Object-Ratio-Preserving Video Retargeting Framework Based on Segmentation and Inpainting

Jun-gyu Jin, Jaehyun Bae, Han-gyul Baek, Sang-hyo Park; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2023, pp. 497-503

Abstract

The recent development of video-based content platforms led the easy access to videos decades ago. However, some past videos have a old screen ratio. If an image with this ratio is executed on a display with a wider screen ratio, the image is excessively stretched horizontally or creates a black box, which prevents efficient viewing of content. In this paper, we propose a method for retargeting the old ratio video frames to a wider ratio while maintaining the original ratio of important objects in content using deep learning-based semantic segmentation and inpainting techniques. Our research shows that proposed method can make a retargeted frames visually natural.

Related Material

[pdf]
[bibtex]
@InProceedings{Jin_2023_WACV, author = {Jin, Jun-gyu and Bae, Jaehyun and Baek, Han-gyul and Park, Sang-hyo}, title = {Object-Ratio-Preserving Video Retargeting Framework Based on Segmentation and Inpainting}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops}, month = {January}, year = {2023}, pages = {497-503} }