The exponential growth of digital video content has intensified storage requirements in cloud environments, creating significant challenges related to redundancy, scalability, and resource optimization. Traditional deduplication mechanisms primarily rely on exact matching techniques, which often fail to identify semantically similar video segments due to variations in resolution, compression, encoding formats, and content representation. This research proposes a Layered Feature Resolution Model with Intelligent Hashing (LFRM-IH) for scalable cloud-based video redundancy elimination. The proposed approach integrates hierarchical feature extraction, adaptive resolution analysis, clustering-assisted similarity identification, and intelligent hashing mechanisms to improve redundancy detection efficiency while maintaining data integrity and storage security. The theoretical foundation of the model is derived from existing cloud deduplication studies focusing on hashing, clustering, content-defined chunking, and secure distributed storage mechanisms. The framework introduces multiple feature-resolution layers where coarse-level visual characteristics are initially analyzed before applying fine-grained hashing comparisons, reducing computational overhead during large-scale video processing. Existing research demonstrates the effectiveness of hashing and clustering strategies in cloud deduplication environments, while video-specific approaches highlight the importance of structural segmentation and adaptive processing. The proposed model addresses limitations in existing systems by combining scalable feature representation with intelligent hash decision-making. Furthermore, integration considerations with cloud-edge environments and secure deduplication mechanisms are analyzed to improve practical applicability. The research contributes a conceptual architecture for efficient video redundancy elimination capable of supporting modern cloud storage ecosystems with improved scalability, reduced storage consumption, and optimized computational performance.

Video Deduplication, Cloud Storage, Intelligent Hashing, Feature Resolution

1. Chen, L., F. Xiang, and Z. Sun, “Image deduplication based on hashing and clustering in cloud storage,” KSII Trans. Internet Inf. Syst., vol. 15, no. 4, pp. 1448–1463, 2021.

2. Hema, S., and A. Kangaiammal, “Distributed storage hash algorithm (DSHA) for file-based deduplication in cloud computing,” in Second International Conference on Computer Networks and Communication Technologies: ICCNCT 2019, Coimbatore, India: Springer International Publishing, 2020, pp. 572–581.

3. Ke, Z., H. Gong, and D. H. Du, PM-Dedup: Secure Deduplication with Partial Migration from Cloud to Edge Servers. arXiv preprint arXiv:2501.02350, 2025.

4. Periasamy, J. K., and B. Latha, “Efficient hash function–based duplication detection algorithm for data deduplication deduction and reduction,” Concurr. Comput.: Pract. Exp., vol. 33, no. 3, pp. e5213, 2021.

5. Sujatha, G. et al., “An efficient cloud storage model for GOP-Level video deduplication using adaptive GOP structure,” Cybern. Syst., vol. 54, pp. 1–26, 2023.

6. Tan, Y. et al., “Improving the performance of deduplication-based storage cache via content-driven cache management methods,” IEEE Trans. Parallel Distrib. Syst., vol. 32, no. 1, pp. 214–228, 2020.

7. Wu, J., and Y. Fu, “SGX based cloud-edge collaborative secure deduplication,” in Workshop Proceedings of the 53rd International Conference on Parallel Processing, Gotland Sweden: Association for Computing Machinery New York NY United States, 2024, pp. 112–113.

8. Xia, W. et al., “The design of fast content-defined chunking for data deduplication-based storage systems,” IEEE Trans. Parallel Distrib. Syst., vol. 31, no. 9, pp. 2017–2031, 2020.

9. K. S. Hebbar, “MACHINE LEARNING-ASSISTED SERVICE BOUNDARY DETECTION FOR MODULARIZING LEGACY SYSTEMS,” International Journal of Applied Engineering & Technology, vol. 04,no.02, pp. 401-414, Sep. 2022, https://romanpub.com/resources/ijaet-v4-2-2022-48.pdf

10. M. A. Hussain, V. B. Meruga, A. K. Rajamandrapu, S. R. Varanasi, S. S. S. Valiveti and A. G. Mohapatra, "Generative AI Sensor Fusion for Secure Digital Twin Ecosystems: A Standardization-Aligned Framework for Cyber-Physical Systems," in IEEE Communications Standards Magazine, doi: 10.1109/MCOMSTD.2026.3660106.