Natural Image Quality Assessment Based on Visual Biological Cognitive Mechanism

1.1. Background

Images are visual basis that people can apperceive the real world. It is an effective way in which human beings can achieve, express and communicate information each other. With the development of images processing technology and visual cognitive scientific theory, much more progress has been made in NR-IQA applications. (Venkatanath,2015) put forward a novel no-reference perception-based images quality evaluator (PIQUE) algorithm on the basis of extracting local features just in spatial domain, imitated human behavior, and employed multivariate Gaussian model (MGM) as probability distribution function (PDF) of natural images features. (Zhao, 2015) proposed an efficient general-purpose blind/no reference image quality assessment (NR-IQA) algorithm with frequency domain features of phase congruency values and local spectral entropy values. In the inspired by inspired by the sparse representation of visual scenes in the primary visual cortex of the human visual system (HVS), (Priya,2016) presented a no reference image quality assessment (NR-IQA) method. (Plataniotis, 2016) put forward parametric models in which described the universal features of chromatic data from natural images, and computed the correlation of chromatic characteristics like hue, saturation, opponent angle and spherical angle between spatially adjacent pixels with the help of color invariance descriptors, and so on. All the algorithm played an advantageous role in images quality assessment with no reference.