# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 6 Comparative Study
### Deep Learning-Based VCS Algorithms  
#### 6.2.1 Quantitative Comparison  
Training Details  
It is important to mention that video compressive sensing algorithms (acquisition and reconstruction) does not have a particular training dataset and can be applied on any scene. Indeed, all experiments are trained on Densely Annotated VIdeo Segmentation (DAVIS2017) [87] dataset. DAVIS2017 is an object segmentation dataset that contains 90 different videos with a resolution of \(480\times 894\). To efficiently train the state-of-the-art algorithms, 6516 videos of size \(8\times 256\times 256\) are generated from DAVIS2017 to learn different parameters on the same compression ratio \(\frac{1}{8}\). Then, all algorithms are tested on 6 simulation datasets: Aerial, Drop, Kobe, Runner, Traffic, Vehicle to evaluate their performances. All experiments are tested on the RTX 2080 GPU and Intel(r) Core(tm) i7-9700K CPU (3.6 GHz, 32 GB memory). Comparison Metrics  
The following three metrics are employed to compare different approaches:  
* Peak Signal to Noise Ratio (PSNR) [46]: Quality metric
* Structural Similarity Index (SSIM) [46]: Quality metric
* Reconstruction Time: this metric is used to prove whether the algorithm can be applied in real-time applications at the testing step  
Benchmark Results  
We present a quantitative comparison to compare the quality performances of the following VCS algorithms: GAP-TV [44], DeSCI [73], PnP-FFDNet [74], PnP-FastDVDNet [88], GAP-FastDVDNet(online) [85], DE-RNN [86], DE-GAP-FFDnet [86], E2E-CNN [48], BIRNAT [75], MetaSCI [83], RevSCI [82], DeepUnfold-VCS [51], GAP-Unet-S12 [76], ELP-Unfolding [84]. Table 2 summarizes the comparison of several VCS algorithms on PSNR, SSIM and the reconstruction time. From this table, different performance results are plotted in Figures 13-16 for visualization purposes. From Figures 13 and 14, we notice that iterative algorithms (GAP-TV, DeSCI, PnP-FFDnet and PnP-FastDVDnet) provide inferior quality performance results (both in terms of PSNR and SSIM) with low recovery speed (from one second to even hours) which threaten their hardware implementation for real-time applications. However, the other deep learning-based algorithms outperforms these iterative approaches in terms of quality performances with faster reconstruction time (<1 s). These performances can prove the potential usability of deep learning-based approaches in real-time applications. From Figures 15 and 16, we notice that DeSCI, the iterative algorithm, provide little improvement over some deep learning-based algorithms on the Kobe, Runner and Drop (e.g., PSNR: +2.22%, +1.65% and +0.15% over BIRNAT, +6.42%, +10.39% and +4.7% over MetaSCI on Drop, Kobe and Runner, respectively). Indeed, these datasets \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & \begin{tabular}{c} **Min. Number of** \\ **Measurements** \\ \end{tabular} & **Complexity** & \
\begin{tabular}{c} **No Requirement of** \\ **Sparsity Information** \\ \end{tabular} & **Noise Resistance** &
\begin{tabular}{c} **Hardware** \\ **Implementation** \\ \end{tabular} \\ \hline Basis Pursuit & \(klog(N)\) & \(O(N^{3})\) & \(\checkmark\) & & \\ \hline OMP & \(klog(N)\) & \(O(kMN)\) & \(\checkmark\) & & \(\checkmark\) \\ \hline StOMP & \(Nlog(N)\) & \(O(Nlog(N))\) & \(\checkmark\) & \(\checkmark\) \\ \hline ROMP & \(klog(N)^{2}\) & \(O(kMN)\) & \(\checkmark\) & & \(\checkmark\) \\ \hline CoSaMP & \(klog(N)\) & \(O(MN)\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline Subspace Pursuits & \(klog(\frac{N}{k})\) & \(O(MNlog(k))\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Complexity, minimum measurement requirement and crucial properties of CS recovery algorithms. are characterized by high-speed motions of some objects. However, we infrequently find these features in DAVIS2017 dataset, which explain these results.