A fast algorithm for entropy estimation of grey-level images

Examines an efficient approach to the calculation of the entropy of long binary and nonbinary 1D information sequences. The entropy calculation is accomplished in a time which is linear in the sequence length. The method is expanded to estimate the entropy of grey-level images which, under raster scanning, may be represented as 1D information sequences. The entropy estimate obtained depends on the image scanning method employed, and consequently, in order to achieve a greater reduction in the bit rate, the scanning should be done in the direction of the highest adjacent pixel statistical dependence. Depending on the image statistics, it is shown that uniform luminance requantization of an image may not lead to an appreciable reduction in the bit rate. The algorithm discussed can be applied to areas such as image compression and string entropy estimation in genetics