The proposed work explains the single-image super-resolution (SISR) utilizing Bilinear Interpolation (BI) and Very Deep Super Resolution Neural Network (VDSRNN). In this work, the transformation of low to high-resolution image spaces using BI is compared with VDSRNN. The key idea of the work is to maintain computational efficiency and also attain a superior super-resolution process by employing the advantages of both BI and VDSRNN. In this work, the RGB image is transformed into YCbCr space before the super-resolution process. This YCbCr image is divided into three channels one intensity (Y) and two color components (Cb and Cr). The end result of the process was evaluated using the Blind and full-reference measures like PSNR, SSIM, and BRISQUE. The outcome of the recommended method clearly demonstrates that VDSRNN outperforms BI with improved perceptual integrity and image quality.