# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 4 Experiments
### Computational Complexity Analysis  
In many practical applications it is desired to estimate the quality of an input image online. Therefore, the computational complexity is also an important factor when evaluating a NR IQA model. The model complexity of the NR IQA model is shown in Figure 5. Our experiments run in Intel Core (TM) i5-3210M CPU @ 2.50 GHz and 4 GB RAM of ASUS A45V laptop. The MATLAB is R2012a (7.14) in the Windows. The 2D scatter plot shows the weighted average SROCC of four standard databases and running time of different methods for feature extraction of a 512 \(\times\) 512 image.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Database** & **Evaluation Index** & **Texture** & **Gradient** & **Texture + Gradient** \\ \hline \multirow{2}{*}{LIVE} & SROCC & 0.9343 & 0.7836 & **0.958** \\  & PLCC & 0.9350 & 0.8063 & **0.960** \\ \hline \multirow{2}{*}{CSIQ} & SROCC & 0.9122 & 0.7230 & **0.939** \\  & PLCC & 0.9213 & 0.7765 & **0.953** \\ \hline \multirow{2}{*}{TID2008} & SROCC & 0.9263 & 0.7234 & **0.940** \\  & PLCC & 0.9335 & 0.7568 & **0.944** \\ \hline \multirow{2}{*}{LIVEWC} & SROCC & 0.4976 & 0.4346 & **0.6053** \\  & PLCC & 0.5601 & 0.5321 & **0.6201** \\ \hline \multirow{2}{*}{CID2013} & SROCC & 0.7327 & 0.5234 & **0.7786** \\  & PLCC & 0.7561 & 0.5679 & **0.7987** \\ \hline \end{tabular}
\end{table}
Table 10: Comparison of contributions of the two groups of characteristics (the two best models indexes are shown in bold).  
\begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**IQA Methods**} & \multicolumn{2}{c}{**LIVEWC**} & \multicolumn{2}{c}{**CID2013**} \\ \cline{2-5}  & **SROCC** & **PLCC** & **SROCC** & **PLCC** \\ \hline BIQI & 0.5324 & 0.5479 & 0.6569 & 0.6757 \\ DIIVINE & 0.5148 & 0.5283 & 0.4972 & 0.5124 \\ BRISQUE & 0.5685 & 0.5864 & 0.4309 & 0.4783 \\ NIQE & 0.4292 & 0.4848 & 0.6007 & 0.6136 \\ BLIINDS2 & 0.4885 & 0.5064 & 0.4766 & 0.4987 \\ NFERM & **0.6055** & **0.5908** & **0.6281** & **0.6322** \\ ILNIQE & 0.5033 & 0.5127 & 0.4540 & 0.4634 \\ TLLFD & **0.6053** & **0.6201** & **0.7786** & **0.7987** \\ \hline \end{tabular}
\end{table}
Table 9: Performance comparison of two databases with different evaluation algorithms (the two best models indexes are shown in bold).  
The computational complexity of TLFFD is significantly lower than NIQE, ILNIQE, DIIVINE, and BLINDS2, and worse than BIQI and G-MLOG, which can be easily discerned in Figure 5. The main reasons are as follows. First, BIQI has only two features and extraction time is short. However, its performance is the worst among all the competing models. Second, GMLOG extracts 40 statistical features which combine gradient amplitude and Laplacian features. So, simple extraction process and high operation efficiency are obtained. Third, NIQE has 36 features and a higher computational complexity, leading to less quality prediction performance and slower running speed. Fourth, ILNIQE extracts five types of NSS features and uses them to learn the multivariate Gaussian model and predict the image quality. It has many parameters, which show performance is less competitive. Fifth, the dimension of DIIVINE features is up to 88, thus this model has long running time and low efficiency. Sixth, BLINDS2 using a natural scene statistics model of discrete cosine transform coefficients. The model process is complicated and requires a long running time. Overall, the calculation of TLLFD has low complexity and high efficiency.