TY - GEN
T1 - Video Compression Using Deep Learning Approach on Drone Video Footage
AU - Navastara, Dini Adni
AU - Maranatha, Reza Adipatria
AU - Shiddiqi, Ary Mazharuddin
N1 - Publisher Copyright:
© 2023 American Institute of Physics Inc.. All rights reserved.
PY - 2023/5/19
Y1 - 2023/5/19
N2 - This paper implements a video compression system using a deep learning approach based on autoencoder architecture. This method is an end-to-end video compression model that combines motion estimation, motion compression, motion compensation, residual compression, and entropy encoding. It is objective to compress drone video footage into a more feasible form for further usage. The comparison between the proposed model and traditional compression algorithm on the test scenarios, such as H.264 and H.265 (HEVC), is evaluated using the metric of PSNR and MS-SSIM. Based on the experimental result, the highest performance of the proposed model on the MS-SSIM metric is yielded on the UVG-Beauty dataset with an MS-SSIM score of 0.943, λ=64, and BPP value of 0.452. While, the highest performance of the proposed model on PSNR metric is obtained on the drone video data with a PSNR score of 36.88 dB, λ=2048, and BPP value of 0.206.
AB - This paper implements a video compression system using a deep learning approach based on autoencoder architecture. This method is an end-to-end video compression model that combines motion estimation, motion compression, motion compensation, residual compression, and entropy encoding. It is objective to compress drone video footage into a more feasible form for further usage. The comparison between the proposed model and traditional compression algorithm on the test scenarios, such as H.264 and H.265 (HEVC), is evaluated using the metric of PSNR and MS-SSIM. Based on the experimental result, the highest performance of the proposed model on the MS-SSIM metric is yielded on the UVG-Beauty dataset with an MS-SSIM score of 0.943, λ=64, and BPP value of 0.452. While, the highest performance of the proposed model on PSNR metric is obtained on the drone video data with a PSNR score of 36.88 dB, λ=2048, and BPP value of 0.206.
UR - http://www.scopus.com/inward/record.url?scp=85161385103&partnerID=8YFLogxK
U2 - 10.1063/5.0121137
DO - 10.1063/5.0121137
M3 - Conference contribution
AN - SCOPUS:85161385103
T3 - AIP Conference Proceedings
BT - Proceedings of the International Conference on Advanced Technology and Multidiscipline, ICATAM 2021
A2 - Widiyanti, Prihartini
A2 - Jiwanti, Prastika Krisma
A2 - Prihandana, Gunawan Setia
A2 - Ningrum, Ratih Ardiati
A2 - Prastio, Rizki Putra
A2 - Setiadi, Herlambang
A2 - Rizki, Intan Nurul
PB - American Institute of Physics Inc.
T2 - 1st International Conference on Advanced Technology and Multidiscipline: Advanced Technology and Multidisciplinary Prospective Towards Bright Future, ICATAM 2021
Y2 - 13 October 2021 through 14 October 2021
ER -