| Autor: |
Aro Kim, Seung-taek Woo, Minho Park, Dong-hwi Kim, Hanshin Lim, Soon-heung Jung, Sangwoon Kwak, Sang-hyo Park |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Image and Video Processing, Vol 2024, Iss 1, Pp 1-20 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1687-5281 |
| DOI: |
10.1186/s13640-024-00649-w |
| Popis: |
Abstract In the video compression industry, video compression tailored to machine vision tasks has recently emerged as a critical area of focus. Given the unique characteristics of machine vision, the current practice of directly employing conventional codecs reveals inefficiency, which requires compressing unnecessary regions. In this paper, we propose a framework that more aptly encodes video regions distinguished by machine vision to enhance coding efficiency. For that, the proposed framework consists of deep learning-based adaptive switch networks that guide the efficient coding tool for video encoding. Through the experiments, it is demonstrated that the proposed framework has superiority over the latest standardization project, video coding for machine benchmark, which achieves a Bjontegaard delta (BD)-rate gain of 5.91% on average and reaches up to a 19.51% BD-rate gain. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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