Paper Info
Title
Performance Evaluation of Edge-Directed Interpolation Methods for Images
Abstract
Many interpolation methods have been developed for high visual quality, but fail for inability to preserve image structures. Edges carry heavy structural information for detection, determination and classification. Edge-adaptive interpolation approaches become a center of focus. In this paper, performance of four edge-directed interpolation methods comparing with two traditional methods is evaluated on two groups of images. These methods include new edge-directed interpolation (NEDI), edge-guided image interpolation (EGII), iterative curvature-based interpolation (ICBI), directional cubic convolution interpolation (DCCI) and two traditional approaches, bi-linear and bi-cubic. Meanwhile, no parameters are mentioned to measure edge-preserving ability of edge-adaptive interpolation approaches and we proposed two. One evaluates accuracy and the other measures robustness of edge-preservation ability. Performance evaluation is based on six parameters. Objective assessment and visual analysis are illustrated and conclusions are drawn from theoretical backgrounds and practical results.
Year
Venue
Field
2013
CoRR
Nearest-neighbor interpolation,Multivariate interpolation,Pattern recognition,Convolution,Computer science,Interpolation,Stairstep interpolation,Robustness (computer science),Artificial intelligence,Image scaling,Bilinear interpolation
DocType
Volume
Citations 
Journal
abs/1303.6455
2
PageRank 
References 
Authors
0.37
13
4
Name
Order
Citations
PageRank
Shaode Yu1195.60
Qingsong Zhu211613.96
Shibin Wu3122.73
Yaoqin Xie412521.70