Some preliminary results are presented on a simple progressive code for scanned 300 dpi documents. The effectiveness of the standard CCITT codes as a function of document resolution is determined. Extensions to the technique of document resolution are examined. For higher-resolution scanning, e.g. 300 dpi, a pyramid of the information source which allows for a tradeoff between the compression achieved and the quality of the representation is proposed. A progressive code that first generates a color block image that may be useful for identification of the document is proposed. Progression in quality corresponds to 100 dpi binary images, 100 dpi gray scale images, 300 dpi binary images, and finally 300 dpi gray-scale images. Each stage in the progressive code is based on the data available at the previous stage. The performance of the progressive code is examined.>

Algorithms for preprocessing, global modeling and segmentation, local modeling and representation, and binary encoding of facsimile images are proposed, and their performance is examined. Some binary morphological operations that can be used to improve the quality and increase the compressibility of binary images and the modeling of binary images are discussed. The techniques are incorporated into a set of comprehensive encoding schemes, the PCSE codes. Their effectiveness in compressing the International Telegraphy and Telephony Consultative Committee (CCITT) binary images is demonstrated. The PCSE code using preprocessing and color shrinking prior to encoding is shown to outperform the READ code by from 7 to 42% for the standard CCITT test images with a very small change in image quality.>

The emphasis of many algorithms that have been proposed for the compression of binary images has been the efficient coding of local redundancy in data. We propose that increased compression may be achieved by a decomposition of the compression problem into two steps. The goal of the first step is to extract the global redundancy in an image. This is achieved by a color shrinking algorithm, The goal of the second step is to code the resulting localized data.