Abstract: General purpose image compression algorithms do not fully exploit the redundancy of color graphical images because the statistics of graphics differ substantially from those of other types of images, such as natural scenes or medical images. This paper reports the results of a study of lossless predictive coding techniques specifically optimized for the compression of computer generated color graphics. In order to determine the most suitable color representation space for coding purposes the Karhunen-Loeve (KL) transform was calculated for a set of test images and its energy compaction ability was compared with those of other color spaces, e.g., the RGB, or the YUV signal spaces. The KL transform completely decorrelates the input color data for a given image and provides a lower bound on the color entropy. Based on the color statistics measured on a corpus of test images a set of optimal spatial predictive coders were designed. These schemes process each component channel independently. The prediction error signal was compressed by both lossless textual substitutional codes and statistical codes to achieve distortionless reproduction. The performance of the developed schemes is compared with that of the lossless function of the JPEG standard.!52
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