  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.
