Dynamic-Clustering-Based Color Quantization for Electrophoretic Display

Electrophoretic Display (EPD) is a reflective technology that closely mimics traditional paper, making it a popular choice in E-readers, IoT devices, and wearables. However, color quantization, which is a critical step to display natural images on EPD with reduced color scales, usually leads to grayscale distortion and edge loss. In this letter, we propose a Dynamic-Clustering-based E-paper Color Quantization (DCECQ) method to address the above issue. First, it employs a dynamically adjustable Particle Swarm Optimization (PSO) clustering, facilitating adaptive threshold optimization for diverse image content. Second, it introduces a Human Visual System (HVS) based model to quantify visual errors and compensates for grayscale ghosting, effectively reducing artifacts such as edge blurring and color distortion. Third, it implements a validation platform for EPD to assess performance under real-world conditions. Experimental results demonstrate that our approach outperforms existing methods across multiple metrics, which attests to its effectiveness and practical applicability.