A Result Analysis of Feature-Enriched Completely Blind Image Quality Evaluator

Abstract

Existing –blind image quality assessment (BIQA) ways are principally opinion-aware. Measuring of image or video quality is crucial for several image-processing algorithms, like acquisition, compression, restoration, improvement, and reproduction. Traditionally, image quality assessment (QA) algorithms interpret image quality as similarity with a “reference” or “perfect” image. Here, we tend to aim to develop an opinion unaware BIQA methodology that may compete with, and maybe outperform, the present opinion-aware ways. By integration the features of natural image statistics derived from multiple cues, we tend to learn a multivariate Gaussian model of image patches from a group of pristine natural pictures.