In the science of digital processing of images, one of the research fields consists in utilizing the main mathematical operations of any digital signal processing system for which the entry is a figure, a series of images, videos, etc; the output of processing system may be an image or a set of characteristics or parameters related to image. According this definition, the scaling of an image refers to the processing of its dimensions meanwhile without changing the quality and resolution of this image.

In the mathematical analysis, the bi-cubic interpolation technique is an extension of the cubic interpolation of the data points in a two-dimensional environment. According to this technique, the mathematical surface is smoother than corresponding surfaces obtained through the other  image escalation techniques such as bilinear interpolation or nearest-neighbor interpolation.

On the other hand, the bilinear interpolation method initially performs the functions of two variables (x and y p.sh) in a two-dimensional environment. The key idea is to perform the linear interpolation in a direction and then in another direction. Although each step is linear in the sampled values, the interpolation itself is not linear but quadratic.

In this paper we have demonstrated a comparative performance analysis between bilinear interpolation method with the default method used for simulations through Matlab. The default method used in Matlab to scale the image with changing the quality of the image is the bi-cubic interpolation method which, as it has been proven from the simulations, is pretty much time-consuming method especially for the high-quality images.

We have built a code in Matlab, which reads a certain image from the computer and through the two algorithms, bi-cubic one and the bilinear, shows the time in seconds needed to perform the scaling of the

images. Finally, we can observe the difference in time execution between the each of the two algorithms.