Neural Network-Assisted Optimization Strategy for Animation Color and Light and Shadow Effects

This article explores the optimization of color and lighting effects in animation design and proposes a neural network-based strategy. Traditional methods often suffer from inefficiency and subpar results in color matching and light-shadow processing. To address this, a deep learning model was developed, integrating color space conversion, color transfer, and detail enhancement techniques to optimize animation visuals. During the experiments, 50 color enhancement tests were conducted on the same base image to compare the proposed system with traditional approaches in terms of speed and quality. Results show that the new system significantly improves processing speed while delivering superior image clarity, richer colors, and more realistic lighting effects. Practical case comparisons further confirm the effectiveness of the strategy in real-world applications. In conclusion, this study introduces an innovative technical approach for animation design, enhancing both efficiency and visual quality in color and lighting optimization.