## 2 Literature Review  
Compressive sensing (CS) has emerged as one of the most substantial mechanisms for compressing visual data (video) in recent times. The principle is to sample each video frame independently using CS (encoder), and then at a decoder the correlations within the video frames are exploited using joint reconstruction schemes for image reconstruction. In literature various video reconstruction coding schemes have been proposed (dictionary-based coding, 3D-transformation coding, residual-based coding, etc), each having their benefits and challenges. In the following, only the state-of-the-art residual coding schemes are discussed as the proposed model relies on residual-based coding and will be compared with these schemes. However, complete explanations of the other coding schemes can be found in [5; 6; 7; 8; 9; 10; 11]. In [12], the modified-CS-residual scheme is presented. The scheme is based on a residual reconstruction approach in which side information helps manage the reconstruction issue related to sparse signals (minutest quantity of linear projections). The least mean squares or Kalman filter estimation methods are used to produce side information. However, this side information can be exposed to errors. The proposed scheme aims to counter the convex relaxation problem correlated to data restraints and sparsity outside the side information. A reconstruction scheme known as k-t FOCUSS is presented in [13; 14]. It is also built on a similar residual reconstruction idea. The scheme first generates side information assuming n keyframes using disparity (compensation/estimation) predictions and a residual encoding approach. This leads to an ideal sample allocation among the estimated and residual steps. The residuals among the bidirectional (DC/DE) estimation of each keyframes and non-key frame is used to attain the reconstruction. A DC/DE-based reconstruction scheme with residuals is introduced in [15]. The scheme generates the side information by integrating DC/DE-based prediction and is referred to as DC-BCS-SPL. The final reconstruction is aided by calculating the residuals among the side information and the original view. Also, in [16], a DC/DE-based joint reconstruction scheme is proposed. The proximal gradient method is adopted by the proposed scheme to resolve the optimization problem. The motion (compensation/estimation)-based scheme is introduced in [17]. The proposed MC/ME scheme is incorporated into the block compressive sensing (BCS-SPL) video restoration method and called MC-BCS-SPL. In this approach, each frame of the video sequence is initially sampled using random block-based CS measurements (at the encoder) and transmitted to the decoder. The received encoded measurements are then decoded and the proposed residual dependent ME/MC approach is applied to generate the final view. The proposed scheme generates video frames alternately along with the motion fields associated with the frames i.e., using one to improve the other iteratively. In [18], a joint reconstruction algorithm using MC/ME and fusion is presented. The proposed scheme uses the down-sample approach (lowering sampling rate of the views) and then MC/ME and a fusion approach are applied on the down-sampled views to produce a view prediction, which helps generate the final view. A view estimation and residual reconstruction-based joint reconstruction method is introduced in [19]. The method integrates a MC/ME approach into the reconstruction to generate the side information. The side information then helps in the reconstruction of the final output. The residual coding schemes discussed above help improve the reconstruction quality of the video frames, however, they are exposed to a few issues such as imprecise estimation or computational intricacy. For example, Ref. [12] claims that the proposed scheme is more suitable for video applications and uses least mean squares (LMS) or Kalman filter prediction methods. However, the scheme is exposed to prediction errors.