MUJTABA, Ghulam, KHOWAJA, Sunder Ali, JARWAR, Aslam, CHOI, Jaehyuk and RYU, Eun-Seok (2023). FRC-GIF: Frame Ranking-based Personalized Artistic Media Generation Method for Resource Constrained Devices. IEEE Transactions on Big Data. [Article]
Documents
32680:626625
PDF
2023_FRC_GIF_final.pdf - Accepted Version
Available under License Creative Commons Attribution.
2023_FRC_GIF_final.pdf - Accepted Version
Available under License Creative Commons Attribution.
Download (19MB) | Preview
32680:627936
PDF
Jarwar-FRC-GIFFrameRanking(VoR).pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Jarwar-FRC-GIFFrameRanking(VoR).pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Download (4MB) | Preview
Abstract
Generating video highlights in the form of animated
graphics interchange formats (GIFs) has significantly simplified the process of video browsing. Animated GIFs have paved the way for applications concerning streaming platforms and emerging technologies. Existing studies have led to large computational complexity without considering user personalization. This paper proposes lightweight method to attract users and increase views of videos through personalized artistic media, i.e., static thumbnails and animated GIF generation. The proposed method analyzes lightweight thumbnail containers (LTC) using the computational resources of the client device to recognize
personalized events from feature-length sports videos. Next, the thumbnails are then ranked through the frame rank pooling method for their selection. Subsequently, the proposed method processes small video segments rather than considering the whole video for generating artistic media. This makes our approach more computationally efficient compared to existing methods that use the entire video data; thus, the proposed method complies with sustainable development goals. Furthermore, the proposed method retrieves and uses thumbnail containers and video
segments, which reduces the required transmission bandwidth
as well as the amount of locally stored data. Experiments reveal that the computational complexity of our method is 3.73 times lower than that of the state-of-the-art method.
More Information
Statistics
Downloads
Downloads per month over past year
Metrics
Altmetric Badge
Dimensions Badge
Share
Actions (login required)
View Item |