An approach to prediction and providing of compression ratio for DCT based coder applied to remote sensing images
DOI:
https://doi.org/10.36023/ujrs.2016.9.67Keywords:
remote sensing, DCT-based coders, compression ratio prediction, hyperspectral dataAbstract
A novel compression ratio prediction and providing technique applicable to noisy and almost noise-free remote sensing images is proposed. It allows predicting and then providing a desired compression ratio for DCT-based coder in automatically manner. The proposed technique is algorithmically simple and has low computational complexity that allows using it onboard spaceborne or airborne carriers. The study is carried out for test and real-life Hyperion images. It is shown that the proposed technique has high accuracy and it is robust with respect to noise intensity and type. Relative error of prediction of providing compression ratio does not exceed 10%.
References
Al-Chaykh O.K., Mersereau R.M. (1998). Lossy compression of noisy images. IEEE Transactions on Image Processing. Vol. 7 (12). P. 1641-1652.
Cameron, C., Windmeijer, A., Frank, A. G., Gramajo, H., Cane, D. E., Khosla C. (1997). An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics. Vol. 77. № 2. P. 329-342.
Christophe E. (2011). Hyperspectral Data Compression Tradeoff. Optical Remote Sensing. Advances in Signal Processing and Exploitation Techniques, Springer. Vol. 8. 9-29.
Chang C-I. (2013). Hyperspectral Data Processing: Algorithm Design and Analysis. First Editions. John Wiley and Sons, Inc. 1164 p.
He Z., Mitra S.K. (2002). A linear source model and a unified rate control algorithm for DCT video coding. IEEE Transactions, Circuits and Systems for Video Technology. Vol. 12(11). P. 970 – 982.
Hsia, S-C., Hsu, W-C., Wu, S-R. (2013). A fast rate-distortion optimization algorithm for H.264/AVC codec. Signal, Image and Video Processing. Vol. 7(5). P.939-949.
Kozhemiakin, R., Abramov, S., Lukin, V., Djurovic, I., Vozel B. (2015). Peculiarities of 3D compression of noisy multichannel images. Procidings of Mediterranean Conference on Embedded Computing (MECO). P. 331-334.
Murtagh F., Starck J. L. (2001) Astronomical image and signal processing. Signal Processing Magazine, IEEE. Vol. 18 (2). P. 30-40.
Osokin A.N., Sidorov D.V. (2013). Modified JPEG encoder with bit-rate control. Online journal "Naukovedenie". 9 p. http://naukovedenie.ru/PDF/69tvn513.pdf
Ponomarenko, N., Lukin, V., Egiazarian, K., Astola J. (2008). ADCT: A new high quality DCT based coder for lossy image compression. CD ROM Proceedings of LNLA. – Switzerland. 6 p.
Ponomarenko, N.N., Lukin, V.V., Egiazarian, K., Astola, J. (2005). DCT Based High Quality Image Compression. Proceedings of 14th Scandinavian Conference on Image Analysis. Vol. 14. P. 1177-1185.
Ponomarenko, N., Zemliachenko, A., Lukin, V., Egiazarian, K., Astola J. (2013). Image lossy compression providing a required visual quality / // CD-ROM Proc. of the Seventh International Workshop on Video Processing and Quality Metrics for Consumer Electronics. 6 p.
Said A. (1996). A new fast and efficient image codec based on the partitioning in hierarchical trees. IEEE Trans. on Circuits Syst. Video Technology. Vol. 6. Р. 243-250.
Schowengerdt R. A. (2006). Remote Sensing: Models and Methods for Image Processing: Third edition. Academic Press, San Diego. 560 p.
Skauli T. (2011). Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing. Optics Express. Vol. 19(14). P. 13031-13046.
Wallace G.K. (1991). The JPEG still picture compression standard. Commun. ACM. Vol. 34. P. 30–44.
Zemliachenko, A.N., Abramov, S.K., Lukin, V.V., Vozel, B., Chehdi K. (2015). Compression ratio prediction in lossy compression of noisy images. Geoscience and Remote Sensing Symposium (IGARSS), IEEE International. P. 3497-3500.
Zemliachenko, A.N., Abramov, S.K.., Lukin, V.V., Vozel, B., Chehdi, K. (2015). Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters. Journal of Applied Remote Sensing. Vol. 9. P. 095066-095066.
Zemliachenko, N., Kozhemiakin, A., Uss, L., Abramov, K., Ponomarenko, N., Lukin, V., Vozel, B., Chehdi, K. (2014). Lossy Compression of Hyperspectral Images Based on Noise Parameters Estimation and Variance Stabilizing Transform. Journal of Applied Remote Sensing. Vol. 8. P. 1-25.
Zemliachenko, A., Kozhemiakin, R., Vozel, B., Lukin, V. (2016). Prediction of Compression Ratio in Lossy Compression of Noisy Images. Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET). 3 p.
Zhare, A., Bolton, J., Cnanussot, J., Gader P. (2014). Foreword to the Special Issue on Hyperspectral Image and Signal Processing. IEEE Journal on Selected Topics in Applied Earth Observations and Remote Sensing. Vol. 7(6). P. 1841-1843.
Downloads
Published
Issue
Section
License
Licensing conditions: the authors retain their copyrights and grant the journal the right of first publication of a work, simultaneously licensed in accordance with the Creative Commons Attribution License International CC-BY, which allows you to share the work with proof of authorship of the work and initial publication in this journal.
The authors, directing the manuscript to the editorial office of the Ukrainian Journal of Remote Sensing of the Earth, agree that the editorial board transfers the rights to protection and use of the manuscript (material submitted to the journal editorial board, including such protected copyright objects as photographs of the author, drawings, charts, tables, etc.), including reproduction in print and on the Internet; for distribution; to translate the manuscript into any languages; export and import of copies of the journal with the article of the authors for the purpose of distribution, informing the public. The above rights are transferred by the authors to the editors, without limitation of their validity, and in the territory of all countries of the world without limitation, including in Ukraine.
The authors guarantee that they have exclusive rights to use the submitted material. The editors are not liable to third parties for breach of data by the authors of the guarantees. The authors retain the right to use the published material, its fragments and parts for personal, including scientific and educational purposes. The rights to the manuscript are considered to be transferred by the authors of the editorial board from the moment of the publication of the issue of the journal in which it is published. Reprinting of materials published in the journal by other individuals and legal entities is possible only with the consent of the publisher, with the obligatory indication of the issue of the journal in which the material was published.