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<p>With the continuous development and improvement of digital media technology, its position in dynamic visual design is also increasing. In this paper, we first combine LCEOPaCH algorithm to extract color features from visual images, use local energy pointing mode to get image color transformation information, optimize the dynamic visual measurement method based on dynamic binocular reconstruction, and construct a dynamic visual communication model combined with visual SLAM. Through the empirical data of the visual communication system, the localization accuracy of different methods in dynamic environments is compared based on the root mean square error value of absolute trajectory error in dynamic datasets A, B, and C. The quantization results of this paper’s algorithm under dynamic visual measurement analysis and color feature extraction data comparison in 6 sets of simulated images, the PSNR value is higher than 30, and the average value of PSNR is as high as 38.11 under 24 feature colors. The SSIM value for 8 feature colors is higher than 0.75 in the same quantization. The study demonstrates the advantages of digital media technology in visual communication design and provides practical design ideas.</p>
</abstract>

With the continuous development and improvement of digital media technology, its position in dynamic visual design is also increasing. In this paper, we first combine LCEOPaCH algorithm to extract color features from visual images, use local energy pointing mode to get image color transformation information, optimize the dynamic visual measurement method based on dynamic binocular reconstruction, and construct a dynamic visual communication model combined with visual SLAM. Through the empirical data of the visual communication system, the localization accuracy of different methods in dynamic environments is compared based on the root mean square error value of absolute trajectory error in dynamic datasets A, B, and C. The quantization results of this paper’s algorithm under dynamic visual measurement analysis and color feature extraction data comparison in 6 sets of simulated images, the PSNR value is higher than 30, and the average value of PSNR is as high as 38.11 under 24 feature colors. The SSIM value for 8 feature colors is higher than 0.75 in the same quantization. The study demonstrates the advantages of digital media technology in visual communication design and provides practical design ideas.

Research on the application of digital media technology in dynamic visual communication design

Hebei Vocational University of Technology and Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the continuous development and improvement of digital media technology, its position in dynamic visual design is also increasing. In this paper, we first...

Research on the application of digital media technology in dynamic visual communication design

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 18 gru 2023

Przyjęty: 28 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0174

Słowa kluczowe
LCEOPaCH algorithm, Color feature extraction, Visual SLAM, Digital media technology, Dynamic vision

© 2024 Wendi Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the application of digital media technology in dynamic visual communication design

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 18 gru 2023

Przyjęty: 28 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0174

Słowa kluczoweLCEOPaCH algorithm, Color feature extraction, Visual SLAM, Digital media technology, Dynamic vision

© 2024 Wendi Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
LCEOPaCH algorithm, Color feature extraction, Visual SLAM, Digital media technology, Dynamic vision