Due to the limitation of the communication system resources, the compression is required to reduce the complexity, the required storage, and the processing time. In traditional compression techniques the signal is sampled according to Shannon-Nyquist theory. Consequently, the complexity of signal processing and the encoder/ decoder increases. In this paper, we study the utilization of Compressive Sensing (CS) to solve this challenge. Compressive sensing considers as an under sampling signal processing technique which can sample the signal with sampling rate under the ShannonNyquist rate and grantee the proper recovery of the signal. The performance of CS is evaluated under different compression ratio. Additionally, we utilize reconstruction compressive sensing techniques including -minimization ( ), 2-minimization ( ), Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP), and Generalized Orthogonal Matching Pursuit (GOMP). The performance is evaluated in terms of peak signal to noise ratio (PSNR), Correlation (CORR), Mean Square Error (MSE), Energy ratio (ER), Structural Similarity Index (SSIM), Dissimilarity Index (DSSIM) and Recovery Tim (RT). That is to stand on the best recovery technique that suits image transmission over communication systems.