1. Field
This disclosure relates to methods for image compression, more particularly to lossless compression method for computer-generated document images.
2. Background
Efficient storage and transmission of electronic images typically require some sort of compression. Compression techniques are generally referred to as lossless or lossy. Lossless compression means that the resulting image is identical to the input image. Lossy compression techniques may have some slight variation between the output image and the input image. The amount of variation between the input image and the output image is determined by the quality of the compression technique. The overall amount of compression achieved is determined by the efficiency of the compression technique. Generally, lossless techniques do not achieve as high efficiency as lossy techniques.
Examples of compression techniques are wide and varied. Compression techniques may compress binary text images, such as run-length encoding used in CCITT (Coinite International Telephonique et Telegraphique) group 3/4 standards and binary adaptive arithmetic coding used in JBIG (Joint Bi-level Image Experts Group) standard for facsimile transmission. Others may compress graphic images, such an Lempel-Ziv-Welch (LZW) compression, used in the Graphics Image File format (GIF). Still photographic images are typically compressed using the JPEG (Joint Photographic Experts Group) standard. However, JPEG is used mostly for lossy compression of photographic images, in which there will be a variation between the output image and the input image. Lossless compression methods for photographic images include JPEG-LS standard and a process called CALIC.
JPEG-LS is based upon the Low Complexity Lossless Compression for Images (LOCO-I) and is optimized for photographic images only. CALIC (Context-based Adaptive Lossless Image Coding) switches between either binary or continuous-tone and thus is capable of compressing both binary text and photographic images. More information on CALIC can be found in U.S. Pat. No. 5,903,676, issued May 11, 1999.
None of above-described methods are particularly designed to handle compound images, which are typically comprised of a mix of text, graphics, and photographic images. The few existing compression algorithms for compound image sources belong to the Mixed Raster Content (MRC) model-based approaches. MRC model employs a layer representation to decompose a document into three layers, e.g. background, foreground and mask. An example of this technique is shown in U.S. Pat. No. 5,778,092, issued Jul. 7, 1998. However, MRC-based approach is a lossy technique and suffers from the intrinsic redundancy of layer representations.
Therefore, it would seem that a lossless image compression technique that can efficiently handle compound images would be useful.