International Journal of Knowledge-based and Intelligent Engineering Systems 23 (2019) 249–258 249 DOI 10.3233/KES-190416 IOS Press Content relative thresholding technique for key frame extraction K. Mallikharjuna Lingam * and V.S.K. Reddy Faculty of Engineering, Lincoln University College, Malaysia 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 communi- cation 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 demon- strates 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 1. Introduction Any key frame extracted from a video can represent a lot of information. As the key frames provides a suit- able meta-data for indexing, browsing and retrievals. The review work by Aigrain et al. [12] presents a sig- nificant proof of concepts demonstrating the benefits of key frame extractions for various video processing and information extraction methods. The notable work by Zhang et al. [6] also proves that the key frame ex- traction and key frame-based indexing, searching and retrieving can be faster for any video datasets. The searching methods can quickly extract the desired key frames based on the analysis carried out for only key frames. None of the single research has predicted the accurate number of key frames to be extracted based on the length of the video. As the optimal number * Corresponding author: K. Mallikharjuna Lingam, Faculty of Engineering, Lincoln University College, Malaysia. E-mail: mallikharjuna@lincoln.edu.my. of key frames depend on the density of the informa- tion present in the video. The information density can be estimated based on the supported audio codec or the colour variations between the frames of the im- age. Thus it is natural to understand that the high den- sity video will have more information available com- pared to the less denser video data. Thus, this becomes the most prominent method for key frame extraction. Zhang et al. [7] attempted first to extract the key frames based on the colour histogram difference between the first key frame and the sub sequent frames. This idea was widely accepted due to the nature of accumulat- ing the effects of object motion or camera motion. This proposal was enhanced by Gunsel and Tekalp [2] by enhancing the threshold based key frame selection method and demonstrated significant improvements. Another direction of the same research is to cluster the frames based on the colour depth and further extract the significant frames from each cluster groups in or- der to formulate the key frame sequence. This proposal was first proposed by Hanjalic and Zhang [1]. Never- ISSN 1327-2314/19/$35.00 c  2019 – IOS Press and the authors. All rights reserved