FRIQA  Permutation Entropy Deviation Index In this work, a new objective image quality assessment (IQA) framework, based on the working principle of permutation entropy (PE) is proposed. The framework is titled as Permutation Entropy Deviation Index (PEDI). The idea is to design an IQA framework that should be highly accurate as well as computationally efficient; making it viable to be used with different image processing applications, for visual quality assessments. The proposed model make use of the PE that helps in detecting and visualizing changes related to structures with correlation between successive samples instead of considering magnitudes of the signal. Thus, the proposed approach uses this property to efficiently predict image quality. The PE exploits the global variations in the local quality map for image quality assessment. With standard deviation as the pooling strategy, it is noted that permutation entropy between reference and distorted images can predict image quality with high measures of accuracy. Experimental results on subjective database, CSIQ, have shown that the proposed model outperforms most of existing SOTA image quality assessment models and highly correlates with subjective judgements.

Perceptual Image Quality Assessment: Recent Progress and Trends Image quality assessment (IQA) is useful in many visual processing systems but challenging to perform in line with the human perception. A great deal of recent research effort has been directed towards IQA. In order to overcome the difficulty and infeasibility of subjective tests in many situations, the aim of such effort is to assess visual quality objectively towards better alignment with the perception of the Human Visual system (HVS). In this work, we review and analyze the recent progress in the areas related to IQA, as well as giving our views whenever possible. Following the recent trends, we discuss the engineering approach in more details, explore the related aspects for feature pooling, and present a case study with machine learning.