Joint approach based quality assessment scheme for compressed and distorted images

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.

Compressive Sensing-Based Computed Tomography Imaging: An effective approach for COVID-19 Detection

We know that COVID-19 has been considered a pandemic and various types of symptoms are analyzed by the doctors. Various cases belonging to COVID-19 are asymptomatic and due to this fact the disease is not analyzed at an initial stage and the condition of the patient will be critical. So, the purpose of this work is to provide a solution that will find out the highly precise test result of COVID-19. Magnetic Resonance Imaging, CT Scan & Lung Ultrasound are some of the methods which can provide the exact results for the testing. But the problem associated with these imaging modalities is that they are time-consuming and the data provided by these modalities are large enough to store or transmit. A compression technique is required which can reduce the time as well as data size. Computed Tomography with Compressive Sensing (CS) Technique is used as an approach to tackle the above-stated problem. To analyze the fact that this technique is efficient, we consider the Computed Tomography-based Chest images of COVID-19 infected patients and apply the CS technique (Basis Pursuit) with Discrete Cosine Transform as a representation basis and Gaussian as a measurement matrix. As a result of this study, we find out three parameters, PSNR, SSIM & FSIM, to visualize the efficiency of the reconstruction strategy. This work concludes that the Computed Tomography approach with the help of CS can be used for fast and efficient imaging for COVID-19 as well as other diseases of the same kind.