Adaptive Sequential Prediction of Multidimensional Signals With Applications to Lossless Image Coding

Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, ON, Canada
IEEE Transactions on Image Processing (Impact Factor: 3.63). 02/2011; 20(1):36 - 42. DOI: 10.1109/TIP.2010.2061860
Source: IEEE Xplore


We investigate the problem of designing adaptive sequential linear predictors for the class of piecewise autoregressive multidimensional signals, and adopt an approach of minimum description length (MDL) to determine the order of the predictor and the support on which the predictor operates. The design objective is to strike a balance between the bias and variance of the prediction errors in the MDL criterion. The predictor design problem is particularly interesting and challenging for multidimensional signals (e.g., images and videos) because of the increased degree of freedom in choosing the predictor support. Our main result is a new technique of sequentializing a multidimensional signal into a sequence of nested contexts of increasing order to facilitate the MDL search for the order and the support shape of the predictor, and the sequentialization is made adaptive on a sample by sample basis. The proposed MDL-based adaptive predictor is applied to lossless image coding, and its performance is empirically established to be the best among all the results that have been published till present.

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Available from: Guangtao Zhai, Jan 22, 2016
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    • "For one-part coding, normalized maximum likelihood (NML) distribution can be estimated to find the optimal class of models[31]. MDL is prevalent in a variety of signal processing applications, e.g., wireless sensor array processing[32], autoregressive models[33], sparse coding[34], lossless image coding[35], and etc. The interlaced correlations of heterogeneous data can be further exploited for high-performance compression. "
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