Enhanced Color Image Quantization Utilizing Hybrid Glowworm Swarm Optimization and K-Means Clustering: A Case Study with Chandrayaan-3 Images

The necessity for color image quantization arises from limitations in image display, output, storage, and transmission devices. This paper proposes a novel metaheuristic approach using Glowworm Swarm Optimization (GSO) hybridized with K-means clustering to achieve optimal color quantization solutions. We start by analyzing the quantization problem and its formulation, followed by introducing GSO and its application to the problem. To evaluate GSO's effectiveness, we conduct a comparative study with conventional heuristics and the K-means clustering algorithm. Experiments on diverse images, including the Kodak dataset and Chandrayaan-3 mission photos, reveal that a well-implemented GSO with an appropriate initialization strategy serves as a highly effective metaheuristic color quantizer. Results underscore GSO's superiority over other algorithms, highlighting its potential to enhance color quantization in various applications.