Lossless image compression is often performed through decorrelation, context modelling and entropy coding of the prediction error. This paper aims to identify the potential improvements to compression performance through improved decorrelation. Two adaptive prediction schemes are presented that aim
A NEW ALGORITHM FOR LOSSLESS STILL IMAGE COMPRESSION
β Scribed by TREES-JUEN CHUANG; JA-CHEN LIN
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 356 KB
- Volume
- 31
- Category
- Article
- ISSN
- 0031-3203
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β¦ Synopsis
This paper presents a spatial domain method for lossless still image compression using a new scheme: base switching (BS). The given image is partitioned into non-overlapping fixed-size subimages. Different subimages then get different compression ratios according to the base values of the subimages. In order to increase the compression ratio, a hierarchical technique is also used. It is found that the compression ratio of the proposed algorithm can compete with that of the VBSS and the international standard algorithms known as JBIG and Lossless JPEG. In addition, when the BS method is compared with the S#P method, which is an excellent frequency domain method that used EZW, although S#P method gains about 9% increase in the compression ratio, its encoding time (excluding I/O) is about three times longer than ours. The math theory needed to build up the proposed compression scheme is also provided.
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