Joint approach based quality assessment scheme for compressed and distorted images

Abstract

The Digital era is improving day by day. We can easily send and receive multimedia data, but it is challenging to know that the data is of actual quality or degraded and compressed. A similar problem arises in the medical imaging domain; it is too tedious to determine whether the image has a particular quality level or not to modify it further. So here we represent one specific method that is termed as “Joint”- Image Quality Estimation Approach as it is a combination of reference-based and no-reference-based Image Quality assessment methods; due to this fact, we termed it “Joint” approach. In some cases, the reference-based image quality assessment methods cannot predict the exact values because we don't know that the reference image that is considered to find the quality of a test image is an actual one or previously compressed. So, this will create a situation where we get the wrong IQA value for the test image. The method proposed by us can overcome this problem. First, we decide the quality of the reference image by using No-reference-based models. Then, we check the final IQA value for a test image with the reference-based models. We created a database of 72 chest images of COVID-19 infected patients and its four-level compressed images for the experiment. Results that are shown in this work are very effective and elaborated with proper justifications.