COMPUTATIONAL COLOR THEORY MODELS FOR OPTIMIZING VISUAL HARMONY IN DIGITAL ART PRODUCTION

Rahul  Thakur; Milind  Patil; Dhanalakshmi  V; Binggui  Lu; Santosh Kumar  Behera; Pavan  P. S.; Antonibiya  S

doi:10.29121/shodhkosh.v7.i4s.2026.7499

Authors

Rahul Thakur Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
Milind Patil Assistant Professor, Department of E&TC Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra 411037, India
Dhanalakshmi V Assistant Professor, Computer Science, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu 600080, India
Binggui Lu Faculty of Education Shinawatra University, Thailand
Santosh Kumar Behera Assistant Professor, Department of Centre for Artificial Intelligence and Machine Learning, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
Pavan P. S. Assistant Professor, Department of Civil Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India
Antonibiya S. Assistant Professor, Department of Mathematics, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu 600080, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7499

Keywords:

Computational Color Theory, Visual Harmony, Color Optimization, Digital Art, Machine Learning, Color Spaces, Aesthetic Evaluation, Generative Models

Abstract [English]

Color harmony is a form of visual aesthetics that exists in digital art and influences the perception, emotion and the quality of the entire design. The more complex the digital creation of art, the more the systematic means of picking out combinations of colors is becoming a necessity as a replacement of the older systems of color combinations that were picked out intuitively. The authors have discussed in this paper the computational colour theory model in visual harmony optimisation in the creation of digital artwork by integrating conventional colour theories with the latest algorithmic and data-driven techniques. The paper explains the theory of colour and colour space perceptions, and the measures of harmony, and then goes further to explain the computational methods of rule-based systems, mathematical modelling, optimization algorithms and machine learning strategies. It is recommended to introduce a comprehensive implementation structure, such as system architecture, data preparation, model training and digital art pipeline preparation. Experimental evaluation based on quantitative measurement is achieved using quantitative measures, such as perceptual color distance, harmony scoring and contrast ratio, and mean squared error and qualitative user study. The results show that machine learning and hybrid networks are more effective in the accuracy and aesthetic quality to provide more flexible and context-sensitive color suggestions. Subjectivity of color perception, biased datasets, and the complexity of computations involved is a critical concern (which will be discussed in the paper) and future prospects, including the use of individualized color models, interpretable AI, and real-time optimization systems are mentioned.

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