An Algorithmic Approach for Multispectral Image Quality Assessment

Abstract

A multispectral image is one that captures image data at specific frequencies across the electromagnetic spectrum. The wavelengths may be separated by filters or by the use of instruments that are sensitive to particular wavelengths, including light from frequencies beyond the visible light range, such as infrared. Spectral imaging can allow extraction of additional information the human eye fails to capture with its receptors for red, green and blue. The terms image quality and image fidelity is used synonymously i.e. how close an image is to a given original or reference image. Image Quality Assessment (IQA) plays a fundamental role in the design and evaluation of imaging and image processing systems. Quality Assessment (QA) algorithms can be used to systematically evaluate the performance of different image compression algorithms that attempt to minimize the number of bits required to store an image, while maintaining sufficiently high image quality. An algorithmic approach to find the quality of images can be achieved through the techniques such as metrics calculations and models. The results predict how good the quality of the image is and hence can be used for imaging systems.