Abstract

The rapid growth of visual data has increased the demand for efficient image representation techniques that reduce storage and computational requirements while preserving structural information. Neural networks have provided powerful mechanisms for learning compact image representations, yet conventional backpropagation often struggles with local minima, slow convergence, and inefficient parameter optimization when handling highly compressed visual features. These limitations have created challenges for developing scalable learning frameworks that maintain reconstruction accuracy and representation efficiency. This study has proposed a hybrid meta-heuristic optimization framework for learning compressive neural image representations. The framework has integrated a Compressive Backpropagation Neural Network with a hybrid search mechanism that has combined Particle Swarm Optimization and Differential Evolution strategies. The hybrid mechanism has guided weight initialization and adaptive parameter tuning during training, which has improved the exploration and exploitation balance within the optimization space. The compressive representation module has transformed high-dimensional image data into compact latent vectors that preserved essential spatial patterns. The neural network has then reconstructed the images from these compressed representations through iterative backpropagation that has minimized the reconstruction loss. The meta-heuristic component has refined network parameters that ensured stable convergence and prevented premature stagnation. The experimental results show that the proposed framework achieves a peak PSNR of 37.4 dB, SSIM of 0.97, MSE as low as 0.009, and a compression efficiency of 24.2. The model converges rapidly within 58 epochs and 126 seconds, outperforming existing methods such as the Convolutional Neural Representation Model, Sparse Autoencoder Representation Model, and Particle Swarm Optimized Neural Network Model. These results indicate that the proposed hybrid meta-heuristic framework effectively balances high reconstruction accuracy with efficient compressive learning.

Authors

Suresh Kumar Sharma, Parul Dhull
Sri Karan Narendra Agriculture University, India

Keywords

Neural Image Representation, Compressive Learning, Hybrid Meta-Heuristic Optimization, Backpropagation Neural Networks, Image Reconstruction