Prediction by partial approximate matching for lossless image compression

Summary form only given. In this paper, motivated by the characteristics of natural images, we introduce PPAM - prediction by partial approximate matching, a modification of the context model used by the prediction by partial matching (PPM) family of text compression algorithms, for images. Unless there is a strong edge boundary, natural images usually contain locally homogeneous areas. However, the correlation within these local neighborhoods is usually not exact. Repeating contexts often occur in an approximate (rather than exact) form. In PPAM, we exploit these inexact contexts using methods from text pattern matching with errors. In PPM, context searching starts by looking for the maximum-order context and then escapes to shorter contexts until a match is found. PPAM uses a simple context quantization scheme to avoid potentially huge time and space requirements. We group the contexts based on the square of their Euclidean distances (SED) from a reference context. We tested the performance of the proposed PPAM on standard test images and good results were obtained.