Use of Local Minimization for Lossless Gray Image Compression

A novel approach for the lossless compression of gray images is presented. A prediction process is performed followed by the mapping of prediction residuals. The prediction residuals are then split into bit–planes. Twodimensional (2D) differencing operation is applied to bitplanes prior to segmentation and classification. Performing an Exclusive-OR logic operation between neighboring pixels in the bit planes creates the difference image. The difference image can be coded more efficiently than the original image whenever the average run length of black pixels in the original image is greater than two. The 2d difference bit-plane is divided in to windows or block of size 16*16 pixels. The segmented 2d difference image is partitioned in to non-overlapping rectangular regions of all white and mixed 16*16 blocks. Each partitioned block is transformed in to Boolean switching function in cubical form, treating the pixel values as a output of the function. Minimizing these switching functions using QuineMcCluskey minimization algorithm performs compression.