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.

Lossless image coding via adaptive linear prediction and classification

CARPENTIERI, Bruno
2000

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4677142
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