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Video coding for machines using region-of-interest-based retargeting.

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Title: Video coding for machines using region-of-interest-based retargeting.
Authors: Różek, Sławomir1 (AUTHOR), Stankiewicz, Olgierd1 (AUTHOR) olgierd.stankiewicz@put.poznan.pl, Maćkowiak, Sławomir1 (AUTHOR), Grajek, Tomasz1 (AUTHOR), Stankowski, Jakub1 (AUTHOR), Wawrzyniak, Maciej1 (AUTHOR), Lorkiewicz, Mateusz1 (AUTHOR), Ding, Ding2 (AUTHOR), Liu, Shan2 (AUTHOR), Domański, Marek1 (AUTHOR)
Source: EURASIP Journal on Image & Video Processing. 10/24/2025, Vol. 2025 Issue 1, p1-17. 17p.
Subjects: Video compression, Video coding, Video processing, MPEG (Video coding standard), Computer vision, Object recognition (Computer vision)
Abstract: The work is focused on video compression for the scenarios, where the decoded video serves not only human viewers but also as input for systems implementing various machine vision tasks, such as object detection and tracking. The proposed innovative tool is based on retargeting video frames processed based on Regions of Interest (RoI), corresponding to the individual objects detected in the frames. Experimental evaluation demonstrates significant average bitrate reduction while maintaining the same quality, ranging from 3 to 57% depending on the machine vision task and encoding scenario. The proposal underwent thorough consideration within the MPEG group and was adopted for the upcoming Video Coding for Machines (VCM) technology. [ABSTRACT FROM AUTHOR]
Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Video coding for machines using region-of-interest-based retargeting.
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  Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Image+%26+Video+Processing%22">EURASIP Journal on Image & Video Processing</searchLink>. 10/24/2025, Vol. 2025 Issue 1, p1-17. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Video+compression%22">Video compression</searchLink><br /><searchLink fieldCode="DE" term="%22Video+coding%22">Video coding</searchLink><br /><searchLink fieldCode="DE" term="%22Video+processing%22">Video processing</searchLink><br /><searchLink fieldCode="DE" term="%22MPEG+%28Video+coding+standard%29%22">MPEG (Video coding standard)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink>
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  Data: The work is focused on video compression for the scenarios, where the decoded video serves not only human viewers but also as input for systems implementing various machine vision tasks, such as object detection and tracking. The proposed innovative tool is based on retargeting video frames processed based on Regions of Interest (RoI), corresponding to the individual objects detected in the frames. Experimental evaluation demonstrates significant average bitrate reduction while maintaining the same quality, ranging from 3 to 57% depending on the machine vision task and encoding scenario. The proposal underwent thorough consideration within the MPEG group and was adopted for the upcoming Video Coding for Machines (VCM) technology. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 10/24/2025
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