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Explore Cross-Codec Quality-Rate Convex Hulls Relation for Adaptive Streaming

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arxiv 2408.09044 v1 pith:RDRXVXMV submitted 2024-08-16 eess.IV

classification eess.IV
keywords videobitrateconvexpsnrqualitywereadaptivecodecs
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With the ongoing advancement of video technology and the emergence of new video platforms, suppliers of video contents are striving to ensure that the video quality meets the desire of consumers. Accessing a limited amount of channel bandwidth, they are often looking for a novel approach to decrease the use of data and thus the required energy and cost. This study evaluates the Quality Rate performance of H.264, H.265, and VP9 codecs across resolutions (960*544, 1920*1080, 3840*2160) to optimize video quality while minimizing bitrate, crucial for energy and cost efficiency. At this approach, original videos at native resolutions were encoded, decoded, and rescaled using FFmpeg. For each resolution, encoding and decoding were performed at various quantization levels. Quality Rate (QR) curves were generated using PSNR and VMAF metric against bitrate. Convex Hull curves were then derived and mathematically modelled for each resolution. The procedure was systematically applied to H.264, H.265, and VP9 codecs. Results indicate that increasing CRF values reduce bitrate, PSNR, and VMAF, with PSNR ranging between 20-40 dB. Logarithmic polynomial modelling of convex hulls demonstrated high accuracy, with low RMSE and high R-Squared values. These findings suggest that the convex hull of one codec can predict the performance of others, aiding future content-driven prediction methodologies and enhancing adaptive streaming efficiency. Keywords: Video Codecs, Adaptive Streaming, Compression, Bitrate, PSNR, VMAF, H.264, H.265, VP9

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining

    cs.CL 2025-06 reject novelty 3.0 of 10

    A proposed Persian biomedical LLM, BioPars, is evaluated on medical QA datasets and reported to beat GPT-4 on a self-built Persian QA benchmark, but the training setup is not described.

  2. Analysis of Biomedical Data to Assess the Risk of Heart Rate Variability in Athletes Participating in Long-Term Excessive Endurance Exercise

    q-bio.OT 2025-06 reject novelty 1.0 of 10

    A narrative review asserting that endurance exercise improves cardiovascular risk factors but not atherosclerosis, with no original data to support the assertion.

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