Color is an important ideographic element in film, television, and animation because variations in hue, saturation, brightness, and contrast can modify the emotional and semantic interpretation of an otherwise similar visual scene. Quantitative analysis of such color-related meaning requires spatially resolved visual understanding rather than global color statistics alone. This study investigates the use of artificial intelligence for creative-media analysis through RGB-D semantic segmentation. A two-stream weighted Gabor convolutional framework is employed to combine RGB appearance information with depth-based geometric structure. The method introduces a weighted Gabor directional filter to enhance orientation- and scale-sensitive representation, a wide residual weighted-Gabor convolutional network for lightweight feature extraction, and pyramid pooling for multi-scale contextual fusion. RGB and depth features are extracted separately, fused across multiple scales, decoded, and classified at the pixel level. Experiments are conducted using the standard NYUv2 training/test split and cross-dataset evaluation on SUN RGB-D. On NYUv2, the proposed configuration achieves 60.4% pixel accuracy, 50.9% mean accuracy, 40.2% mean intersection over union, and 53.2% frequency-weighted intersection over union. The method does not obtain the highest pixel accuracy among all listed baselines, but it achieves the strongest mean accuracy, mean IoU, and frequency-weighted IoU in the reported comparison. In the cross-dataset SUN RGB-D experiment, the proposed method achieves 58.3%, 38.6%, 28.3%, and 42.1%, respectively, outperforming the listed comparison configurations on all four reported measures. The results indicate that weighted Gabor representation, wide residual feature extraction, and multi-scale RGB-D fusion can provide useful structural robustness while maintaining moderate model complexity. The study therefore provides a computational basis for linking color-sensitive creative-media analysis with pixel-level semantic scene understanding.
