Abstract
Markov model approximation of digital images gives very low entropy, but requires a huge amount of storage to maintain the frequency table for encoding and decoding. Therefore, it has only been used for binary picture compression so far.
We have developed a high performance DPCM-based predictive lossless image coder, and have combined this technique with a Markov model, which provides a better compression ratio for gray scale images.
The basic idea is, encode the first part (this size is variable) of an image using the normal DPCM-based encoder and concurrently use these data as a Markov model training set. Then the latter half will be encoded using the Markov model with the assistance of the DPCM coder.
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References
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'crush’ package is available via anonymous ftp at dftnic.gsfc.nasa.gov: disk$moe:[anonymous.files.software.unix.crushv3]: crush_v3_tar.Z
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© 1995 Springer-Verlag Berlin Heidelberg
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Takamura, S., Takagi, M. (1995). A hybrid lossless compression of still images using Markov model and linear prediction. In: Braccini, C., DeFloriani, L., Vernazza, G. (eds) Image Analysis and Processing. ICIAP 1995. Lecture Notes in Computer Science, vol 974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60298-4_259
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DOI: https://doi.org/10.1007/3-540-60298-4_259
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