Images compression by using cubic spline-functions methods
DOI:
https://doi.org/10.15587/2312-8372.2018.134978Keywords:
images coding, compression images, raster images, coefficients of the spline function, approximating polynomialAbstract
The object of research is image compression algorithms based on mathematical methods. The main problem with image compression is loss of quality during recovery. The approach is proposed in which the user can determine the quality of the reconstructed image itself. This is achieved due to the use of the spline interpolation method, which allows to set the compression ratio, thus controlling the quality of the decoded image.
The use of the spline function for image compression makes it possible to reduce the processing time of files due to the simplicity of the mathematical model of the algorithm. Given the accuracy of the restored image, the algorithm determines the size of the compressed file, depending on the color scale.
As a result of the analysis of the proposed development, the compression coefficients are shown, which show that the size of the compressed image can be smaller than the original image by 50–70 %. The decoding is performed using known spline function coefficients. The result is compared with the original file. The difference between the intensity of the points of the source and decoded images determines the quality of the restoration.
An algorithm is obtained that allows one to specify the accuracy of the reconstructed image. This result depends on the weighting coefficients of the spline function, which affect the accuracy of the construction of the approximating polynomial. A feature of the proposed approach is the ability of the user to specify the accuracy and quality of the image after decoding. This is achieved due to the fact that points close in intensity value are restored with a small error.
In this paper, let’s propose an approach involving the sequential extraction of blocks of points of equal intensity. For the selected blocks, an approximating polynomial is constructed based on the spline function, and the coefficients of the polynomial are transferred to a file containing information for image reconstruction. So it is possible to obtain large compression ratios by building a polynomial for blocks containing points that are close in intensity.
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Copyright (c) 2018 Kateryna Kotsyubivska, Olena Chaikovska, Maryna Tolmach, Svitlana Khrushch
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