Evaluation of detection methods in an image edge error measure

• Authors: J. C. Garcia-Alvarez;J. E. Rodriguez;H. Führ
• Venue: Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
• Year: 2013

This work presents an evaluation of different detectors used for the Edge Error measure, which is one of the most used features in Image Quality Assessment (IQA). Among detectors, the proposed Wavelet--based Edge Detector (WEE) is also evaluated. the test is carried out for the quality assessment of images which are distorted by either JPEG, JPEG2000, Fast Fading, Gaussian Blur or White Noise process. The measures with high correlation values are considered as more accurate for human--based evaluations, namely perceptual. As a result, we found the following: firstly, the measure can be considered as perceptual for images distorted by JPEG and White Noise processes using any of the considered detectors, specially for the measure using wavelet--based edge detector (0:94 Spearman, 0:81 Kendall and 0:93 Pearson); secondly, Edge Error can be considered perceptual by using a Canny detector to evaluate Fast--Fading, Gaussian Blur, JPEG and White Noise distortion types. Finally, WEE achieve high correlation values for all considered distortion types, over performing the other considered edge error measures. As a conclusion, we select the WEE and Canny--based measures as perceptual, confirming that an edge enhancement method can improve the measurement accuracy.