Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914757404000256 |
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| author | Falahati, Ali Safavi, Mohammad Karim Elahi, Ardavan Pakdaman, Farhad Gabbouj, Moncef |
| author_facet | Falahati, Ali Safavi, Mohammad Karim Elahi, Ardavan Pakdaman, Farhad Gabbouj, Moncef |
| contents | Providing high-quality video with efficient bitrate is a main challenge in video industry. The traditional one-size-fits-all scheme for bitrate ladders is inefficient and reaching the best content-aware decision computationally impractical due to extensive encodings required. To mitigate this, we propose a bitrate and complexity efficient bitrate ladder prediction method using transfer learning and spatio-temporal features. We propose: (1) using feature maps from well-known pre-trained DNNs to predict rate-quality behavior with limited training data; and (2) improving highest quality rung efficiency by predicting minimum bitrate for top quality and using it for the top rung. The method tested on 102 video scenes demonstrates 94.1% reduction in complexity versus brute-force at 1.71% BD-Rate expense. Additionally, transfer learning was thoroughly studied through four networks and ablation studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03195 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features Falahati, Ali Safavi, Mohammad Karim Elahi, Ardavan Pakdaman, Farhad Gabbouj, Moncef Multimedia Computer Vision and Pattern Recognition Machine Learning I.4.2 Providing high-quality video with efficient bitrate is a main challenge in video industry. The traditional one-size-fits-all scheme for bitrate ladders is inefficient and reaching the best content-aware decision computationally impractical due to extensive encodings required. To mitigate this, we propose a bitrate and complexity efficient bitrate ladder prediction method using transfer learning and spatio-temporal features. We propose: (1) using feature maps from well-known pre-trained DNNs to predict rate-quality behavior with limited training data; and (2) improving highest quality rung efficiency by predicting minimum bitrate for top quality and using it for the top rung. The method tested on 102 video scenes demonstrates 94.1% reduction in complexity versus brute-force at 1.71% BD-Rate expense. Additionally, transfer learning was thoroughly studied through four networks and ablation studies. |
| title | Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features |
| topic | Multimedia Computer Vision and Pattern Recognition Machine Learning I.4.2 |
| url | https://arxiv.org/abs/2401.03195 |