Video Stream Completion with real-time processing

The following videoes use the video streaming completion model, which combines static and dynamic information for real-time processing. The proposed model is solved using the alternating direction method of multipliers (ADMM), and using MATLAB for solution recovery.

Gray video suzie: This video is restored in the case of the missing rates set to 70%, 80%, 90%, respectively

Color Video Hall: This video is restored in the case of the missing rates set to 70%, 80%, and 90%, respectively

Color Video Flower: This video is recovered separately in the case of the missing rates set to 90%

Color video tempete: This video is restored separately in the case of the missing rates set to 90%

We evaluate our proposed model using two commonly used public tensor datasets: the gray-scale video dataset and the color video dataset (http://trace.eas.asu.edu/yuv/). These datasets are frequently used to assess the tensor completion performance of different models. We conducted tests on the gray video dataset named "suzie" with the size of 144$\times$176$\times$150. The missing rates were set to 80$\%$, 90$\%$, and 95$\%$, and the error tolerance $tol$ was set to [$10^{-6}$, $10^{-4}$]. We also conducted experiments on three color video datasets: $"Hall"$, "$Flower$"  and "$tempete$". The dimensions of these datasets are 144$\times$ 176 $\times$ 3 $\times$ 300. The initial size of the tensor is $\mathcal{X}^{1}(\in \mathbb{R}^{144\times 176 \times 3 \times d})$, where the parameter $d$ can be chosen based on the RSE values.