Chapter 1: Invited Addresses and Tutorials on Signals, Coding,
  Systems and Intelligent Techniques

Title: Improving the Performance of Adaptive Linear Prediction Coding (ALPC) Via Least Square Minimization

Author(s): Giovanni Motta, James A. Storer, Bruno Carpentieri

Address: Computer Science Dep., Brandeis Univ., 02254 Waltham, MA, USA | Computer Science Dep., Brandeis Univ., 02254 Waltham, MA, USA | Dip. di Informatica ed Applicazioni, Università di Salerno, 84081 Baronissi (SA), Italy

Reference: 12th International Workshop on Systems, Signals and Image Processing pp. 335 - 338

Abstract/Summary: Recently proposed state of the art lossless image compressors could be divided in two categories: single and double pass compressors. While in the first category, that includes CALIC and JPEG-LS, linear prediction is very little used; TMW, a state of the art double pass image compressor relies on linear prediction for its performance. ALPC is a single pass adaptive algorithm that uses context classification and multiple linear predictors, locally optimized on a pixel-by-pixel basis. On the average, ALPC obtains a compression ratio comparable to CALIC while improving on some images. In this paper we review the ALPC coder and improve its performance by using an approach based on least square minimization. The resulting algorithm is faster than the original ALPC and improves on its compression.

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