Almost lossless compression of noisy images-Reference-Cited by-同舟云学术

Almost lossless compression of noisy images

Published:2023-05-23 Issue:7-8 Volume:90 Page:454-463
ISSN:0171-8096
Container-title:tm - Technisches Messen
language:en
Short-container-title:

Author:

Jähne Bernd¹^ORCID

Affiliation:

1. Heidelberg University, Interdisciplinary Center for Scientific Computing (IWR) , Berliner Straße 43, 69120 , Heidelberg , Germany

Abstract

Abstract An almost lossless compression method for images is introduced adapted to the temporal noise of image sensors. In a first step, a non-linear gray value transform is applied to generate an image with a gray value independent temporal noise and less bits than the original image. The chosen value for the standard deviation of the temporal noise in the transformed image determines how accurately mean values and the standard deviation of temporal noise can be computed and to which extent the image can be compressed further by a lossless compression in a second step. Just a measurement of the noise characteristics according to the open and international EMVA standard 1288, a non-linear gray value transform for noise equalization, and an open source lossless compression algorithm are required to use this new compression method.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Instrumentation

Link

https://www.degruyter.com/document/doi/10.1515/teme-2023-0028/pdf

Reference15 articles.

1. A. Skodras, C. Christopoulos, and T. Ebrahimi, “The JPEG 2000 still image compression standard,” IEEE Signal Process. Mag., vol. 18, pp. 36–58, 2001. https://doi.org/10.1109/79.952804.

2. I. Galić, J. Weickert, M. Welk, A. Bruhn, A. Belyaev, and H.-P. Seidel, “Image compression with anisotropic diffusion,” J. Math. Imag. Vis., vol. 31, pp. 255–269, 2008. https://doi.org/10.1007/s10851-008-0087-0.

3. T. Richter, “JPEG on STEROIDS: common optimization techniques for JPEG image compression,” in 2016 IEEE International Conference on Image Processing (ICIP), 2016.

4. A. Martin, H. Zbinden, and B. Sanguinetti, “Image compression method with neglible and quantifiable information loss and high compression ratio,” Patent EP 3 185 555 A1, filed 23.12.2015, 2017.

5. A. Martin, H. Zbinden, and B. Sanguinetti, “Image compression method with neglible and quantifiable information loss and high compression ratio,” Patent US 10,063,891 B2, filed 23.12.2016, 2018.