Lossless image compression by using gradient adjusted prediction and burrows-wheeler transformation
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 380-383 |
Journal / Publication | IEEE Transactions on Consumer Electronics |
Volume | 45 |
Issue number | 2 |
Publication status | Published - 1999 |
Link(s)
Abstract
In generally text compression techniques cannot be used directly in image compression because the model of text and image are different. Recently, a new class of text compression, namely, blocksorting algorithm which involves Burrows and Wheeler transformation (BWT) gives excellent results in text compression. However, if we apply it directly into image compression, the result is poor. Surprisingly, good results can be obtained if we employ a prediction model such as the one defined in JPEG standard before the BWT algorithm. Thus, the predictive model plays a critical role in the compression process. To further improve the compression efficiency, we use Gradient Adjusted Prediction (GAP). Experimental results show that the proposed method is better than lossless JPEG and some LZ-based compression methods. © 1999 IEEE.
Citation Format(s)
Lossless image compression by using gradient adjusted prediction and burrows-wheeler transformation. / Ng, K. S.; Cheng, L. M.
In: IEEE Transactions on Consumer Electronics, Vol. 45, No. 2, 1999, p. 380-383.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review