# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 6 Comparative Study
### Deep Learning-Based VCS Algorithms
#### 6.2.1 Quantitative Comparison
As a result, high-speed motions datasets are recommended while training these deep learning-based algorithms to enhance their quality performances. In addition, we note that the recent ensemble learning-based algorithm (ELP-Unfolding) is proposed to enhance the performance of the previous algorithms by strategically generate and combine multiple models which confirm the fact to consider this technique as a promising research topic in video reconstruction. In addition, we notice from Figures 15 and 16, that DeepUnfold-VCS outperforms the rest of the proposed algorithms in terms of quality performances (PSNR and SSIM) on almost all experiments. In fact, the authors propose an algorithm that combines iterative strategy and deep learning. In addition, they used a deep unfolding approach and exploit its interpretability to reconstruct the video scene. In the other hand, GAP-net-Unet-S12 is the fastest VCS reconstruction approach with good quality performances since it proposes also to combine ADMM-net and neural networks. However, in contrast to DeepUnfold-VCS, it proposes a CNN-based network which much faster than recurrent neural nets. It can be used in real-time applications that require prompt capture and reconstruction time. GAP-net-Unet-S12 can acquire and reconstruct up to 250 measurement per second. To conclude, recent deep learning-based approaches proposed for VCS purposes present good quality performances and research in this field becomes very competitive and very challenging to come up with the fastest algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Algorithms** & **Year** & **Aerial** & **Drop** & **Kobe** & **Runner** & **Traffic** & **Vehicle** & **Average** & **Time** \\ \hline
**GAP-TV [44]** & 2016 & 25.03 & 33.81 & 26.45 & 28.48 & 20.90 & 24.82 & 26.58 & 4.2 \\  & 0.828 & 0.963 & 0.845 & 0.899 & 0.715 & 0.838 & 0.848 & \\ \hline
**DeSCI [73]** & 2019 & 25.33 & 43.22 & 33.25 & 38.76 & 28.72 & 27.04 & 32.72 & 6180 \\  & 0.860 & 0.993 & 0.952 & 0.969 & 0.925 & 0.909 & 0.935 & \\ \hline
**PnP-FFDNet [74]** & 2020 & 24.02 & 40.87 & 30.47 & 32.88 & 24.08 & 24.32 & 29.44 & 3.0 \\  & 0.814 & 0.988 & 0.926 & 0.938 & 0.833 & 0.836 & 0.889 & \\ \hline
**PnP-FastDVDNet [88]** & 2021 & 27.98 & 41.82 & 32.73 & 36.29 & 27.95 & 27.32 & 32.35 & 18 \\  & 0.897 & 0.989 & 0.946 & 0.962 & 0.932 & 0.925 & 0.942 & \\ \hline
**GAP-FastDVDNet(online) [85]** & 2022 & 28.24 & 41.95 & 32.95 & 36.41 & 28.16 & 27.64 & 32.56 & 35 \\  & 0.897 & 0.989 & 0.951 & 0.962 & 0.934 & 0.928 & 0.944 & \\ \hline
**DE-RNN [86]** & 2022 & 24.83 & 30.16 & 21.46 & 27.85 & 19.47 & 23.65 & 24.53 & 4.68 \\  & 0.855 & 0.909 & 0.697 & 0.818 & 0.715 & 0.832 & 0.804 & \\ \hline
**DE-GAP-FFDnet [86]** & 2022 & 26.02 & 39.89 & 29.32 & 33.06 & 24.71 & 25.85 & 29.81 & 1.90 \\  & 0.892 & 0.992 & 0.952 & 0.971 & 0.907 & 0.905 & 0.936 & \\ \hline
**E2E-CNN [48]** & 2020 & 27.18 & 36.56 & 27.79 & 34.12 & 24.62 & 26.43 & 29.45 & 0.0312 \\  & 0.869 & 0.949 & 0.807 & 0.947 & 0.840 & 0.882 & 0.882 & \\ \hline
**BIRNAT [75]** & 2020 & 28.99 & 42.28 & 32.71 & 38.70 & 29.33 & 27.84 & 33.31 & 0.16 \\  & 0.927 & 0.992 & 0.950 & 0.976 & 0.942 & 0.927 & 0.951 & \\ \hline
**MetaSCI [83]** & 2021 & 28.31 & 40.61 & 30.12 & 37.02 & 26.95 & 27.33 & 31.72 & 0.025 \\  & 0.904 & 0.985 & 0.907 & 0.967 & 0.888 & 0.906 & 0.926 & \\ \hline
**RevSCI [82]** & 2021 & 29.35 & 42.93 & 33.72 & 39.40 & 30.02 & 28.12 & 33.92 & 0.19 \\  & 0.924 & 0.992 & 0.957 & 0.977 & 0.949 & 0.937 & 0.956 & \\ \hline
**DeepUnfold-VCS [51]** & 2022 & 30.86 & 44.43 & 35.24 & 41.47 & 31.45 & 30.32 & 35.63 & 1.43 \\  & 0.965 & 0.997 & 0.984 & 0.994 & 0.977 & 0.976 & 0.982 & \\ \hline
**GAP-Unet-S12 [76]** & 2020 & 28.88 & 42.02 & 32.09 & 38.12 & 28.19 & 27.83 & 32.86 & 0.0072 \\  & 0.914 & 0.992 & 0.944 & 0.975 & 0.929 & 0.931 & 0.947 & \\ \hline
**ELP-Unfolding [84]** & 2022 & 30.68 & 44.99 & 34.41 & 41.16 & 31.58 & 29.65 & 35.41 & 0.24 \\  & 0.943 & 0.995 & 0.966 & 0.986 & 0.962 & 0.960 & 0.969 & \\ \hline \hline \end{tabular}
\end{table}