A Survey on Lossless Compression of Bayer Color Filter Array Images
Although most digital cameras acquire images in a raw
format, based on a Color Filter Array that arranges RGB color
filters on a square grid of photosensors, most image compression
techniques do not use the raw data; instead, they use the rgb result
of an interpolation algorithm of the raw data. This approach is
inefficient and by performing a lossless compression of the raw data,
followed by pixel interpolation, digital cameras could be more power
efficient and provide images with increased resolution given that the
interpolation step could be shifted to an external processing unit. In
this paper, we conduct a survey on the use of lossless compression
algorithms with raw Bayer images. Moreover, in order to reduce the
effect of the transition between colors that increase the entropy of
the raw Bayer image, we split the image into three new images
corresponding to each channel (red, green and blue) and we study
the same compression algorithms applied to each one individually.
This simple pre-processing stage allows an improvement of more than
15% in predictive based methods.
[1] B. Bayer, “Color imaging array,” Jul. 20 1976, uS Patent 3,971,065.
(Online). Available: http://www.google.pt/patents/US3971065
[2] R. Kimmel, “Demosaicing: image reconstruction from color ccd
samples,” IMAGE PROCESSING, IEEE TRANSACTIONS ON, 1999.
[3] X. Xie, G. Li, Z. Wang, C. Zhang, D. Li, and X. Li, “A novel method
of lossy image compression for digital image sensors with bayer color
filter arrays.” in ISCAS (5). IEEE, 2005, pp. 4995–4998.
[4] K. Chin Chye, M. Jayanta, and M. Sanjit K., “New efficient methods
of image compression in digital cameras with color filter array,” IEEE
Transactions on Consumer Electronics, vol. 49, no. 4, 2003.
[5] B. Arcangelo, V. Filippo, B. Antonio, and C. Salvatore, “Predictive
differential modulation for cfa compression.” in Proceedings of the 6th
Nordic Signal Processing Symposium - NORSIG, 2004.
[6] G. K. Wallace, “The jpeg still picture compression standard,” Commun.
ACM, vol. 34, no. 4, pp. 30–44, Apr. 1991.
[7] L. Sang-Yong and O. Antonio, “A novel approach of image compression
in digital cameras with a bayer color filter array.” in Proceedings of ICIP
2001 – International Conference on Image Processing, 2001.
[8] Y. Tsai, K. Parulski, and M. Rabbani, “Compression method and
apparatus for single-sensor color imaging systems,” Oct. 1991, uS Patent
5,053,861.
[9] Z. Ning and W. Xiaolin, “Lossless compression of color mosaic images.”
in Proceedings of ICIP 2004 – International Conference on Image
Processing, 2004.
[10] K.-H. Chung, Y.-H. Chan, C.-H. Fu, and Y.-L. Chan, “A high
performance lossless bayer image compression scheme.” in ICIP (2).
IEEE, 2007, pp. 353–356.
[11] K.-H. Chung and Y.-H. Chan, “A lossless compression scheme for bayer
color filter array images.” IEEE Transactions on Image Processing,
vol. 17, no. 2, pp. 134–144, 2008.
[12] M. J. Weinberger, G. Seroussi, and G. Sapiro, “The loco-i lossless image
compression algorithm: Principles and standardization into jpeg-ls,”
IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, no. 8, pp.
1309–1324, 2000.
[13] A. Skodras, C. Christopoulos, and T. Ebrahimi, “The jpeg2000 still
image compression standard,” IEEE Signal Proc. Mag, 2001.
[14] V. Kyrki, “Jbig image compression standard,” 1999.
[15] T. Boutell, “PNG (Portable Network Graphics) Specification Version
1.0,” RFC 2083 (Informational), Internet Engineering Task Force, March
1997.
[16] M. J. Weinberger, G. Seroussi, and G. Sapiro, “Loco-i: A low
complexity, context-based, lossless image compression algorithm,” 1996.
[17] M. J. Weinberger, G. Seroussi, I. Ueno, and F. Ono, “Embedded block
coding in jpeg 2000.” in ICIP, 2000, pp. 33–36.
[18] X. Wu and N. Memon, “Context-based, adaptive, lossless image coding,”
vol. 45, no. 4, pp. 437–444, Apr. 1997.
[19] A. J. R. Neves and A. J. Pinho, “Lossless compression of microarray
images using image-dependent finite-context models,” IEEE Trans. on
Medical Imaging, vol. 28, no. 2, pp. 194–201, Feb. 2009.
[20] Y. Yoo, Y. G. Kwon, and A. Ortega, “Embedded image-domain
compression using context models,” in Proc. of the IEEE Int. Conf. on
Image Processing, ICIP-99, vol. I, Kobe, Japan, Oct. 1999, pp. 477–481.
[21] A. J. Pinho and A. J. R. Neves, “Progressive lossless compression of
medical images,” in Proc. of the IEEE Int. Conf. on Acoustics, Speech,
and Signal Processing, ICASSP-2009, Taipei, Taiwan, Apr. 2009.
[22] L. M. O. Matos, A. J. R. Neves, and A. J. Pinho, “Compression of
microarrays images using a binary tree decomposition,” in Proc. of
the 22th European Signal Processing Conf., EUSIPCO-2014, Lisbon,
Portugal, Sep. 2014, pp. 531–535.
[23] “Kodak image set,” http://r0k.us/graphics/kodak/, accessed: 2015-10-5.
[24] “Jbig kit,” http://www.cl.cam.ac.uk/∼mgk25/jbigkit, accessed:
2015-10-5.
[25] “Jpeg-ls software,” http://sweet.ua.pt/luismatos/codecs/jpeg ls v2.2.tar.
gz, accessed: 2015-10-5.
[26] “Png software,” urlhttp://netpbm.sourceforge.net/, accessed: 2015-10-5.
[1] B. Bayer, “Color imaging array,” Jul. 20 1976, uS Patent 3,971,065.
(Online). Available: http://www.google.pt/patents/US3971065
[2] R. Kimmel, “Demosaicing: image reconstruction from color ccd
samples,” IMAGE PROCESSING, IEEE TRANSACTIONS ON, 1999.
[3] X. Xie, G. Li, Z. Wang, C. Zhang, D. Li, and X. Li, “A novel method
of lossy image compression for digital image sensors with bayer color
filter arrays.” in ISCAS (5). IEEE, 2005, pp. 4995–4998.
[4] K. Chin Chye, M. Jayanta, and M. Sanjit K., “New efficient methods
of image compression in digital cameras with color filter array,” IEEE
Transactions on Consumer Electronics, vol. 49, no. 4, 2003.
[5] B. Arcangelo, V. Filippo, B. Antonio, and C. Salvatore, “Predictive
differential modulation for cfa compression.” in Proceedings of the 6th
Nordic Signal Processing Symposium - NORSIG, 2004.
[6] G. K. Wallace, “The jpeg still picture compression standard,” Commun.
ACM, vol. 34, no. 4, pp. 30–44, Apr. 1991.
[7] L. Sang-Yong and O. Antonio, “A novel approach of image compression
in digital cameras with a bayer color filter array.” in Proceedings of ICIP
2001 – International Conference on Image Processing, 2001.
[8] Y. Tsai, K. Parulski, and M. Rabbani, “Compression method and
apparatus for single-sensor color imaging systems,” Oct. 1991, uS Patent
5,053,861.
[9] Z. Ning and W. Xiaolin, “Lossless compression of color mosaic images.”
in Proceedings of ICIP 2004 – International Conference on Image
Processing, 2004.
[10] K.-H. Chung, Y.-H. Chan, C.-H. Fu, and Y.-L. Chan, “A high
performance lossless bayer image compression scheme.” in ICIP (2).
IEEE, 2007, pp. 353–356.
[11] K.-H. Chung and Y.-H. Chan, “A lossless compression scheme for bayer
color filter array images.” IEEE Transactions on Image Processing,
vol. 17, no. 2, pp. 134–144, 2008.
[12] M. J. Weinberger, G. Seroussi, and G. Sapiro, “The loco-i lossless image
compression algorithm: Principles and standardization into jpeg-ls,”
IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, no. 8, pp.
1309–1324, 2000.
[13] A. Skodras, C. Christopoulos, and T. Ebrahimi, “The jpeg2000 still
image compression standard,” IEEE Signal Proc. Mag, 2001.
[14] V. Kyrki, “Jbig image compression standard,” 1999.
[15] T. Boutell, “PNG (Portable Network Graphics) Specification Version
1.0,” RFC 2083 (Informational), Internet Engineering Task Force, March
1997.
[16] M. J. Weinberger, G. Seroussi, and G. Sapiro, “Loco-i: A low
complexity, context-based, lossless image compression algorithm,” 1996.
[17] M. J. Weinberger, G. Seroussi, I. Ueno, and F. Ono, “Embedded block
coding in jpeg 2000.” in ICIP, 2000, pp. 33–36.
[18] X. Wu and N. Memon, “Context-based, adaptive, lossless image coding,”
vol. 45, no. 4, pp. 437–444, Apr. 1997.
[19] A. J. R. Neves and A. J. Pinho, “Lossless compression of microarray
images using image-dependent finite-context models,” IEEE Trans. on
Medical Imaging, vol. 28, no. 2, pp. 194–201, Feb. 2009.
[20] Y. Yoo, Y. G. Kwon, and A. Ortega, “Embedded image-domain
compression using context models,” in Proc. of the IEEE Int. Conf. on
Image Processing, ICIP-99, vol. I, Kobe, Japan, Oct. 1999, pp. 477–481.
[21] A. J. Pinho and A. J. R. Neves, “Progressive lossless compression of
medical images,” in Proc. of the IEEE Int. Conf. on Acoustics, Speech,
and Signal Processing, ICASSP-2009, Taipei, Taiwan, Apr. 2009.
[22] L. M. O. Matos, A. J. R. Neves, and A. J. Pinho, “Compression of
microarrays images using a binary tree decomposition,” in Proc. of
the 22th European Signal Processing Conf., EUSIPCO-2014, Lisbon,
Portugal, Sep. 2014, pp. 531–535.
[23] “Kodak image set,” http://r0k.us/graphics/kodak/, accessed: 2015-10-5.
[24] “Jbig kit,” http://www.cl.cam.ac.uk/∼mgk25/jbigkit, accessed:
2015-10-5.
[25] “Jpeg-ls software,” http://sweet.ua.pt/luismatos/codecs/jpeg ls v2.2.tar.
gz, accessed: 2015-10-5.
[26] “Png software,” urlhttp://netpbm.sourceforge.net/, accessed: 2015-10-5.
@article{"International Journal of Information, Control and Computer Sciences:72682", author = "Alina Trifan and António J. R. Neves", title = "A Survey on Lossless Compression of Bayer Color Filter Array Images", abstract = "Although most digital cameras acquire images in a raw
format, based on a Color Filter Array that arranges RGB color
filters on a square grid of photosensors, most image compression
techniques do not use the raw data; instead, they use the rgb result
of an interpolation algorithm of the raw data. This approach is
inefficient and by performing a lossless compression of the raw data,
followed by pixel interpolation, digital cameras could be more power
efficient and provide images with increased resolution given that the
interpolation step could be shifted to an external processing unit. In
this paper, we conduct a survey on the use of lossless compression
algorithms with raw Bayer images. Moreover, in order to reduce the
effect of the transition between colors that increase the entropy of
the raw Bayer image, we split the image into three new images
corresponding to each channel (red, green and blue) and we study
the same compression algorithms applied to each one individually.
This simple pre-processing stage allows an improvement of more than
15% in predictive based methods.", keywords = "Bayer images, CFA, losseless compression, image
coding standards.", volume = "10", number = "4", pages = "729-6", }
