Article: Recursive quantitative analysis modelling of computer art design interaction Journal: International Journal of Computational Systems Engineering (IJCSYSE) 2024 Vol.8 No.5 pp.1 - 11 Abstract: With the increasing demand for high-quality images in computer art interaction design models, image compression is crucial for effective transmission of information. This study combines traditional recursive characteristics with convolutional neural network models to propose a recursive convolutional neural network-based image compression algorithm. The algorithm's performance was tested on Kodak1 and Kodak2 datasets, showing a decreasing trend in mean square error as the number of iterations increases. At 800 iterations, the algorithm outperformed other algorithms in terms of mean square error. Additionally, it exhibited higher peak signal-to-noise ratio and multi-scale structural similarity compared to traditional neural network algorithms. These results indicate that the proposed algorithm effectively compresses images and efficiently handles the large volume of images generated in artistic interaction design. Inderscience Publishers - linking academia, business and industry through research

Title: Recursive quantitative analysis modelling of computer art design interaction

Authors: Duan Song

Addresses: Department of Fine Arts, Hebei Vocational Art College, Shijiazhuang, 050000, China

Abstract: With the increasing demand for high-quality images in computer art interaction design models, image compression is crucial for effective transmission of information. This study combines traditional recursive characteristics with convolutional neural network models to propose a recursive convolutional neural network-based image compression algorithm. The algorithm's performance was tested on Kodak1 and Kodak2 datasets, showing a decreasing trend in mean square error as the number of iterations increases. At 800 iterations, the algorithm outperformed other algorithms in terms of mean square error. Additionally, it exhibited higher peak signal-to-noise ratio and multi-scale structural similarity compared to traditional neural network algorithms. These results indicate that the proposed algorithm effectively compresses images and efficiently handles the large volume of images generated in artistic interaction design.

Keywords: quantisation recursion; convolution neural network; image compression; deep learning; art design.

DOI: 10.1504/IJCSYSE.2024.139715

International Journal of Computational Systems Engineering, 2024 Vol.8 No.5, pp.1 - 11

Received: 15 Jun 2023
Accepted: 08 Sep 2023
Published online: 05 Jul 2024 *

Title: Recursive quantitative analysis modelling of computer art design interaction

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