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
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
  • [1] Influence of lossy compression techniques on processing precision of astronomical images
    Hanzlík, P
    Páta, P
    Schindler, J
    Vítek, S
    [J]. 2005 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT), VOLS 1 AND 2, 2005, : 346 - 351
  • [2] Restoration of lossy compressed astronomical images
    Bobichon, Y
    Bijaoui, A
    [J]. INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, PROCEEDINGS - VOL II, 1996, : 33 - 36
  • [3] Lossy compression of noisy images
    Al-Shaykh, OK
    Mersereau, RM
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 1998, 7 (12) : 1641 - 1652
  • [4] Regularization constraints in lossy compressed astronomical images restoration
    Bobichon, Y
    Bijaoui, A
    [J]. WAVELET APPLICATIONS IN SIGNAL AND IMAGE PROCESSING IV, PTS 1 AND 2, 1996, 2825 : 32 - 40
  • [5] Lossy Compression of Landsat Multispectral Images
    Kozhemiakin, Ruslan
    Abramov, Sergey
    Lukin, Vladimir
    Djurovic, Blazo
    Djurovic, Igor
    Vozel, Benoit
    [J]. 2016 5TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO), 2016, : 104 - 107
  • [6] Lossy compression approach to transmultiplexed images
    Sypka, Przemyslaw
    Ziolko, Mariusz
    Ziolko, Bartosz
    [J]. PROCEEDINGS ELMAR-2006, 2006, : 289 - 292
  • [7] Lossy compression of images with additive noise
    Ponomarenko, N
    Lukin, V
    Zriakhov, M
    Egiazarian, K
    Astola, J
    [J]. ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, PROCEEDINGS, 2005, 3708 : 381 - 386
  • [8] Prediction of Compression Ratio in Lossy Compression of Noisy Images
    Zemliachenko, Alexander
    Kozhemiakin, Ruslan
    Vozel, Benoit
    Lukin, Vladimir
    [J]. 2016 13TH INTERNATIONAL CONFERENCE ON MODERN PROBLEMS OF RADIO ENGINEERING, TELECOMMUNICATIONS AND COMPUTER SCIENCE (TCSET), 2016, : 693 - 697
  • [9] COMPRESSION RATIO PREDICTION IN LOSSY COMPRESSION OF NOISY IMAGES
    Zemliachenko, Alexander N.
    Abramov, Sergey
    Lukin, Vladimir V.
    Vozel, Benoit
    Chehdi, Kacem
    [J]. 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2015, : 3497 - 3500
  • [10] Lossy compression of ultraspectral images: integrating preprocessing and compression stages
    Herrero, Rolando
    Ingle, Vinay K.
    [J]. SIGNAL IMAGE AND VIDEO PROCESSING, 2014, 8 (08) : 1569 - 1580