Contents I. Introduction
In our increasingly visual digital world, advanced technology have allowed for images to be captured, transmitted and stored over a range of devices easily and efficiently [1]. However, the processes of acquisition, digitization, compression, storage, transmission and display may introduce modifications to the original image. Therefore, the evaluation of image quality is particularly important. In general, there are two main categories of methods in the field of image quality assessment: subjective quality assessment and objective quality assessment [2]. The subjective image quality assessment methods are reliable for assessing image quality because the image is tested by human observers. But these methods are easily affected by human observers' educational background, vision and other factors. Furthermore, these methods are too inconvenient, expensive and slow to use in real-time applications. Based on mathematical theory, the objective quality assessment methods can obtain the accurate quantitative result of the image quality. However the objective evaluation sometimes lacks combination with HVS. Therefore, developing an objective evaluation that has a strong correlation with HVS is significant.
