On two-dimensional polynomial predictors

Jaakko Astola, Yrjö Neuvo, Corneliu Rusu

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

Many signals in nature and engineering systems can be locally modeled as relatively low degree polynomials, thus one-dimensional polynomial predictive filters are useful especially in time-critical systems. The goal of this paper is to introduce the two-dimensional polynomial predictive FIR filters and present few of their properties. First we discuss previous main results in one-dimensional polynomial predictive filters. Then we show how to find the coefficients and the system functions of the minimum area polynomial predictor, and we present the recursive form for the system function of a minimum area polynomial predictor. Finally, we approach the general form of 2D polynomial predictors.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEURASIP
Pages2254-2258
Number of pages5
ISBN (Electronic)9789082797053
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference - Amsterdam, Netherlands
Duration: 24 Aug 202028 Aug 2020
Conference number: 28

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
CountryNetherlands
CityAmsterdam
Period24/08/202028/08/2020

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