# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 5 Conclusions  
IQA is important and useful for image acquisition and processing systems in many applications. In this paper, we focus on blurred IQA. We have proposed a novel NR IQA metric called NSSIM based on SSIM and re-blur theory. The proposed NSSIM takes the advantage of SSIM in mathematical convenience and expanded it from FR to NR. We blur the distorted image and take the re-blurred image as a reference. The definition of image blurriness is given by evaluating grayscale distribution. We score image quality by taking four parts of image features into consideration, including luminance, contrast, structural and blurriness. We discussed the impact of parameters of our algorithm on the performance. We tested the proposed NSSIM metric on four datasets. The experimental results show that NSSIM achieves promising performance and has good consistency of HVS. Compared to existing IQA models, NSSIM does not need reference or prior training or learning procedure, which makes it more time-efficient and convenient to apply. We also expanded the proposed metric to IQA for image restoration, which proves our metric is practically useful. We believe that NSSIM has a great potential to be applied in unconstrained environments.  
Conceptualization, H.Z., B.Y. and B.D.; Methodology, H.Z., B.Y. and B.D.; Software, B.Y. and B.D.; Validation, H.Z., B.Y. and B.D.; Formal Analysis, H.Z. and B.Y.; Writing--Original Draft Preparation, H.Z., B.Y. and B.D.; Visualization, B.Y.; Supervision, H.Z. and Z.J.; Project Administration, H.Z. and Z.J.; Funding Acquisition, H.Z. and Z.J.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**IQA Algorithm** & **Original Image** & **Blurred Image** & **[PERSON]** & **Kotera** \\ \hline PSNR & \(\backslash\) & 23.3116 & 25.1542 & 25.0633 \\ SSIM [1] & 1.0000 & 0.8132 & 0.8904 & 0.8951 \\ BIQI [14] & 46.8936 & 23.1284 & 49.8244 & 49.2987 \\ BRISQUE [4] & 32.3916 & 41.5441 & 36.6743 & 35.2054 \\ NIQE [17] & 21.5543 & 27.3656 & 19.2333 & 19.1788 \\ BLIIND-II [3] & 45.8900 & 30.5677 & 51.4986 & 51.6889 \\ SFA [20] & 12.8285 & 6.2353 & 6.9934 & 11.0588 \\ NSSIM(\(\times\)10\({}^{-4}\)) & 50.0684 & 16.7567 & 39.5872 & 42.2391 \\ \hline \hline \end{tabular}
\end{table}
Table 12: IQA algorithms performance on Group 2 restorations. Original image is the reference (sharp) image given by IVC [27] dataset. NSSIM represents the quality score given by the proposed metric.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**IQA Algorithm** & **Original Image** & **Blurred Image** & **[PERSON]** & **Kotera** \\ \hline PSNR & \(\backslash\) & 23.3116 & 25.1542 & 25.0633 \\ SSIM [1] & 1.0000 & 0.8132 & 0.8904 & 0.8951 \\ BIQI [14] & 46.8936 & 23.1284 & 49.8244 & 49.2987 \\ BRISQUE [4] & 32.3916 & 41.5441 & 36.6743 & 35.2054 \\ NIQE [17] & 21.5543 & 27.3656 & 19.2333 & 19.1788 \\ BLIIND-II [3] & 45.8900 & 30.5677 & 51.4986 & 51.6889 \\ SFA [20] & 12.8285 & 6.2353 & 6.9934 & 11.0588 \\ NSSIM(\(\times\)10\({}^{-4}\)) & 50.0684 & 16.7567 & 39.5872 & 42.2391 \\ \hline \hline \end{tabular}
\end{table}
Table 11: IQA algorithms performance on Group 1 restorations. Original image is the reference (sharp) image given by LIVE II [26] dataset. NSSIM represents the quality score given by the proposed metric.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Grant No. 61501009, 61771031, and 61371134), the National Key Research and Development Program of China (2016 YFB0501300 and 2016 YFB0501302), and the Fundamental Research Funds for the Central Universities.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.