Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features

• URL: http://arxiv.org/abs/2401.03195v2
• Date: Thu, 14 Mar 2024 03:59:19 GMT
• Title: Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features
• Authors: Ali Falahati, Mohammad Karim Safavi, Ardavan Elahi, Farhad Pakdaman, Moncef Gabbouj,
• Abstract summary: We propose an efficient ladder prediction method using transfer and learning features. The method tested on 102 video scenes demonstrates 94.1% reduction in complexity versus brute-force at 1.71% BD-Rate expense.
• Score: 12.631821085716853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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