FRC-GIF: Frame Ranking-Based Personalized Artistic Media Generation Method for Resource Constrained Devices

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 computati...

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Bibliographic Details
Published in:IEEE transactions on big data 2024-08, Vol.10 (4), p.343-355
Main Authors: Mujtaba, Ghulam, Khowaja, Sunder Ali, Jarwar, Muhammad Aslam, Choi, Jaehyuk, Ryu, Eun-Seok
Format: Article
Language:English
Subjects:
client-driven
Complexity
Containers
Convolutional neural networks
Customization
Feature extraction
Feature recognition
feature-length videos
Lightweight
Media
Personalized media
Segments
Sports
Streaming media
Sustainable development
Three-dimensional displays
thumbnail container
Video data
video highlights
Video transmission
Visualization
Weight reduction
Citations: Items that cite this one
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Summary: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.
ISSN:2332-7790
2332-7790
2372-2096
DOI:10.1109/TBDATA.2023.3338012