Measurement of Image Quality plays an important role in numerous image processing applications such as forensic science, image enhancement, medical imaging, etc. In recent years, there is a growing interest among researchers in creating objective Image Quality Assessment (IQA) algorithms that can correlate well with perceived quality. A significant progress has been made for full reference (FR) IQA problem in the past decade. In this paper, we are comparing 5 selected FR IQA algorithms on TID2008 image datasets. The performance and evaluation results are shown in graphs and tables. The results of quantitative assessment showed wavelet-based IQA algorithm outperformed over the non-wavelet based IQA method except for WASH algorithm which the prediction value only outperformed for certain distortion types since it takes into account the essential structural data content of the image.

Measurement of Image Quality plays an important role in numerous image processing applications such as forensic science, image enhancement, medical imaging, etc. In recent years, there is a growing interest among researchers in creating objective Image Quality Assessment (IQA) algorithms that can correlate well with perceived quality. A significant progress has been made for full reference (FR) IQA problem in the past decade. In this paper, we are comparing 5 selected FR IQA algorithms on TID2008 image datasets. The performance and evaluation results are shown in graphs and tables. The results of quantitative assessment showed wavelet-based IQA algorithm outperformed over the non-wavelet based IQA method except for WASH algorithm which the prediction value only outperformed for certain distortion types since it takes into account the essential structural data content of the image.

The research on image quality assessment (IQA) has been become a hot topic in image processing. Many studies show that HVS edge information plays crucial role when human perceive the quality of an image. The proposed metric is called HEPSI. The method from HaarPSI metric is combined with edge structural similarity and a contrast map is added for pooling the structural similarity map, Validation is taken by comparing HEPSI with the well-known state-of-the-art IQA metrics: PSNR, SSIM, MSSIM, FSIM and HaarPSI over the LIVE database. Experiment shows that HEPSI achieved better performance than other 5 IQA metrics.