Enhanced FSS Algorithm for Robust Vector Quantization in Image Processing

In order to reduce data redundancy while keeping the integrity of visual information intact, Vector Quantization (VQ) is a crucial approach in image processing and compression. When it comes to high-dimensional data spaces and real-time processing, traditional VQ approaches often have trouble with optimization. In order to provide a more reliable method for vector quantization, this research presents an improved Firefly Search Strategy (FSS) algorithm. The suggested technique improves picture reconstruction accuracy and efficiency by optimizing the codebook generation process using the FSS's nature-inspired metaheuristic features. Improvements in convergence rates and solution quality are greatly aided by key additions such as adaptive attraction formulation and dynamic light intensity adjustment. In comparison to traditional approaches, the algorithm demonstrated a 15% improvement in quantization error reduction and an average increase of 3 dB in PSNR (Peak Signal-to-Noise Ratio) values when evaluated on typical datasets such as the CIFAR-10 and MNIST. When it comes to complicated and varied picture collections, the Enhanced FSS approach is far more resilient and beats current vector quantization methods. Where speed and accuracy are of the utmost importance, this development may find use in digital media, satellite photography, and medical image processing.