تقرير
Quality-Aware Dynamic Resolution Adaptation Framework for Adaptive Video Streaming
| العنوان: | Quality-Aware Dynamic Resolution Adaptation Framework for Adaptive Video Streaming |
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| المؤلفون: | Premkumar, Amritha, Rajendran, Prajit T, Menon, Vignesh V, Wieckowski, Adam, Bross, Benjamin, Marpe, Detlev |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Multimedia |
| الوصف: | Traditional per-title encoding schemes aim to optimize encoding resolutions to deliver the highest perceptual quality for each representation. XPSNR is observed to correlate better with the subjective quality of VVC-coded bitstreams. Towards this realization, we predict the average XPSNR of VVC-coded bitstreams using spatiotemporal complexity features of the video and the target encoding configuration using an XGBoost-based model. Based on the predicted XPSNR scores, we introduce a Quality-A ware Dynamic Resolution Adaptation (QADRA) framework for adaptive video streaming applications, where we determine the convex-hull online. Furthermore, keeping the encoding and decoding times within an acceptable threshold is mandatory for smooth and energy-efficient streaming. Hence, QADRA determines the encoding resolution and quantization parameter (QP) for each target bitrate by maximizing XPSNR while constraining the maximum encoding and/ or decoding time below a threshold. QADRA implements a JND-based representation elimination algorithm to remove perceptually redundant representations from the bitrate ladder. QADRA is an open-source Python-based framework published under the GNU GPLv3 license. Github: https://github.com/PhoenixVideo/QADRA Online documentation: https://phoenixvideo.github.io/QADRA/ Comment: ACM MMSys '24 | Open-Source Software and Dataset. arXiv admin note: substantial text overlap with arXiv:2401.15346 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1145/3625468.3652172 |
| URL الوصول: | http://arxiv.org/abs/2403.10976 |
| رقم الأكسشن: | edsarx.2403.10976 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1145/3625468.3652172 |
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