# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 1 Introduction  
Wireless sensor network (WSN) technology has been identified as one of the key components in designing future internet of things platforms [1]. It has been gaining a lot of attention since smart sensors have become an important part in our daily lives. However, in real life, these devices are resource-constrained: the storage resources, the energy capacity and the computing performances are all limited. That is why the processing of huge data especially video data is becoming very challenging. In order to shift the computation burdens from the sensor level to the decoder in WSN, compressive sensing is used as an effective way to reduce the complexity of the encoder, which means that by optimizing the way the acquire and transmit data over wireless channels, we optimize the computational resources of the devices and enhance their performances. In fact, the compressive sensing technique significantly enhances the coding efficiency of the wireless devices (considered as encoders) by reducing the sampling rate (in comparison with the well-known Shannon-Nyquist) and synchronizing the data sampling process. Another problem can be detected from a macro perspective in WSN platforms: the sporadic (infrequent) transmission rate. Indeed, not all wireless sensors send their data simultaneously to the central server, which means that the WSN architecture sparsity should be exploited to reach high data reliability with a limited number of sensors. In addition, IoT platforms can easily integrate compressive sensing into their several applications because many real-world datasets can be well approximated by sparse signals using an appropriate transform (e.g., DCT, DWT...to represent images, videos...). So, in many applications related to WSN, energy consumption is a principal concern because sensors have to send regularly their sensing data to the coordinator node. Data transmission being considered as a principal factor of energy consumption, many research efforts are focusing on reducing the amount of data acquired at the sensor level. In order to reduce the amount of transmission data, we have to compress them inside the network. As a result, compressive sensing (CS) algorithms have led to new ways of designing energy efficient WSN with low cost data acquisition [2]. Compressive sensing is a technique exploited today in several applications such as medical imaging, remote sensing and wireless sensor networks. In fact, CS is a theory which can efficiently acquire and reconstruct sparse signals [3]. CS theory suggests that the sampling rate necessary to acquire and reconstruct the signal can be significantly lower than the minimal rate required by the Nyquist-Shannon sampling theorem. This lower sampling rate can reduce the processing and energy requirement at the sensor nodes which can lead to revolutionary results for embedded video sensors. In fact, the video signal in general is sparse so it contains a significant amount of redundancy in both spatial and temporal domains and therefore video compression is one of the most important fields where CS can be applied. The advent of CS has led to the emergence of new image devices such as Single Pixel Cameras [4]. CS techniques are commonly used to deal with high transmission throughput and large storage spaces. Indeed, an impressive progress has been made in Video Compressive Sensing (VCS) with the appearance of single pixel cameras where the video is represented in the Fourier domain [5] or the Wavelet domain [6]. Then, video CS cameras tried to integrate temporal compression into the systems with the arrival of the optical flow based algorithms for video reconstruction [7]. In addition, Total Variation (TV) [8] and Dictionary Learning [9] were among the popular approaches used for VCS. TV methods suppose the sparsity of the gradient of each video frame and try to minimize the \(l1\) norm of the gradient frames. However, dictionary-based approaches consider the video patches as a sparse linear extension in the dictionary elements.