In the case of image coding are containing interpolation methods, a linear methods of component forming usually used. However, taking in account the huge speed increasing of a computer and hardware integration power, of special interest was more complicated interpolation methods, in particular spline interpolation. A spline interpolation is known to be a approximation that performed by spline, which consist of polynomial bounds, where a cub parabola usually used. At this article is to perform image analysis by 5 X 5 aperture, result in count rejection of low-frequence component of image: an one base count per 5 X 5 size fragment. The passed source counts were restoring by spline interpolation methods, then formed counts of high-frequence image component, by subtract from counts of initial image a low-frequence component and their quantization. At the final stage Huffman coding performed to divert of statistical redundancy. Spacious set of experiments with various images showed that source compression factor may be founded into limits of 10 - 70, which for majority test images are superlative source compression factor by JPEG standard applications at the same image quality. Investigated research show that spline approximation allow to improve restored image quality and compression factor to compare with linear interpolation. Encoding program modules has work out for BMP-format files, on the Windows and MS-DOS platforms.