Quality-Aware Dynamic Resolution Adaptation Framework for Adaptive Video Streaming
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914717501489152 |
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| author | Premkumar, Amritha Rajendran, Prajit T Menon, Vignesh V Wieckowski, Adam Bross, Benjamin Marpe, Detlev |
| author_facet | Premkumar, Amritha Rajendran, Prajit T Menon, Vignesh V Wieckowski, Adam Bross, Benjamin Marpe, Detlev |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10976 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quality-Aware Dynamic Resolution Adaptation Framework for Adaptive Video Streaming Premkumar, Amritha Rajendran, Prajit T Menon, Vignesh V Wieckowski, Adam Bross, Benjamin Marpe, Detlev Multimedia 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/ |
| title | Quality-Aware Dynamic Resolution Adaptation Framework for Adaptive Video Streaming |
| topic | Multimedia |
| url | https://arxiv.org/abs/2403.10976 |