Reducing redundancy in videos using reference frame and clustering technique of key frame extraction

Abstract

Digital video is becoming an emerging force in current computer and telecommunication industries for its large mass of data. Video segmentation and key-frame extraction have become crucial for the development of advanced digital video systems. Key frame extraction is a very useful technique to provide a concise access to the video content and is the first step towards efficient browsing and retrieval in video databases. Existing approaches are either computationally expensive or ineffective in capturing salient visual content. The proposed system extracts key frames from input videos using two distinct, cost-effective algorithms namely reference based key frame extraction and clustering. It uses multiple characteristics such as co-relation, optical flow and mutual information to identify and extract key frames. The proposed system is able to extract the key frames efficiently for any video format & the extracted key frames can satisfactorily represent the salient content of the video. Storage is reduced by one-eighth of the total space required by the original video and the original content can be represented in one-fourth the time of the input video achieving very high compression efficiency & hence can be used in any video retrieval applications.

Quality assessment framework for video contextualisation of personal videos

“…Consequently, an advanced contrast sensitivity function, derived from the attention‐driven foveation mechanism, was modelled and then integrated into a wavelet‐based distortion visibility measure to build a full reference attention‐driven foveated video quality metric. Nasreen and Shobha [15] proposed a system that involves two main methods for extracting keyframes: (i) reference frame‐based method and (ii) correlation clustering algorithm. In the filtering phase, first a global threshold is used for all the frames of the video to eliminate redundancy to a large extent.…”

An Effective Scheme for Shot Boundary Detection and Key Frame Extraction for Video Retrieval

“…an information viewer narrating news story, replay in sporting activities, lecture videos, etc.. This results in several redundant representative frames [6] [10]. To get rid of these repetitive crucial frameworks, a filtering system action is carried out, where each representative frame is compared to every other representative frame to discover the duplicate or near-duplicate structures [14] [15].…”