## 6 Conclusions  
The goal of FR-IQA is to predict digital images' perceptual quality with full access to the distortion-free, reference images. FR-IQA methods usually contain two stages: local image quality estimation and pooling. In addition, visual saliency is utilized in the pooling stage as weights for the weighted averaging of local image quality. Unlike this traditional approach, we applied visual saliency in the computation of local image quality, motivated by the fact that local image quality is determined both by local image degradation and  
Figure 4: Execution time (logarithmic scale) versus SROCC measured on CSIQ [8].  
Figure 3: Execution time (logarithmic scale) versus SROCC measured on KADID-10k [20].  
visual saliency simultaneously. Experimental results on four publicly available benchmark IQA databases showed that the proposed algorithm was able to outperform the state-of-the-art and was characterized by low computational costs. Future work could involve the incorporation of local and global saliency into a novel FR-IQA metric. The source code of the proposed method is available at: [[https://github.com/Skythianos/SG-ESSIM](https://github.com/Skythianos/SG-ESSIM)]([https://github.com/Skythianos/SG-ESSIM](https://github.com/Skythianos/SG-ESSIM)) (accessed on 12 June 2022).  
This research received no external funding.  
Not applicable.  
In this paper, the following publicly available benchmark databases were used: 1. KADID-10k: [[http://database.mmsp-kn.de/kadid-10k-database.html](http://database.mmsp-kn.de/kadid-10k-database.html)]([http://database.mmsp-kn.de/kadid-10k-database.html](http://database.mmsp-kn.de/kadid-10k-database.html)) (accessed on 12 May 2022), 2. TID2013: [[http://www.ponomarenko.info/tid2013.htm](http://www.ponomarenko.info/tid2013.htm)]([http://www.ponomarenko.info/tid2013.htm](http://www.ponomarenko.info/tid2013.htm)) (accessed on 12 May 2022), 3. TID2008: [[http://www.ponomarenko.info/tid2008.htm](http://www.ponomarenko.info/tid2008.htm)]([http://www.ponomarenko.info/tid2008.htm](http://www.ponomarenko.info/tid2008.htm)) (accessed on 12 May 2022), 4. CSIQ: [[https://isp.uves/data_quality.html](https://isp.uves/data_quality.html)]([https://isp.uves/data_quality.html](https://isp.uves/data_quality.html)) (accessed on 12 May 2022).  
We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.  
The author declares no conflict of interest.  
The following abbreviations were used in this manuscript:  
\begin{tabular}{l l} AGN & additive Gaussian noise; \\ ANC & additive noise in color components; \\ CA & chromatic aberrations; \\ CC & contrast change; \\ CCS & change in color saturation; \\ CN & comfort noise; \\ CNN & convolutional neural network; \\ CPU & central processing unit; \\ DEN & image denoising; \\ FR-IQA & full-reference image quality assessment; \\ GB & Gaussian blur; \\ GPU & graphics processing unit; \\ HFN & high frequency noise; \\ HVS & human visual system; \\ ICQD & image color quantization with dither; \\ IN & impulse noise; \\ IQA & image quality assessment; \\ JGTE & JPEG transmission error; \\ JPEG & joint photographic experts group; \\ KROCC & Kendall's rank order correlation coefficient; \\ LCNI & lossy compression of noisy images; \\ MGN & multiplicative Gaussian noise; \\ MN & masked noise; \\ MOS & mean opinion score; \\ MS & mean shift; \\ MSE & mean squared error; \\ NEPN & noneccentricity pattern noise; \\ NR-IQA & no-reference image quality assessment; \\ PLCC & Pearson's linear correlation coefficient; \\ PSNR & peak signal-to-noise ratio; \\ QN & quantization noise; \\ RR-IQA & reduced-reference image quality assessment; \\ \end{tabular}