Rate-Distortion Optimized Temporal Segmentation Using Reinforcement Learning for Video Coding

Jung Kyung Lee, Nayoung Kim, Je Won Kang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we present a reinforcement learning (RL)-based coding method to recursively divide video frames into several groups displaying similar temporal characteristics and improve rate-distortion (R-D) performance. Although the previous works have attempted to challenge the problem with analytical models, it was difficult to address complicated de-pendencies of video frames. In the proposed method, we cast the recursive problem as a sequence of a state-action for an agent to conduct an RL, by partitioning the current group to the half. The optimal solution is obtained by maximizing a reward function of the RL policy. Experimental results demonstrate that the proposed method can adapt to a video sequence whereas a fixed coding scheme cannot efficiently achieve optimal coding performance in dynamic video sequences.

Original languageEnglish
Title of host publication2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1598-1601
Number of pages4
ISBN (Electronic)9789881476890
StatePublished - 2021
Event2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
Duration: 14 Dec 202117 Dec 2021

Publication series

Name2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Country/TerritoryJapan
CityTokyo
Period14/12/2117/12/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work has been supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(No.NRF-2019R1C1C1010249). This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2020-0-01460) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation. This work was supported by Institute of Information & communications TechnologyPlanning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-00920, Development of Ultra High Resolution Unstructured Plenoptic Video Storage/Compression/Streaming Technologyfor Medium to Large Space)

Publisher Copyright:
© 2021 APSIPA.

Fingerprint

Dive into the research topics of 'Rate-Distortion Optimized Temporal Segmentation Using Reinforcement Learning for Video Coding'. Together they form a unique fingerprint.

Cite this