Context-Aware Neural Video Compression on Solar Dynamics Observatory

Thumbnail Image

Files

Context-Aware_Neural_Video_Compression_on_Solar_Dynamics_Observatory.pdf (789.1 KB)

Links to Files

https://ieeexplore.ieee.org/abstract/document/10460000

Permanent Link

https://doi.org/10.1109/ICMLA58977.2023.00098
 http://hdl.handle.net/11603/30940

Collections

UMBC Goddard Planetary Heliophysics Institute (GPHI)
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0001-7537-3539

Date

2024-03-19

Type of Work

conference papers and proceedings
Text

Department

Program

Citation of Original Publication

Khoshkhahtinat, Atefeh, Ali Zafari, Piyush M. Mehta, Nasser M. Nasrabadi, Barbara J. Thompson, Michael S. F. Kirk, and Daniel Da Silva. “Context-Aware Neural Video Compression on Solar Dynamics Observatory.” In 2023 International Conference on Machine Learning and Applications (ICMLA), 667–74, 2023. https://doi.org/10.1109/ICMLA58977.2023.00098.

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0

Subjects

Abstract

NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. Data compression is crucial for space missions to reduce data storage and video bandwidth requirements by eliminating redundancies in the data. In this paper, we present a novel neural Transformer-based video compression approach specifically designed for the SDO images. Our primary objective is to efficiently exploit the temporal and spatial redundancies inherent in solar images to obtain a high compression ratio. Our proposed architecture benefits from a novel Transformer block called Fused Local-aware Window (FLaWin), which incorporates window-based self-attention modules and an efficient fused local-aware feed-forward (FLaFF) network. This architectural design allows us to simultaneously capture short-range and long-range information while facilitating the extraction of rich and diverse contextual representations. Moreover, this design choice results in reduced computational complexity. Experimental results demonstrate the significant contribution of the FLaWin Transformer block to the compression performance, outperforming conventional hand-engineered video codecs such as H.264 and H.265 in terms of rate-distortion trade-off.