perceptual quality with PEAQ.
Rubem J. V. de Medeiros, Edmar C. Gurjão, Joâo M. de Carvalho   +2 more
openaire   +1 more source

Lossy Compression Algorithms

2014
In this chapter we examine compression algorithms such that recovered input data cannot be exactly reconstructed from compressed version. This termed “loss”. What we have, then, is a tradeoff between efficient compression versus a less accurate version of the input data. This tradeoff is captured in the Rate-Distortion Theory.
Ze-Nian Li, Mark S. Drew, Jiangchuan Liu
openaire   +1 more source

Lossy Audio Compression Identification

2018 26th European Signal Processing Conference (EUSIPCO), 2018
We propose a system which can estimate from an audio recording that has previously undergone lossy compression the parameters used for the encoding, and therefore identify the corresponding lossy coding format. The system analyzes the audio signal and searches for the compression parameters and framing conditions which match those used for the encoding.
Bongjun Kim, Zafar Rafii
openaire   +1 more source

Pointwise redundancy in lossy data compression and universal lossy data compression

IEEE Transactions on Information Theory, 2000
Summary: The author characterizes achievable pointwise redundancy rates for lossy data compression at a fixed distortion level. Pointwise redundancy refers to the difference between the description length achieved by an \(n\)th-order block code and the optimal \(nR(D)\) bits.
openaire   +1 more source