Perceptual Color Difference Modeling Using Machine Learning and Human Similarity Judgments

arXiv:2609.39130 · cs.CV, cs.HC · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.HC

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Accuracy — 4× in this paper · explained in 1 episode
• Consistency — 4× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Mean Squared Error — 2× in this paper
• School of Information Technology and Engineering — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Feature Selection — 1× in this paper · explained in 4 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• International Conference Information Visualisation — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models