# LightVSR: A Lightweight Video Super-Resolution Model with Multi-Scale Feature Aggregation
## 4 Results
### Comparison of Feature Fusion Methods  
In this section, we compare the proposed multi-input attention mechanism with the concatenation mechanism, where tensors are concatenated, as well as the direct tensor addition mechanism, which involves simply adding the tensors together. The structural comparisons of these three mechanisms are depicted in Figure 7.  
As illustrated in Table 3, our proposed multi-input attention mechanism achieves the best performance compared to the concatenation and direct tensor addition mechanisms. This is because the proposed attention mechanism can adaptively assign weights to different channels for achieving more refined and efficient feature fusion compared to traditional \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{} & \multicolumn{6}{c}{**BI Degradation**} & \multicolumn{6}{c}{**BD Degradation**} \\ \cline{2-11}  & **Params** & **Runtime** & **Fps** & **REDS4** & **Vimeo-90K** & **Vid4** & **UDM10** & **Vimeo-90K** & **Vid4** \\ \hline (1981) Bicubic [10] & - & - & - & 26.14/0.7292 & 31.32/0.8684 & 23.78/0.6347 & 28.47/0.8253 & 31.30/0.8687 & 21.80/0.5246 \\ (2017) VESPCN [31] & - & - & - & - & - & 25.35/0.7557 & - & - & - \\ (2017) SPMC [32] & - & - & - & - & - & 25.88/0.7752 & - & - & - \\ (2019) TOFUg [29] & - & - & - & 27.98/0.7990 & 33.08/0.9054 & 25.89/0.7651 & 36.26/0.9488 & 34.62/0.9212 & - \\ (2018) FEVSR [15] & 5.1 & 137 & 7.30 & - & - & - & 37.09/0.9522 & 35.64/0.9319 & 26.69/0.8103 \\ (2018) DUF [33] & 5.8 & 974 & 1.03 & 28.63/0.8251 & - & - & 38.48/0.9605 & 36.87/0.9447 & 27.38/0.8329 \\ (2019) RBPN [34] & 12.2 & 1507 & 0.66 & 30.09/0.8590 & 37.07/0.9435 & 27.12/0.8180 & 38.66/0.9596 & 37.20/0.9458 & \\ (2019) EDVR-M [12] & 3.3 & 118 & 8.47 & 30.53/0.8699 & 37.09/0.9446 & 27.01/0.8186 & 39.40/0.9646 & 37.33/0.9484 & 27.45/0.8406 \\ (2019) EVRV [12] & 20.6 & 378 & 2.65 & 31.09/0.8800 & 37.61/0.9489 & 27.35/0.8264 & 39.89/0.9686 & 37.81/0.9523 & 27.85/0.8503 \\ (2019) PFNL [35] & 3.0 & 295 & 3.39 & 29.63/0.8502 & 36.14/0.9363 & 26.73/0.8029 & 38.74/0.9627 & - & 27.16/0.8355 \\ (2020) MCAN [13] & - & - & - & 30.88/0.8750 & 37.32/0.9465 & - & - & 37.59/0.9516 & 27.63/0.8423 \\ (2020) TCGA [36] & 5.8 & - & - & - & - & - & 38.48/0.9606 & 36.49/0.9403 & 27.48/0.8388 \\ (2020) RSDN [38] & 6.2 & 94 & 10.64 & - & - & - & 39.35/0.9653 & 37.23/0.9471 & 27.92/0.8505 \\ (2020) RRN [39] & 3.4 & 45 & 22.22 & - & - & - & 38.96/0.9644 & - & 27.69/0.8488 \\ (2020) SDDet [40] & 2.6 & - & - & 30.51/0.8657 & 35.65/0.9331 & 26.52/0.7993 & - & - & - \\ (2022) RSTT [41] & 4.5 & 38 & 26.32 & 30.11/0.8613 & 36.58/0.9381 & 26.29/0.7941 & - & - & - \\ (2023) STDAN [42] & 8.3 & 72 & 13.89 & 29.98/0.8613 & 35.70/0.9387 & 26.28/0.8041 & - & - & - \\ (2024) FDDCC-VSR [25] & 1.8 & - & - & 30.55/0.8663 & 35.73/0.9357 & 26.79/0.8334 & - & - & - \\ LightVSR (ours) & 3.5 & 35 & 28.57 & 30.71/0.8780 & 36.69/0.9406 & 26.95/0.8188 & 39.25/0.9656 & 36.91/0.9444 & 27.37/0.8360 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison (PSNR/SSIM). All results are calculated on Y-channel except REDS4 (RGB-channel). The runtime is computed on an LR size of 180 \(\times\) 320. Notably, Params is expressed in millions (M), and the runtime is reported in milliseconds (ms).