[Paper Review] Performance Evaluation of Edge-Directed Interpolation Methods for Images

Shaode Yu, Qingsong Zhu+2 more|arXiv (Cornell University)|Mar 26, 2013

Advanced Image Processing Techniques19 references4 citations

TL;DR

This paper evaluates four edge-directed interpolation methods—NEDI, EGII, ICBI, and DCCI—against traditional bi-linear and bi-cubic interpolation on image quality preservation. It introduces two novel metrics to assess edge-preserving accuracy and robustness, demonstrating that edge-directed methods significantly outperform traditional approaches in structural fidelity and edge detail retention, especially in high-frequency regions.

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.

Motivation & Objective

• To assess the performance of edge-directed interpolation methods in preserving image structures and edges compared to traditional methods.
• To address the lack of standardized parameters for measuring edge-preserving capability in interpolation algorithms.
• To propose two new metrics—accuracy and robustness—specifically for evaluating edge-preserving performance.
• To conduct a comprehensive evaluation using both objective metrics and visual analysis across diverse image categories.
• To provide theoretical and empirical justification for the superiority of edge-adaptive methods in high-quality image reconstruction.

Proposed method

• The study compares five interpolation methods: NEDI, EGII, ICBI, DCCI, bi-linear, and bi-cubic interpolation.
• Edge-directed methods use local image structure and gradient information to guide interpolation along edges, minimizing blur and ringing.
• A new accuracy metric is defined based on edge alignment and structural similarity in reconstructed images.
• A robustness metric is introduced to evaluate consistency of edge preservation across varying image content and noise levels.
• Performance is evaluated using six quantitative parameters, including PSNR, SSIM, and edge preservation scores.
• Visual analysis is conducted alongside objective metrics to validate results across different image types and scales.

Experimental results

Research questions

• RQ1 How do edge-directed interpolation methods compare to traditional bi-linear and bi-cubic methods in preserving image edges and structures?
• RQ2 To what extent do existing metrics fail in capturing the true edge-preserving capability of interpolation algorithms?
• RQ3 Can the proposed accuracy and robustness metrics effectively quantify edge preservation in image interpolation?
• RQ4 Which interpolation method achieves the best balance between visual quality and structural fidelity across diverse image content?
• RQ5 What is the theoretical and empirical basis for the improved performance of edge-adaptive methods over conventional approaches?

Key findings

• Edge-directed methods such as NEDI, EGII, ICBI, and DCCI consistently outperform bi-linear and bi-cubic interpolation in preserving fine image structures and edges.
• The proposed accuracy metric successfully identifies methods that maintain edge alignment and reduce geometric distortion.
• The robustness metric reveals that DCCI and EGII exhibit more consistent edge preservation across varying image complexities and noise conditions.
• Visual analysis confirms that edge-directed methods reduce blurring and ringing artifacts, particularly along high-contrast boundaries.
• Quantitative results show that NEDI and EGII achieve the highest SSIM and PSNR values on test images, indicating superior structural similarity.
• The study demonstrates that traditional interpolation methods degrade significantly in edge preservation, especially in high-frequency regions.