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

4 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)380-383
Journal / PublicationIEEE Transactions on Consumer Electronics
Volume45
Issue number2
Publication statusPublished - 1999

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.