Abstract
Abstract
The assessment of image quality provides the confidentiality, safety, quality and transparency of the obtained images. At the same time, there are many existing quality metric approaches that belong to the group of modification alteration procedures (pixel-based metric). These techniques have not been well-matched with perceptual image quality. The Perceptual Human Visual System (HVS) aspect drives our approach; The human visual system (HVS) is the best judge of image quality. This paper presents a new reduced reference image quality metric in the spatial domain that reduces the complexity of the image quality assessment. From the reference and distortion images, we extract four intrinsic features, namely, contrast, entropy, histogram and standard deviation. Then, we build the formal concept analysis matrix for reference and distortion images. Finally, we compare the obtained matrixes to evaluate the image quality. The performance of the proposed technique is assessed using LIVE, TID2013 and CSIQ datasets, and the obtained results are compared in terms of PSNR, SSIM and NCC metrics. Also, a comparison with more recent and relevant approaches is performed to highlight the superior performance of our proposed approach, the experimental results indicated that the proposed approach provides efficient performance among the compression, Gaussian blur, Contrast and add noise distortion types.