Open AccessAnalysis of the relationship between the choice of the color model for the representation of images and the efficiency of solving the problem of improving the image resolution
Author(s) -
D. N. Veremeev,
Pavel Obukhov,
К. В. Кислов,
D. I. Gladkih
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1029/1/012099
Subject(s) - rgb color model , ycbcr , artificial intelligence , artificial neural network , computer science , raster graphics , color image , hsl and hsv , representation (politics) , image (mathematics) , computer vision , ntsc , process (computing) , pattern recognition (psychology) , image processing , telecommunications , virus , high definition television , virology , politics , political science , law , biology , operating system
The article discusses the influence of the choice of a color model for representing a raster image on the efficiency of increasing its resolution using an artificial neural network. The article begins with the essence of the research. After that, the process of obtaining for training networks with an identical topology of a dataset using different color models of image representation, namely RGB, HSV, L * a * b *, NTSC and YCbCr, is described. After that, an assessment of the effectiveness of trained neural networks on the previously described datasets is given. To assess the performance of an artificial neural network, two algorithms are used: SSIM and PSNR. As a result of the assessment, the networks using RGB and YCbCr color models showed the highest results. At the end of the article, there are reflections on the reasons for this result.