An efficient image focus measure based on perceptual features analysis

Abstract

Abstract In this paper, a new approach no-reference image quality assessment (NR-IQA) focus measure based on the additivity of Renyi Entropy is proposed. In human and computer vision, image quality must be quantified with human perception. Therefore, perceptual features such as image texture and structure are analyzed. It allows us to achieve a better correlation with the subjective quality assessment. The probability coefficients of images are obtained to extract the human visual system (HVS) features, and significant image details are evaluated. The randomness in the information of the image is observed by considering the additivity of Renyi Entropy. The majority of objective IQA algorithms evaluate the image quality by comparing the original image with the distorted. The presented approach is tested on artificial images by introducing a certain amount of blur without noise and in the presence of noise. The effectuality and performance of the presented method on real-time images show consistent responses under blurred and noisy conditions. Moreover, the proposed method is evaluated with three publicly available data sets such as LIVE, TID2013, and CSIQ. The presented method is compared with various existing techniques in the literature, and it is evident from the experiments that the method has better monotonicity and improved focus measures. The proposed approach achieved better performance metrics such as SROCC, KRCC, and PLCC. The computation time and complexity of the presented approach are reduced due to the logarithmic function.