Lossy compression of astronomical images

被引：0
|
作者：
Bernas, M [1 ]
Páta, P [1 ]
Weinlich, J [1 ]
Hudec, R [1 ]
Tirado, AC [1 ]
机构：
[1] Czech Tech Univ, Prague 16627, Czech Republic
来源：
PROCEEDINGS OF THE 5TH INTEGRAL WORKSHOP ON THE INTEGRAL UNIVERSE | 2004年 / 552卷
关键词：
D O I：
暂无
中图分类号：
P1 [天文学];
学科分类号：
0704 ;
摘要：
The paper deals with the discussion of the lossless and lossy compression of astronomical images. Because the lossless compression methods allow to reach the compression ratio according to properties of image data approx. 1.5 - 3 only, we tried to apply lossy methods, which allow to reach much higher compression ratio up to 100 according to allowed loss. For successful application of lossy compression it is necessary to find the appropriate definition of acceptable loss. The lossy compression methods are usually used for the compression of normal, day to day images, where the acceptable loss is done by the visibility of image errors. For astronomical images it is necessary to find another criterion. We applied compression standard JPEG 2000 on astronomical images of star fields and changed the parameters of wavelet decomposition. For the evaluation of loss (quality of the compression) we use the difference in position and magnitude of stars in original and compressed images, calculated by programme IRAF.
引用
收藏
页码：829 / 832
页数：4
相关论文
共 50 条
  • [41] Lossy compression of high dynamic range images and video
    Mantiuk, Rafal
    Myszkowski, Karol
    Seidel, Hans-Peter
    [J]. HUMAN VISION AND ELECTRONIC IMAGING XI, 2006, 6057
  • [42] TWO ASPECTS IN LOSSY COMPRESSION OF HYPESPECTRAL AVIRIS IMAGES
    Lukin, Vladimir
    Ponomarenko, Nikolay
    Zriakhov, Mikhail
    Kaarna, Arto
    [J]. 2008 INTERNATIONAL CONFERENCE ON MATHEMATICAL METHODS IN ELECTROMAGNETIC THEORY, 2008, : 375 - +
  • [43] Content-based compression and transmission of astronomical images
    Dong, JY
    Boussalis, H
    Liu, C
    Rad, K
    [J]. ITRE2003: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: RESEARCH AND EDUCATION, 2003, : 17 - 21
  • [44] Modeling of end-to-end transmission of astronomical images over lossy network
    Jonathan, R
    Boussalis, H
    Liu, C
    Rad, K
    Dong, J
    [J]. ICOMP '05: PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON INTERNET COMPUTING, 2005, : 469 - 473
  • [45] Neural network assisted effective lossy compression of medical images
    Jaiswal, Ravi R.
    Gaikwad, Abhay N.
    [J]. IETE TECHNICAL REVIEW, 2006, 23 (02) : 119 - 126
  • [46] An Automatic Approach to Lossy Compression of Images Corrupted by Poisson Noise
    Lukin, V. V.
    Zriakhov, M. S.
    Ponomarenko, N. N.
    Kaarna, Arto
    [J]. MRRS: 2008 MICROWAVES, RADAR AND REMOTE SENSING SYMPOSIUM, PROCEEDINGS, 2008, : 139 - +
  • [47] Convolution Neural Network based lossy compression of hyperspectral images
    Dua, Yaman
    Singh, Ravi Shankar
    Parwani, Kshitij
    Lunagariya, Smit
    Kumar, Vinod
    [J]. SIGNAL PROCESSING-IMAGE COMMUNICATION, 2021, 95
  • [48] Task-oriented lossy compression of magnetic resonance images
    Anderson, M
    Atkins, MS
    Vaisey, J
    [J]. IMAGE DISPLAY: MEDICAL IMAGING 1996, 1996, 2707 : 239 - 250
  • [49] Scalable Lossy Compression for Pixel-Value Encrypted Images
    Kang, Xiangui
    Xu, Xianyu
    Peng, Anjie
    Zeng, Wenjun
    [J]. 2012 DATA COMPRESSION CONFERENCE (DCC), 2012, : 400 - 400
  • [50] Lossy Compression of Multichannel Remote Sensing Images with Quality Control
    Lukin, Vladimir
    Vasilyeva, Irina
    Krivenko, Sergey
    Li, Fangfang
    Abramov, Sergey
    Rubel, Oleksii
    Vozel, Benoit
    Chehdi, Kacem
    Egiazarian, Karen
    [J]. REMOTE SENSING, 2020, 12 (22) : 1 - 35
12345