Open AccessInterpolation based on context modeling for hierarchical compression of multidimensional signals
Author(s) -
М. В. Гашников
Publication year - 2018
Publication title -
kompʹûternaâ optika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.491
H-Index - 29
eISSN - 2412-6179
pISSN - 0134-2452
DOI - 10.18287/2412-6179-2018-42-3-468-475
Subject(s) - interpolation (computer graphics) , compression (physics) , data compression , context (archaeology) , algorithm , dimension (graph theory) , computer science , multivariate interpolation , mathematics , artificial intelligence , computer vision , bilinear interpolation , motion (physics) , paleontology , materials science , pure mathematics , composite material , biology
Context algorithms for interpolation of multidimensional signals in the compression problem are researched. A hierarchical compression method for arbitrary dimension signals is considered. For this method, an interpolation algorithm based on the context modeling is proposed. The algorithm is based on optimizing parameters of the interpolating function in a local neighborhood of the interpolated sample. At the same time, locally optimal parameters found for more decimated scale signal levels are used to interpolate samples of less decimated scale signal levels. The context interpolation algorithm is implemented programmatically as part of a hierarchical compression method. Computational experiments have shown that using a context interpolator instead of an average interpolator makes it possible to significantly improve the efficiency of hierarchical compression.