{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:04:20Z","timestamp":1760231060379,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korean Government (MSIT)","award":["2021-0-00087","4199990214394"],"award-info":[{"award-number":["2021-0-00087","4199990214394"]}]},{"name":"Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea","award":["2021-0-00087","4199990214394"],"award-info":[{"award-number":["2021-0-00087","4199990214394"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose an object-cooperated decision method for efficient ternary tree (TT) partitioning that reduces the encoding complexity of versatile video coding (VVC). In most previous studies, the VVC complexity was reduced using decision schemes based on the encoding context, which do not apply object detecion models. We assume that high-level objects are important for deciding whether complex TT partitioning is required because they can provide hints on the characteristics of a video. Herein, we apply an object detection model that discovers and extracts the high-level object features\u2014the number and ratio of objects from frames in a video sequence. Using the extracted features, we propose machine learning (ML)-based classifiers for each TT-split direction to efficiently reduce the encoding complexity of VVC and decide whether the TT-split process can be skipped in the vertical or horizontal direction. The TT-split decision of classifiers is formulated as a binary classification problem. Experimental results show that the proposed method more effectively decreases the encoding complexity of VVC than a state-of-the-art model based on ML.<\/jats:p>","DOI":"10.3390\/s22176328","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T02:55:34Z","timestamp":1661309734000},"page":"6328","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Object-Cooperated Ternary Tree Partitioning Decision Method for Versatile Video Coding"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0258-3196","authenticated-orcid":false,"given":"Sujin","family":"Lee","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea"}]},{"given":"Sang-hyo","family":"Park","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea"}]},{"given":"Dongsan","family":"Jun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Dong-A University, Busan 49315, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_2","unstructured":"Pereira, F., Burges, C., Bottou, L., and Weinberger, K. 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