Akcije

Telfor Journal
kako citirati ovaj članak
podeli ovaj članak

Metrika

  • citati u SCIndeksu: 0
  • citati u CrossRef-u:0
  • citati u Google Scholaru:[]
  • posete u poslednjih 30 dana:1
  • preuzimanja u poslednjih 30 dana:0

Sadržaj

članak: 8 od 15  
Back povratak na rezultate
2012, vol. 4, br. 2, str. 122-127
On predictive-based lossless compression of images with higher bit depths
(naslov ne postoji na srpskom)
Univerzitet u Banjoj Luci, Elektrotehnički fakultet, Republika Srpska, BiH

e-adresaaleksej@etfbl.net, goran.banjac@etfbl.net
Ključne reči: bit depth; compression; lossless; medical images; prediction
Sažetak
(ne postoji na srpskom)
Due to the rapidly increasing requirements for data transmission and storage, applications for fast and efficient compression of data have a very important role. Lossless compression must be applied when data acquisition is expensive. For example, lossless image compression must be applied in aerial, medical and space imaging. Besides the requirements for high compression ratios as much as it is possible, lossless image coding algorithms should be as fast as possible. During the late nineties of the previous century, many predictive-based algorithms for lossless compression of 8-bit images were introduced. These algorithms were usually expanded to enable processing of images with higher bit depths. All predictive based algorithms used more or less efficient predictors to remove spatial redundancy in images. This paper gives a comparative analysis of predictor efficiency with special emphasis on images with higher bit depths. A novel predictive-based, lossless image compression algorithm with a simple context-based entropy coder is presented, as well. A comparison with standardized lossless compression algorithms JPEG-LS and JPEG2000 is made on a large set of 12-bit medical images of different modalities and 12-bit and 16-bit natural images. It is shown that the proposed solution can achieve approximately the same bitrates as standardized algorithms even though it is much simpler.
Reference
Avramović, A. (2011) Lossless compression of medical images based on gradient edge detection. u: Proceedings of 19th Telecommunications Forum TELFOR 2011, Belgrade
Bankman, I.N. (2009) Handbook of Medical Image Processing and Analysis. London: Elsevier
Castelli, V., Bergam, L.D. (2002) Image databases, search and retrieval of digital imagery. John Wiley & Sons
Deng, G., Ye, H. (1999) Lossless image compression using adaptive predictor combination: Symbol mapping and context filtering. u: Conference on image processing, Kobe, Japan, Oct., vol. 4, str. 63-67
Hsieh, F.Y., Fan, K.C. (2005) High performance lossless image coder. u: IPPR Conf. on Computer Vision & Graphic Image Processing
Lih-Jen, K., Yuan-Pei, L. (2005) Adaptive lossless image coding using least squares optimization with edge-look-ahead. IEEE Transactions on Circuits and Systems II: Express Briefs, 52(11): 751-755
Memon, N., Wu, X. (1997) Recent Developments in Context-Based Predictive Techniques for Lossless Image Compression. Computer Journal, 40(2 and 3): 127-136
Meyer, B., Tisher, P. (1997) TMW: A new method for lossless image compression. u: PCS97 Picture Coding Symposium, Berlin: VDE-Verlag GMBH, str. 533-538
Meyer, B., Tisher, P. (2001) Glicbawls: Grey level image compression by adaptive weighted least squares. u: Proceedings of Data Compression Conference
Rane, S.D., Sapiro, G. (2001) Evaluation of JPEG-LS, the New Lossless and Near-Lossless Still Image Compression Standard for Compression of High-Resolution Elevation Data. IEEE Transactions of Geosciences and Remote Sensing, 39 (10): 2298-2306
Salomon, D. (2007) Data compression: The complete reference. Springer
Schlossmacher, E.J. (1973) An Iterative Technique for Absolute Deviations Curve Fitting. Journal of the American Statistical Association, 68(344): 857-859
Starosolski, R. (2007) Simple fast and adaptive lossless image compression algorithm. Software: Practice and Experience, 37(1): 65-91
Taubman, D., Marcellin, M. (2004) JPEG2000: Image compression fundamentals, standards and practice. Boston: Kluwer Academic Publishers
Wienberger, M.J., Seroussi, G., Sapiro, G. (1996) LOCO-I: A low complexity, context-based, lossless image compression algorithm. u: Conference on data compression, Snowbird, mar/apr., USA, str. 140-149
Wu, X., Memon, N. (1996) CALIC: A context based adaptive lossless image codec. u: IEEE international conference on acoustics, speech, and signal processing, Atlanta, GA, May, vol. 4, str. 1890-1893
Ye, H., Deng, G., Devlin, J.C. (1998) Least squares approach for lossless image coding. u: IEEE international conference on Image processing, Proceedings, str. 901-904
 

O članku

jezik rada: engleski
vrsta rada: neklasifikovan
objavljen u SCIndeksu: 22.03.2013.

Povezani članci

Serb J Electr Engineering (2011)
Lossless predictive compression of medical images
Avramović Aleksej, i dr.

Serb J Electr Engineering (2011)
Improving quality of medical image compression using biorthogonal CDF wavelet based on lifting scheme and SPIHT coding
Beladgham Mohammed, i dr.