Optimal Transcoding Resolution Prediction for Efficient Per-Title Bitrate Ladder Estimation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917562291322880 |
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| author | Yang, Jinhai Guo, Mengxi Zhao, Shijie Li, Junlin Zhang, Li |
| author_facet | Yang, Jinhai Guo, Mengxi Zhao, Shijie Li, Junlin Zhang, Li |
| contents | Adaptive video streaming requires efficient bitrate ladder construction to meet heterogeneous network conditions and end-user demands. Per-title optimized encoding typically traverses numerous encoding parameters to search the Pareto-optimal operating points for each video. Recently, researchers have attempted to predict the content-optimized bitrate ladder for pre-encoding overhead reduction. However, existing methods commonly estimate the encoding parameters on the Pareto front and still require subsequent pre-encodings. In this paper, we propose to directly predict the optimal transcoding resolution at each preset bitrate for efficient bitrate ladder construction. We adopt a Temporal Attentive Gated Recurrent Network to capture spatial-temporal features and predict transcoding resolutions as a multi-task classification problem. We demonstrate that content-optimized bitrate ladders can thus be efficiently determined without any pre-encoding. Our method well approximates the ground-truth bitrate-resolution pairs with a slight Bjøntegaard Delta rate loss of 1.21% and significantly outperforms the state-of-the-art fixed ladder. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_04405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimal Transcoding Resolution Prediction for Efficient Per-Title Bitrate Ladder Estimation Yang, Jinhai Guo, Mengxi Zhao, Shijie Li, Junlin Zhang, Li Multimedia Artificial Intelligence Computer Vision and Pattern Recognition Image and Video Processing Adaptive video streaming requires efficient bitrate ladder construction to meet heterogeneous network conditions and end-user demands. Per-title optimized encoding typically traverses numerous encoding parameters to search the Pareto-optimal operating points for each video. Recently, researchers have attempted to predict the content-optimized bitrate ladder for pre-encoding overhead reduction. However, existing methods commonly estimate the encoding parameters on the Pareto front and still require subsequent pre-encodings. In this paper, we propose to directly predict the optimal transcoding resolution at each preset bitrate for efficient bitrate ladder construction. We adopt a Temporal Attentive Gated Recurrent Network to capture spatial-temporal features and predict transcoding resolutions as a multi-task classification problem. We demonstrate that content-optimized bitrate ladders can thus be efficiently determined without any pre-encoding. Our method well approximates the ground-truth bitrate-resolution pairs with a slight Bjøntegaard Delta rate loss of 1.21% and significantly outperforms the state-of-the-art fixed ladder. |
| title | Optimal Transcoding Resolution Prediction for Efficient Per-Title Bitrate Ladder Estimation |
| topic | Multimedia Artificial Intelligence Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2401.04405 |