Visual Communication Strategies of Textile Patterns Driven by Digital Media
Main Article Content
Abstract
With the rapid development of digital media and intelligent information transmission technologies, maintaining semantic consistency and stable multi-scale feature propagation has become increasingly important for visual communication systems and data-driven engineering applications, including future electromagnetic sensing and communication scenarios. However, textile pattern communication often suffers from fragmented information representation and disrupted transmission pathways, resulting in reduced visual coherence and communication effectiveness. To address these issues, this study proposes a visual communication optimization framework based on Swin Transformer (Swin-T) by integrating a Multi-Scale Attention (MSA) module and a path regularization strategy. The MSA module reorganizes hierarchical feature representations to achieve semantic alignment across different scales, while the path regularization constraint stabilizes similarity distributions during cross-layer propagation and suppresses feature dispersion. Experimental results demonstrate that the proposed framework achieves a maximum cosine similarity of 0.918 and a minimum Jensen–Shannon divergence of 0.183 during cross-scale transfer, while reaching an SSIM of 0.911 in geometric pattern scenarios and 0.871 in complex composite scenarios. The collaborative optimization strategy significantly improves visual consistency, information aggregation, attention concentration, and cross-scale transmission stability compared with existing approaches. The proposed framework provides an effective solution for intelligent textile pattern communication and offers valuable insights for multi-scale information representation and reliable feature propagation in future electromagnetic information processing and communication-oriented engineering systems.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
X. Yuan and N. Chuprina, “Ecological Design Concept of Textile Products: A Study on Brocade Weaving Materials of the Chinese Ethnic Minority Yao,” Art and Design, vol. (4), pp. 51-61, 2024, doi: 10.30857/2617-0272.2024.4.4.
M. A. Habib and M. S. Alam, “A comparative study of 3D virtual pattern and traditional pattern making,” Journal of Textile Science and Technology, vol. 10, no. 1, pp. 1-24, 2023, doi: 10.4236/jtst.2024.101001.
Z. A. Awad, M. A. Eida, H. S. Soliman, M. A. Alkaramani, I. G. EIbadwy, and A. G. Hassabo, “The psychological effect of choosing colors in advertisements on stimulating human interaction,” Journal of Textiles. Coloration and Polymer Science, vol. 22, no. 1, pp. 289-298, 2025, doi: 10.21608/jtcps.2024.259790.1323.
J. Liu, “Research on multimedia art expression in visual communication design,” Advances in Social Sciences, vol. 13, no. 4, pp. 250-256, 2024, doi: 10.12677/ass.2024.134296.
S. Y. Jang and J. Ha, “The influence of tactile information on the human evaluation of tactile properties,” Fashion and Textiles, vol. 8, no. 1, pp. 39, 2021, doi: 10.1186/s40691-020-00242-5.
A. Chen, J. Tan, and P. Henry, “E-textile design through the lens of affordance,” Journal of Textile Design Research and Practice, vol. 9, no. 2, pp. 164-183, 2021, doi: 10.1080/20511787.2021.1935110.
M. T. Fischer, F. L. Dennig, D. Seebacher, D. A. Keim, and M. E. Assady, “Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers,” 2022 IEEE Visualization in Data Science (VDS). Oklahoma City, OK, USA, pp. 6-16, 2022, doi: 10.1109/VDS57266.2022.00006.
B. A. Mandour, “Creativity in textile printing design: An integrative framework in design education,” International Journal of Technology and Design Education, vol. 35, no. 3, pp. 1031-1062, 2025, doi: 10.1007/s10798-024-09936-z.
N. Lee and S. Suh, “How does digital technology inspire global fashion design trends? Big data analysis on design elements,” Applied Sciences, vol. 14, no. 13, pp. 5693, 2024, doi: 10.3390/app14135693.
S. Moon and Y. Chae, “Quantitative analysis of the color accuracy and reproducibility in digital textile printing: Discrepancies within color reproduction media,” Textile Research Journal, vol. 95, no. 9-10, pp. 1053-1069, 2025, doi: 10.1177/00405175241288229.
L. Haghzare, X. Ping, M. Arnison, D. Monaghan, D. Karlow, V. Honson, et al., “Digital fabrics for online shopping and fashion design,” Frontiers in Virtual Reality, vol. 4, Art. no. 1236095, 2023, doi: 10.3389/frvir.2023.1236095.
S. Y. Jang and J. Ha, “Presenting fabrics in digital environment: fashion designers’ perspectives on communicating tactile qualities of the fabrics,” Fashion and Textiles, vol. 10, no. 1, pp. 6, 2023, doi: 10.1186/s40691-022-00328-2.
M. Tkalec, M. Glogar, Ž. Penava, F. T. Petra, K. Danjela, and I. Stojanoska, “The Complexity of Colour/Textile Interaction in Digital Printing as an Integral Part of Environmental Design,” Arts (2076-0752), vol. 13, no. 1, pp. 29, 2024, doi: 10.3390/arts13010029.
M. X. Vargas, Z. Zhexin, and O. Yoichi, “An exploration in digital techniques to read Ainu textile patterns,” Journal of Cultural Heritage Management and Sustainable Development, vol. 15, no. 1, pp. 1-24, 2025, doi: 10.1108/JCHMSD-05-2022-0083.
G. Xu, Y. U. Chen, and J. Hua, “A fast multi-scale textile pattern generation method combining layered loss and convolutional attention,” Industria Textila, vol. 76, no. 3, pp. 1-10, 2025, doi: 10.35530/IT.076.03.2024126.
S. Liu, Y. K. Liu, C. Lo, and C. Kan, “Intelligent techniques and optimization algorithms in textile colour management: a systematic review of applications and prediction accuracy,” Fashion and Textiles, vol. 11, no. 1, pp. 13, 2024, doi: 10.1186/s40691-024-00375-x.
L. Xu, F. Li, and S. Chang, “A fiber recognition framework based on multi-head attention mechanism,” Textile Research Journal, vol. 94, no. 23-24, pp. 2629-2640, 2024, doi: 10.1177/00405175241253307.
N. Skliarenko, I. Gryshchenko, and M. Kolosnichenko, “Symmetry in the visual communication design: Methods of dynamic image construction,” Art and Design, vol. 3, no. 4, pp. 9-20, 2021, doi: 10.30857/2617-0272.2021.3.1.
Y. Chen and D. Meng, “Optimization of Visual Communication Design Scheme by Combining Computer-Aided Design and Deep Learning,” Computer-Aided Design & Applications, vol. 22, pp. 253-267, 2025, doi: 10.14733/cadaps.2025.S1.253-267.
C. Zhang, “Creation of a system for designing textile patterns using an iterative function system,” International Journal of Innovative Research and Scientific Studies, vol. 7, no. 1, pp. 115-26, 2024, doi: 10.53894/ijirss.v7i1.2530.
S. He and J. Lu, “Generation Method of Visual Communication Design Elements Based on Recurrent Neural Network,” Computer-Aided Design & Applications, vol. 21, pp. 149-163, 2025, doi: 10.14733/cadaps.2025.S1.149-163.
B. Liu, B. Guo, and L. Jiang, “Image Feature Extraction and Interactive Design of Cultural and Creative Products Based on Deep Learning,” Computer-Aided Design & Applications, vol. 21, pp. 148-163, 2024, doi: 10.14733/cadaps.2024.S7.148-163.
J. Bian and Y. Ji, “Research on the teaching of visual communication design based on digital technology,” Wireless Communications and X. Zhang Mobile Computing, vol. 2021, no. 1, Art. no. 8304861, 2021, doi: 10.1155/2021/8304861.
W. Zhu, “A Study of Big-Data-Driven Data Visualization and Visual Communication Design Patterns,” Scientific programming, vol. 2021, no. 1, Art. no. 6704937, 2021, doi: 10.1155/2021/6704937.