Although Convolutional Neural Networks provided effective solutions for computer vision tasks across various domains in recent years, the high training times of the models remain a crucial aspect. A straightforward way to decrease the computational costs is to use smaller images during training. However, existing image resizing approaches are either computationally intensive or lossy methods. This paper introduces the usage of the Cantor pairing function for image resizing through pixel reduction. The pairing function is a fast bijective transformation that maps a pair of two integers into a single value. Combining two pixels into one leads to a decrease in the image size, while the bijective property ensures a lossless transformation. The experiments conducted on the MNIST datasets demonstrated that the proposed approach reduces resizing time at least six times, decreases training times significantly, and achieves similar performance with baseline methods.