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A Study on DNN-Based Practical Model for Predicting Spot Color

Applied Sciences (Switzerland), ISSN: 2076-3417, Vol: 13, Issue: 24
2023
  • 3
    Citations
  • 0
    Usage
  • 6
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    3
    • Citation Indexes
      3
  • Captures
    6
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • 1

Most Recent News

Korea Institute of Industrial Technology Researchers Update Knowledge of Applied Sciences (A Study on DNN-Based Practical Model for Predicting Spot Color)

2024 JAN 04 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Science Daily -- Current study results on applied sciences have been published.

Article Description

The color of product packaging plays an important role in brand awareness and consistency. Given the importance of consistent color reproduction, the use of standardized spot colors is essential. However, the reproduction of specific spot colors in offset packaging printing involves additional processes and costs. This study presents an efficient approach to predict the color result of spot color inks in the packaging printing industry, using only the amount of ink involved in the mixing process as an input. Using a neural network-based model, our approach uses the CIEDE2000 color difference formula as a loss function to accurately estimate the final color. This method provides a simplified alternative to traditional color mixing techniques, which often involve subjective judgment and can be resource-intensive. Particularly beneficial for smaller companies, our approach reduces the complexity and cost associated with achieving accurate spot colors. The significance of this work lies in its practical application, providing a simpler, more objective and cost-effective solution for consistent color reproduction in packaging printing.

Bibliographic Details

Jaekyeong Moon; Geonhee Yang; Hyunchul Tae

MDPI AG

Materials Science; Physics and Astronomy; Engineering; Chemical Engineering; Computer Science

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