Human Visual System Quality Assessment in The Images Using the IQA Model Integrated with Automated Machine Learning Model

Abstract

With rapid growth in multimedia technology there arises the need for processing millions of images. Distortion and noises were introduced in images during image acquisition, storage, transmission, restoration, and reproduction. Image quality assessment (IQA) is used to estimate the degree of distortion introduced in images. Subjective IQA denotes assessment of the quality of the image by humans. Since subjective assessment is costly and time consuming and impractical for real time visual quality monitoring, controlling and integration, hence a need for reliable objective IQA method arises. Simple objective quality assessment methods are Peak signal to noise ratio (PSNR) and mean square error (MSE) which measures the statistical information of an image. In these methods accuracy level is very low. The performance is evaluated based on improving the image quality assessment. The proposed image quality assessment scheme uses the Weighted IQA model for the quality assessment of the images. The proposed WIQA model estimates the image distortion with the conventional Human Visual System (HVS) to measure the objectives. The estimated model comprises of the middle level representation of images with the recognition of objects, classification, and segmentation. The performance of the proposed WIQA model is evaluated with the consideration of the different image’s dataset. The estimation of the parameters expressed that the proposed model achieves the reduced RMSE compared minimal value of 0.83.