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Quality-Aware Dynamic Resolution Adaptation Framework for Adaptive Video Streaming (2403.10976v1)

Published 16 Mar 2024 in cs.MM

Abstract: 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/

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Authors (6)
  1. Amritha Premkumar (5 papers)
  2. Vignesh V Menon (21 papers)
  3. Adam Wieckowski (10 papers)
  4. Benjamin Bross (11 papers)
  5. Detlev Marpe (14 papers)
  6. Prajit T Rajendran (15 papers)
Citations (4)
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