Speeding up Lossless Image Compression: Experimental Results on a Parallel Machine

Arithmetic encoders enable the best compressors both for bi-level images (JBIG) and for grey scale and color images (CALIC), but they are often ruled out because too complex. The compression gap between simpler techniques and state of the art compressors can be significant. Storer extended dictionary text compression to bi-level images to avoid arithmetic encoders (BLOCK MATCHING), achieving 70 percent of the compression of JBIG1 on the CCITT bi-level image test set. We were able to partition an image into up to a hundred areas and to apply the BLOCK MATCHING heuristic independently to each area with no loss of compression effectiveness. On the other hand, we presented in [5] a simple lossless compression heuristic for gray scale and color images (PALIC), which provides a highly parallelizable compressor and decompressor. In fact, it can be applied independently to each block of 8x8 pixels, achieving 80 percent of the compression obtained with LOCO-I (JPEG-LS), the current lossless standard in low-complexity applications. We experimented the BLOCK MATCHING and PALIC heuristics with up to 32 processors of a 256 Intel Xeon 3.06GHz processors machine in Italy (avogadro.cilea.it) on a test set of large topographic bi-level images and color images in RGB format. We obtained the expected speed-up of the compression and decompression times, achieving parallel running times about twenty-five times faster than the sequential ones.