Efficient Memory Handling Model with Consistent Video Frame Duplication Removal with Precise Compression

Abstract

Massive amounts of videos are being made and shared online as mobile devices and social networks gain popularity in recent years. The enormous expansion in the amount of video data created has made storing and quickly searching it all quite difficult. Because many movies are duplicates or near-duplicates in practice, recognizing these copies has become a critical strategy for decreasing the amount of storage with duplicate removal models. Video compression is an important part of Internet video delivery for efficient memory management. Deep learning's growth has sparked a revival in video compression, with many frameworks offering comparable or even higher performance than traditional video codecs presented in recent years. Despite the advancement in rate-distortion, these models are substantially slower and need more memory, limiting their practical application. The exponentially increasing volume of video data created has presented enormous problems to video deduplication technologies. People are interested in uploading and sharing information in photo and video formats in this digital era. This expansion has resulted in increased storage capacity, which contains a large amount of redundant multimedia material. Many deduplication algorithms are being rapidly developed nowadays, although they are often slow and have rather imprecise identification processes. Deduplication is one of the emerging ways for coping with redundant data stored in several locations. When more than a copy of the same data is detected, a single copy is preserved, and the other data is replaced by pointers pointing to the preserved copy and also duplicate frames will be removed by segmenting the video for memory efficiency. Storage can be utilised to effectively store a large amount of other data. While there are many other types of deduplication algorithms, picture and video deduplication strategies and implementations receive a lot of attention since they are difficult to implement. In this research a Consistent Video Frame Duplication Removal with Precise Compression (CVFDR-PC) model for efficient memory handling is proposed. This research provides a versatile and efficient video frame deduplication framework with compression model that effectively handles the memory. The proposed model when contrasted with the existing methods exhibit better performance levels.