JBIG2


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AcronymDefinition
JBIG2Joint Bilevel Image Experts Group
References in periodicals archive ?
Table I shows that the transmission energy consumption is highest for transmitting segmented images and is lowest for JBIG2 compression.
Contrary to Table I, the total energy consumption of JBIG2 is high compared to Group 4 and Gzip_Pack and the reason for this is the high compression time.
Though the compression efficiency of JBIG2 is the highest but due to its long compression time, the total energy consumption is high.
However, the mask layer is a bi-level image, containing many characters, i.e., recurring symbols, for which JBIG or JBIG2 obtain better performances, as mentioned above.
In both solutions, JBIG2 was used for mask compression.
It is concluded in [7] that JBIG2, CCITT Group 4 and Gzip_pack provide better compression efficiency compared to other Bi-level image compression methods.
A number of lossless Bi-level image compression algorithms exist and some of them are the Ziv-Lempel algorithms [15], Efficient partitioning into rectangular regions [16], Arithmetic coding [17], CCITT Group 4 [9], JBIG2 [8] etc.
Fortunately, the location map which is a binary data can be compressed using a lossless compression algorithm such as JBIG2. As a conclusion, its maximum possible embedding capacity has to be below 0.5 bpp.