Hash Overhead Analysis for GOP-level Video Deduplication in Cloud Storage Environment

Duplicate video files consume valuable storage capacity. Video deduplication removes needless redundancy, to reclaim the storage space and provides an affordable solution that lowers long-term operating expenses. The recent work on hash-based deduplication mechanism uses adaptive Group of Pictures (GOP) structure focusing I-frames based hash for identifying duplicate videos. The storage required for hash of all I-frames in a video increase with the size of the video. The goal of effective storage with video deduplication is abound. This paper addresses the issue of hash storage overhead based on I-frame of the GOP structure. All I-frames are collected in a temporary buffer and a hash is computed on it instead of individual I-frame hash. This single hash value generated is used to check for video duplication. The database is updated based on result of video deduplication. This methodology shows increased efficiency, performance and resource utilization, due to the fact that it is found to have lesser execution time, hashing time, as well as lesser amount of storage space that is employed.