In this article, we explore efficient video encoding methods that balance quality and speed. Encoding videos at multiple bitrates can significantly reduce the time required for encoding while maintaining high quality. Existing methods typically use a single reference representation and leverage its information to accelerate the encoding process for lower-quality representations. However, this approach can lead to suboptimal trade-offs between compression efficiency and encoding time savings.
To address these limitations, we propose an improved multi-rate encoding approach that utilizes co-located Convolutional Neural Networks (CNNs) to extract additional features from the reference representation and enhance partitioning depth decisions. By leveraging these features, we can significantly reduce the time required for encoding while maintaining high quality.
Our proposed method consists of three main stages
- Reference Encoding: In this stage, we encode the video at a high quality representation using a state-of-the-art encoder. This produces a detailed representation that captures the most important features of the video.
- Dependent Representations Encoding: Next, we use the information from the reference encoding to accelerate the encoding process for lower-quality representations. We treat each Convolutional Neural Network (CNN) in the reference encoding as a "super-feature extractor," which provides a rich set of features that can be used to enhance the partitioning depth decisions for the dependent representations.
- Partitioning and Encoding: In this final stage, we apply coarse-to-fine partitioning to the reference representation, starting with the highest quality CTUs (Compression Takfiyat User) and progressively increasing the partitioning depth until the desired level of detail is achieved. We then use these partitioned representations to guide the encoding process for the dependent representations, which are encoded using a combination of Intra- and Inter-frame coding techniques.
By combining these stages, we achieve significant time savings while maintaining high quality. Our proposed approach can reduce the encoding time by up to 50% without sacrificing compression efficiency. This is because we use the rich features extracted from the reference representation to guide the encoding process for lower-quality representations, which reduces the amount of information that needs to be encoded.
In conclusion, our proposed method offers a comprehensive solution for fast multi-rate video encoding that balances quality and speed. By leveraging co-located CNNs to extract additional features from the reference representation and guiding the encoding process for lower-quality representations, we can significantly reduce the time required for encoding while maintaining high quality. This approach has important implications for streaming applications where fast and efficient video encoding is critical.
