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.

The article discusses the challenges faced in the video industry when it comes to providing high-quality video with efficient bitrate. The traditional approach of using a one-size-fits-all scheme for bitrate ladders is inefficient and computationally impractical. To address this issue, the authors propose a method that utilizes transfer learning and spatio-temporal features to predict the optimal bitrate ladder.

Transfer learning, a concept widely used in machine learning, is employed in this method by utilizing feature maps from pre-trained deep neural networks (DNNs) to predict the rate-quality behavior of videos. This approach allows for accurate predictions even with limited training data, reducing the computational complexity of encoding decisions.

In addition to transfer learning, the authors also propose improving the efficiency of the highest quality rung by predicting the minimum bitrate required for the top quality and using it as a reference for encoding. By doing so, the method achieves a significant reduction in complexity compared to traditional brute-force methods, while only incurring a minimal 1.71% BD-Rate expense.

This approach has several implications for the wider field of multimedia information systems. Firstly, it highlights the importance of considering the multi-disciplinary nature of video encoding, which combines concepts from computer vision, machine learning, and video compression. The use of pre-trained DNNs for feature extraction demonstrates how techniques from artificial intelligence can be leveraged to improve video quality.

Furthermore, this method is closely related to the fields of animations, augmented reality (AR), virtual reality (VR), and artificial reality (AR). These technologies heavily rely on high-quality video content to deliver immersive experiences. By optimizing the bitrate ladder, this method can improve the visual fidelity and streaming performance of multimedia content used in AR and VR applications.

In conclusion, the proposed method for efficient bitrate ladder prediction using transfer learning and spatio-temporal features is a significant advancement in the video industry. Its effectiveness in reducing complexity and its broader implications for multimedia information systems, animations, AR, VR, and artificial reality make it a valuable contribution to the field.

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