Masi Entropy Based Multi‐Level Thresholding Segmentation Using Reinforcement Learning Assisted Firefly Oriented Multiverse Optimizer

ABSTRACT Segmentation is a crucial step to divide an image into background and foreground sections and uses different colors to identify the target portion. Different thresholding‐based segmentations have been developed in the current research analysis; however, the problems are difficult to solve and take a long time. To overcome these existing limitations, a novel Masi entropy‐based multi‐level thresholding with an effective optimization algorithm is introduced for image segmentation. The input images are collected from the open‐source dataset, namely the Berkeley Segmentation Dataset 500 (BSDS500) and the Cityscapes dataset. To remove noise and enhance image quality, use the Quantized Haar Wavelet Assisted Histogram Equalization (QuaWHe) technique in the pre‐processing stage. After noise removal, image segmentation was performed by the 2D practical Masi entropy histogram function (2D‐MentH) with the Reinforcement Learning‐assisted fire‐fly‐oriented multiverse optimizer (RL‐FF‐MVO) algorithm. The RL‐FF‐MVO mechanism helps to select the optimal set of threshold values from the values obtained using the 2D‐MentH mechanism. By integrating reinforcement learning, the optimization process's convergence speed and accuracy are greatly increased while computing overhead is decreased. The proposed model has obtained Peak Signal Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values of 30.587 and 0.9995 at threshold level 10. At threshold level 10, the proposed model has obtained Mean Square Error (MSE) and Feature Similarity Index (FSIM) values of 0.8531 and 0.96 248. For the Probabilistic Rand Index (PRI), the proposed model obtains a value of 0.72003, and for Variation of Information (VOI), it achieves 4.0298 at threshold level 10. The proposed approach outperforms existing methods regarding segmentation quality and computational efficiency, making it suitable for applications requiring high‐accuracy image analysis, such as autonomous systems and medical imaging.