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<p>Artificial intelligence (AI) technology significantly enhances the creative capabilities and expressions of artists, playing a pivotal role in the innovation of digital media art. This paper delves into the technical aspects of digital media art expression, introducing a human-computer interaction (HCI) media art model. This model is designed to cater to the visual sensitivities of the human eye by optimizing text readability and interface color during interaction. We further enhance the interactive interface by converting the color space, applying de-blurring techniques, and mitigating the noise associated with image binarization to refine the HCI interface. The efficacy of these interventions is evaluated through practical implementations in digital media art, focusing on the application effectiveness of HCI and user satisfaction. Our experimental outcomes indicate that the noise level of the employed techniques is maintained within a narrow range of −1.5 to 1.5 dB, demonstrating minimal and consistent fluctuations that substantially improve image processing and influence the final visual output. Furthermore, in assessing user interaction satisfaction, the average user satisfaction scores for the four interactive modalities ranged between 3.316 and 3.684, indicating that most users are satisfied with the digital media art designs integrated with AI.</p>
</abstract>

Artificial intelligence (AI) technology significantly enhances the creative capabilities and expressions of artists, playing a pivotal role in the innovation of digital media art. This paper delves into the technical aspects of digital media art expression, introducing a human-computer interaction (HCI) media art model. This model is designed to cater to the visual sensitivities of the human eye by optimizing text readability and interface color during interaction. We further enhance the interactive interface by converting the color space, applying de-blurring techniques, and mitigating the noise associated with image binarization to refine the HCI interface. The efficacy of these interventions is evaluated through practical implementations in digital media art, focusing on the application effectiveness of HCI and user satisfaction. Our experimental outcomes indicate that the noise level of the employed techniques is maintained within a narrow range of −1.5 to 1.5 dB, demonstrating minimal and consistent fluctuations that substantially improve image processing and influence the final visual output. Furthermore, in assessing user interaction satisfaction, the average user satisfaction scores for the four interactive modalities ranged between 3.316 and 3.684, indicating that most users are satisfied with the digital media art designs integrated with AI.

Research on the Theory and Innovation Strategy of Digital Media Art Development Based on Artificial Intelligence Technology

School of Humanities and Foreign Languages, Qingdao University of Technology

Applied Mathematics and Nonlinear Sciences

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

Artificial intelligence (AI) technology significantly enhances the creative capabilities and expressions of artists, playing a pivotal role in the innovation...

Research on the Theory and Innovation Strategy of Digital Media Art Development Based on Artificial Intelligence Technology

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 19. März 2024

Akzeptiert: 07. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1543

Schlüsselwörter
Image color space, De-blurring, Image binarization, Visual enhancement processing, Digital media art

© 2024 Juncheng Wang et al., published by Sciendo

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

Research on the Theory and Innovation Strategy of Digital Media Art Development Based on Artificial Intelligence Technology

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 19. März 2024

Akzeptiert: 07. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1543

SchlüsselwörterImage color space, De-blurring, Image binarization, Visual enhancement processing, Digital media art

© 2024 Juncheng Wang et al., published by Sciendo

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

Schlüsselwörter
Image color space, De-blurring, Image binarization, Visual enhancement processing, Digital media art