Lossless image coding via adaptive linear prediction and classification

In the past years, there have been several improvements in lossless image compression. All the recently proposed state-of-the-art lossless Image compressors can be roughly divided into two categories: single and double-pass compressors. Linear prediction is rarely used in the first category, while TMW [7], a state-of-the-art double-pass image compressor, relies on linear prediction for its performance. We propose a single-pass adaptive algorithm that uses context classification and multiple linear predictors, locally optimized on a pixel-by-pixel basis. Locality is also exploited in the entropy coding of the prediction error. The results we obtained on a test set of several standard images are encouraging. On the average, our ALPC obtains a compression ratio comparable to CALIC [20] while improving on some images. © 2000 IEEE Publisher item Identifier S0018-9219(00)09992-8.