Hybrid System for Image Storage and Retrieval in Big Data Environments

This paper presents a hybrid image storage model for big data environments. The model combines relational and non-relational (NoSQL) databases, file systems (IPFS), and blockchain technologies to ensure an optimal balance between performance, scalability, and security in image storage. The existing approaches to organising image data storage and image compression methods in decentralised systems are analysed. Optimised image indexing is proposed to accelerate data search and access. A prototype system based on the proposed model was developed, and an experimental study was conducted on various image datasets (medical, satellite, and digital art). The experimental results demonstrate that the hybrid model outperforms traditional approaches: image access time is reduced by ~30% compared to standalone storage systems, providing high scalability (with increased nodes, processing time decreases nonlinearly). The efficiency of image compression in reducing storage costs in blockchain-oriented systems is also confirmed: the WebP format allows file size to be reduced by 40–60% while maintaining acceptable quality (PSNR > 30 dB). The proposed solution is relevant for medical diagnostics, video surveillance systems, geographic information systems, and other fields requiring reliable storage and fast processing of large-scale image datasets.