An evaluation and study of detail contrast preservation and color consistency in decolorization

Abstract

Grayscale conversion plays a crucial role in image processing, particularly for edge detection and segmentation tasks, where decolorization quality directly impacts subsequent analysis. An ideal decolorization algorithm should be both efficient and robust while preserving color consistency and detail contrast. In this study, we revisit the RTCP (Real-time Contrast-Preserving Decolorization) algorithm and propose three key optimizations: a clustering-guided decolorization approach, a locally adaptive decolorization strategy, and a weight-optimized decolorization method. To enhance solution quality, we implement a constrained particle swarm optimization framework to systematically explore the parameter space. Experimental validation on two standard datasets (Ĉadík and CSDD) demonstrates that our optimized methods handle diverse decolorization scenarios more effectively while maintaining competitive performance against existing approaches. Recognizing the limitations of current evaluation metrics in assessing detail contrast preservation, we introduce the D-C2G-SSIM metric for more accurate quantitative assessment. Comparative results show consistent improvements over the original RTCP algorithm, with the average D-C2G-SSIM score increasing from 0.8331 to 0.8442 on Ĉadík dataset and from 0.8696 to 0.8847 on the CSDD dataset, confirming the effectiveness of our approach.