Paper Info
Title
Image interpolation by adaptive 2-D autoregressive modeling
Abstract
This paper presents a new interpolation algorithm based on the adaptive 2-D autoregressive modeling. The algorithm uses a piece-wise autoregressive (PAR) model to predict the unknown pixels of high resolution image. For this purpose, we used a block-based prediction model to predict the unknown pixels. The unknown pixels are categorized into three categories and they are predicted using predictors of different structure and order. Prediction accuracy and the visual quality of the interpolated image depend on the size of the window. We experimentally found an appropriate window size and have shown that subjective as well as objective (PSNR) quality of the high resolution (HR) images is same, on an average, as that of the competitive such method reported in literature and also the method is a single pass.
Year
DOI
Venue
2010
10.1117/12.855785
Proceedings of SPIE
Keywords
Field
DocType
Autoregressive model,Predictors,LMMSE,FDWT,BDWT,Covariance
Single pass,Autoregressive model,Computer vision,Interpolation,Image processing,Image quality,Digital image,Pixel,Artificial intelligence,Image scaling,Mathematics
Conference
Volume
ISSN
Citations 
7546
0277-786X
5
PageRank 
References 
Authors
0.66
0
3
Name
Order
Citations
PageRank
Vinit Jakhetiya110212.89
ashok kumar281.13
Anil Kumar Tiwari36517.51