A New Edge and Pixel-Based Image Quality Assessment Metric for Colour and Depth Images

Abstract

Measuring the quality of digital image is a complicated and importance task in image processing. This task is possible using Image Quality Assessment (IQA) metrics. Among them Pixel and edge-based IQA metrics are so crucial in dealing with a digital image. So, combination of edge and pixel features could handle not all but, almost all aspects of an image. Most recently using edge-based image quality metrics are popular, due to weakness of traditional image quality assessment metrics such as Peak Signal-to Noise Ratio. Also, majority of IQA metrics are belonged to color images, but recently new metrics for depth images are emerged. This paper proposes a new Full-Reference image quality assessment metric for color and depth images, which works based on edge and pixel features. Proposed method is a combination of improved Edge Based IQA and Peak Signal-to Noise Ratio methods. Proposed method is called Edge and Pixel-based Image Quality Assessment Metric (EPIQA). The system is validated using famous and benchmark performance metrics or quality measures such as Spearman Rank-Order Correlation Coefficient (SROCC), along with comparison with other similar methods on well-known related databases. Color databases have proper and diverse number of noises, but there is no proper depth noisy database, which it is decided to make one. Proposed method returned promising and satisfactory results in different tests.