Wildlife conservation is dependent on camera trap technology to monitor animal populations and habitats. However, processing and storing the large datasets generated by these devices presents significant challenges, particularly in dynamic environments with a mix of relevant (animal) and irrelevant (background) images. To address these issues, we propose a novel Class-Agnostic Adaptive Triple Attention TCS framework that incorporates Temporal, Channel, and Spatial attention mechanisms for dataset optimization and adaptive compression in wildlife camera trap images. The former temporal attention phase helps in optimizing the dataset by removing redundant frames, whereas the latter phase with channel and spatial attention helps for context-aware variable-rate compression, by prioritizing and preserving key regions containing wildlife. Our triple-attention-based TCS framework significantly improves the processing and analysis of large wildlife image datasets, contributing to more effective and resource-efficient wildlife monitoring.