Development of image quality assessment measure

Abstract

Without the actual images as reference, human as an observer can simply detect the quality without quantifying a distorted image. Quality assessment with no reference is a very challenging and difficult task in modern research field in computer vision and digital image processing. Many research works have been done for this purpose and mostly, they are criticized for not correlating with desired quality assessment model. Noises and distortions effect the sense of human as well as machine to detect and extract the information contained in an image. So, to enhance, control and ensure the quality of images, quality measurement becomes most important. Image Quality Assessment (IQA) models have important practical significance at every stage of image processing. We developed an efficient and more accurate noreference IQA model for general purpose which achieves improved quality prediction. The model depends upon the extracted combined features and entropies spatially and spectrally by using some well-known machine learning algorithms.