In parallel imaging, the array coil with a large number of channels accelerates the data acquisition speed and provides high quality reconstructed images at the cost of an increase in computational complexity, image reconstruction time and memory requirement. Recently, Principal Component Analysis (PCA) has been used to compress multiple physical channels to a few virtual channels. However, the results showed a degradation in image quality and loss of sensitivity information. We introduce a novel channel compression technique combining PCA and U-Net before Compressed Sensing MRI reconstruction. The experimental results show less channel compression losses and retention of coil sensitivity information.