Hybrid Images for Personalized Media Streaming Optimization

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Publisher: SMPTE
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Motion Imaging Jour.
Volume: 133, No. 4, pp. 23–34
Abstract: Efficient delivery of media while maintaining or improving the quality of experience (QoE) poses a constant challenge. On the media playback side, various factors, such as device characteristics, viewing conditions, and individual visual acuity, affect QoE. This paper proposes a novel method to estimate viewers' visible frequency ranges to optimize media delivery tailored to individual users and playback contexts. Our approach utilizes hybrid images created by combining high and low-frequency components of two different images. By measuring user perception in hybrid image tasks, we can estimate the minimum video resolution required to maintain quality, reducing unnecessary data transmission without sacrificing user experience. Our experimental results demonstrate a strong correlation between the proposed method and traditional approaches while offering the advantages of lower complexity of cognitive process and user effort. This technique paves the way for personalized and adaptive media delivery in various viewing conditions and on diverse devices.
Publication Date: 2024-07-01
DOI: 10.5594/JMI.2024/DPLO3149
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2024/DPLO3149
Author(s): Doh-Suk Kim
Scott Daly
Ludovic Malfait
Neel Chaudhari
Jeffrey Riedmiller
Keyword(s): QoE, Hybrid Image, Playback-Side Context, Image Resolution
Copyright: © 2024 SMPTE

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Doh-Suk Kim, Scott Daly, Ludovic Malfait, Neel Chaudhari, and Jeffrey Riedmiller; Hybrid Images for Personalized Media Streaming Optimization, MIJ 2024, Volume 133, Number 4 (pp. 23 to 34); SMPTE, 2024. Available at https://doi.org/10.5594/JMI.2024/DPLO3149

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Doh-Suk Kim, Scott Daly, Ludovic Malfait, Neel Chaudhari, and Jeffrey Riedmiller; Hybrid Images for Personalized Media Streaming Optimization, MIJ 2024, Volume 133, Number 4 (pp. 23 to 34); SMPTE, 2024. Available at https://doi.org/10.5594/JMI.2024/DPLO3149

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<span class="citation">Doh-Suk Kim, Scott Daly, Ludovic Malfait, Neel Chaudhari, and Jeffrey Riedmiller; <cite>Hybrid Images for Personalized Media Streaming Optimization</cite>, MIJ 2024, Volume 133, Number 4 (pp. 23 to 34); SMPTE, 2024. Available at <a href="https://doi.org/10.5594/JMI.2024/DPLO3149" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2024/DPLO3149</a></span>

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Doh-Suk Kim, Scott Daly, Ludovic Malfait, Neel Chaudhari, and Jeffrey Riedmiller; Hybrid Images for Personalized Media Streaming Optimization, MIJ 2024, Volume 133, Number 4 (pp. 23 to 34); SMPTE, 2024
doi: 10.5594/JMI.2024/DPLO3149
url: https://doi.org/10.5594/JMI.2024/DPLO3149

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<li>
Doh-Suk Kim, Scott Daly, Ludovic Malfait, Neel Chaudhari, and Jeffrey Riedmiller; <cite id="bib-10-5594-jmi-2024-dplo3149">Hybrid Images for Personalized Media Streaming Optimization</cite>, MIJ 2024, Volume 133, Number 4 (pp. 23 to 34); SMPTE, 2024
<span class="doi">10.5594/JMI.2024/DPLO3149</span>
</li>