Objective Enhancement for Image and Video Compression Using Feature Extraction and Fast RNN-Based Motion Estimation Optimisation

Abstract

Enhancing compressed visual content remains challenging due to visual degradation, motion distortions, and poor temporal coherence. Existing methods often fail to balance detail preservation with accurate motion estimation, especially under high compression or motion. To address this, we introduce the Faster-Recurrent Neural Network-Swarm Intelligence Metaheuristic of the CT Optimisation Algorithm (F-RNN-SIMCT), a novel method combining a fast recurrent neural network with swarm intelligence inspired by coyotes and tuna fish. This hybrid approach optimises motion estimation and preserves spatial-temporal details under harsh compression. F-RNN-SIMCT leverages advanced feature extraction and metaheuristic optimisation to improve motion accuracy and perceptual quality. Experiments show it outperforms standard methods, making it suitable for video transmission and storage in bandwidth-limited environments.