The article describes the theoretical foundations and software tools for scaling digital images by adaptive and combined application of bilinear and bicubic interpolation algorithms. An analysis of modern algorithms and image scaling tools has been performed. The theoretical foundations of image scaling using interpolation algorithms are described. The root mean square error between the pixel values of the original and scaled images was used as the scaling error. The scaling of images was performed by a complex of two interpolation algorithms. The first algorithm reduces the image scale, after which the second algorithm increases the scale. Such image processing is performed, in particular, in telecommunication systems for transmitting images at reduced scales. A correlation was found between the values of the average spatial period of the image and the relative scaling error, which is equal to the ratio of the scaling errors for different interpolation algorithms. The spatial period of the image was calculated based on its energy spectrum. A regression analysis was performed to determine the dependence of the relative scaling error on the spatial period of the images. It is found that in most cases bicubic interpolation provides a smaller scaling error, but for some images with small spatial period, bilinear interpolation provides a smaller error. It is proposed to increase the scaling accuracy by adaptively selecting the image interpolation algorithm depending on its spatial period. A combined application of interpolation algorithms was performed, which consists of reducing the scale using the bilinear interpolation algorithm and increasing the scale using the bicubic interpolation algorithm. A statistical analysis of the results of image scaling was performed. It was found that the combined application of algorithms in most cases provides a smaller error than the separate application of the bicubic and bilinear interpolation algorithms.