Content relative thresholding technique for key frame extraction

Article type: Research Article

Authors: Lingam, K. Mallikharjuna^* | Reddy, V.S.K.

Affiliations: Faculty of Engineering, Lincoln University College, Malaysia

Correspondence: [*] Corresponding author: K. Mallikharjuna Lingam, Faculty of Engineering, Lincoln University College, Malaysia. %****␣kes-23-kes190416_temp.tex␣Line␣25␣**** E-mail: mallikharjuna@lincoln.edu.my.

Abstract: The growth in communication methods have motivated a good number of users to migrate the existing communication methods towards video-based communications. Thus, the use of video-based communications have become the basic communication method for various fields and domains as distance education, business, physical security monitoring and also in the field of news and media. The summarization process demands to extract key components from the video data in order to reduce the size of the data without compromising on any information loss. This processing is called key frame extraction process. Realizing the priority of the key frame extraction process, a few parallel research attempts were executed to match with the bottleneck of information loss and size reduction. Nevertheless, the processes were highly criticised for being time complex and sometimes for information loss. The issue with the standard or parallel methods for extraction of key frames is either high or low rate of key frame extractions, which in turn results into high size or high information loss respectively. Thus, this work aims to provide a novel key frame extraction process using the image meta data and further the adaptive thresholding method. The work demonstrates a nearly 50% reduction in time complexity with 100% accuracy of the key frame extraction process and finally a nearly 30% reduction in the key frame replication control.

Keywords: Key frame extraction, automated framework, data replication control, reduced time complexity, video stabilization

DOI: 10.3233/KES-190416

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 23, no. 4, pp. 249-258, 2019