Limitation and Challenges of Image Quality Measurement Subjectivelyrated image databases have become increasingly popular in the evaluation of image quality measurement algorithms. Several groups recently have improved their metricsu0027 performance in matching these databases, using particular HVS (human visual system) properties or image statistical models. However, it is difficult to know whether these improvements are due to progress towards mimicking the perceptual properties, or are due to matching some characteristics of the databases. This paper demonstrates an inherent limitation in using such databases, showing that our very simple metric, built on the contrast masking effect, is able to perform as good as many stateoftheart metrics. It is also argued that existent databases neither contain enough images with particularly biased distortions to test the significance of single HVS property, nor cover diverse distortion types to reflect the requirement of emerging applications.

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.