Title :
Utilizing gray code and pixel decomposition to improve PPM compression performance
Author :
Noorwali, Seereen ; El-Sakka, Mahmoud R.
Author_Institution :
Comput. Sci. Dept., Univ. of Western Ontario, London, ON, Canada
Abstract :
In digital gray-scale images, nearby pixels tend to have similar intensities, which normally are encoded using the ordinary binary code. However, the correlation between bits in such binary representation does not reflect how close these intensity values to each other. In this research, we proposed to utilize Gray code, instead of the ordinary binary code, and to decompose the image into multiple sub-images (two, four and eight). Each decomposed sub-image will be compressed separately using Prediction by Partial Matching (PPM) scheme, which is an adaptive statistical context-based lossless scheme. The proposed scheme is tested on 8 natural scene images. Results show improvement in compression performance when Gray code is utilized and the image is decomposed, compared to utilizing the ordinary binary code under the same conditions.
Keywords :
Gray codes; data compression; image coding; image matching; image representation; natural scenes; statistical analysis; PPM compression performance; PPM scheme; adaptive statistical context-based lossless scheme; binary code; binary representation; digital gray-scale image; gray code; image decomposition; multiple subimage; natural scene image; pixel decomposition; prediction by partial matching scheme; Complexity theory; Image coding; Gray code; Prediction by Partial Matching (PPM); lossless image compression; pixel decomposition; raster scan;
Conference_Titel :
Computer Engineering Conference (ICENCO), 2011 Seventh International
Conference_Location :
Giza
Print_ISBN :
978-1-4673-0730-7
DOI :
10.1109/ICENCO.2011.6153940