Most current image quality assessment methods based on discrete wavelet transform use linear wavelet kernels [23, 24, 34].
Generally, the advantage of including visual attention-based modeling appears to be larger for methods designed for video quality assessment than for image quality assessment methods.
This model has been successfully applied in quality assessment and image coding schemes M. Carnec, P. Le Callet, D. Barba, An image quality assessment method based on perception of structural information, IEEE Internat.
The novel No-Reference Image Quality Assessment method and adequate criterion were introduced in this paper.
Besides PSNR, another objective image quality assessment method called structural similarity indexing (SSIM) [39, 40] is used to evaluate the performance of the proposed method.
In this study, a full-reference image quality assessment method called Riesz transform and Visual contrast sensitivity-based feature SIMilarity index (RVSIM) is proposed.
Since the tracking result in the last frame provides the latest variation information of the target, we employ an image quality assessment method to obtain the similarity scores in the second part, and the spatial context information is also exploited.
In Table 1 we summarize the articles above reviewed indicating the model assumptions and imaging conditions for which they have been developed and tested as well as the image quality assessment method used to evaluate the corresponding results.
Objective image quality (IQA) assessment methods can be categorized as full reference (FR), reduced reference (RR), and no reference (NR) depending on whether a reference, partial information about a reference, or no reference is used for calculation.
Figure 1 An overview of NR image and video quality assessment methods.
