# LightVSR: A Lightweight Video Super-Resolution Model with Multi-Scale Feature Aggregation
## 4 Results  
### Experiment  
In the experiments, we used the REDS [28] and Vimeo-90K [29] datasets for training and the Vid4, UDM10, and Vimeo-90K-T datasets for testing. To evaluate the model's performance under different downsampling conditions, we conducted 4\(\times\) downsampling tests using both bicubic downsampling (BI) and blur downsampling (BD). Our proposed LightVSR model employs a pre-trained SPYNET as the optical flow prediction module and is trained on four NVIDIA GeForce GTX Titan Xp GPUs (Nvidia, Santa Clara, CA, USA) in Cuda 11.3 with Python 3.8.13 and Pytorch 1.12.1. During training, we set the batch size to 2, and each batch contains 15 consecutive frames. The number of training iterations is 300,000, with AdamW as the optimizer and an initial learning rate of \(2\times 10^{-4}\). To ensure stable training, we set the moving average of gradients to 0.9 and the moving average of gradient squares to 0.99. Additionally, to fine-tune the model, the minimum learning rate was set to \(1\times 10^{-7}\). We used the Charbonnier loss function as in [30] to improve reconstruction quality. To comprehensively evaluate the performance of the LightVSR model, we compared LightVSR with a range of state-of-the-art video super-resolution models, including bicubic interpolation [10], VESPCN [31], SPMC [32], TOFlow [29], FRVSR [15], DUF [33], RBPN [34], EDVR-M [12], EDVR [5], PFNL [35], MuCAN [13], TGA [36], RLSP [37], RSDN [38], RRN [39], D3 Dnet [40], RSTT [41], STDAN [42], and FDDCC-VSR [25], and adopted peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) to evaluate the quality of reconstruction video. PSNR is a standard metric for assessing the quality of reconstructed video frames. It is defined as the logarithmic ratio between the peak signal and noise levels, where a higher PSNR value indicates that the reconstructed video frame is much more similar to the reference video frame. The PSNR calculation formula is as follows:  
\[PSNR=10*log_{10}\left(\frac{MAX^{2}}{MSE}\right) \tag{17}\]  
where MAX denotes the maximum possible pixel value in the video frame; MSE represents the mean squared error between the reconstructed and ground truth frame. SSIM can be used to measure the similarity between a reconstructed frame and a reference frame in terms of luminance, contrast, and structural attributes, and its value ranges from 0 to 1. The larger SSIM value indicates the higher similarity. The calculation formula for SSIM is:  
\[\text{SSIM(x,y)}=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c _{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+c_{2}\right)} \tag{18}\]  
where \(\mu_{x}\) and \(\sigma_{x}\) represent the mean value and variance of pixels in frame \(x\), respectively, \(\sigma_{xy}\) is the pixel covariance between frame \(x\) and \(y\), and \(c_{1}\) and \(c_{2}\) are stabilization constants. As illustrated in Table 1, LightVSR demonstrates competitive performance compared with other VSR models across all test datasets. Specifically, under the BI degradation model, LightVSR achieved PSNR/SSIM values of 30.71/0.8780 on the REDS4 dataset, 36.69/0.9406 on Vimeo-90K-T, 26.95/0.8188 on Vid4, and 39.25/0.9656 on UDM10. Under the BD degradation model, LightVSR achieved PSNR/SSIM values of 36.91/0.9444 on Vimeo-90K-T and 27.37/0.8360 on Vid4. Although EDVR performs better than LightVSR in terms of PSNR/SSIM, it has a parameter size of 20.6 M and requires 378 ms for inference per frame. In contrast, LightVSR contains only 3.5 M parameters and achieves an inference time of just 35 ms per frame. The key advantage of our approach lies in its lightweight design, which significantly reduces computational requirements. In addition, LightVSR also strikes a good balance between model size and runtime efficiency. It can be seen that compared with RSTT and STDAN, our proposed model achieves better performance in terms of PSNR and SSIM across all test datasets under the BI degradation model while using fewer parameters and faster runtime.