Published In

Proceedings of the 4th ACM International Conference on Multimedia in Asia

Document Type

Article

Publication Date

12-2022

Subjects

Computer vision, Image processing -- Methodology, Neural networks (Computer science), Machine learning

Abstract

Video data is ubiquitous; capturing, transferring, and storing even compressed video data is challenging because it requires substantial resources. With the large amount of video traffic being transmitted on the internet, any improvement in compressing such data, even small, can drastically impact resource consumption. In this paper, we present a hybrid video compression framework that unites the advantages of both DCT-based and interpolation-based video compression methods in a single framework. We show that our work can deliver the same visual quality or, in some cases, improve visual quality while reducing the bandwidth by 10--20%.

Rights

© 2022 Copyright held by the owner/author(s).

Description

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

DOI

10.1145/3551626.3564959

Persistent Identifier

https://archives.pdx.edu/ds/psu/39067

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