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Learned Wavelet Video Coding Using Motion Compensated Temporal Filtering
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DOI：10.1109/ACCESS.2023.3323873.png)
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Abstract
En 中文
This paper presents an end-to-end trainable wavelet video coder based on motion-compensated temporal filtering. Thereby, it introduces a different coding scheme for learned video compression, which is dominated by residual and conditional coding approaches. By performing discrete wavelet transforms in temporal, horizontal, and vertical dimensions, an explainable framework with spatial and temporal scalability is obtained. This paper investigates a novel trainable motion-compensated temporal filtering module implemented using the lifting scheme. It demonstrates how multiple temporal decomposition levels can be considered during training. Furthermore, larger temporal displacements owing to the coding order are addressed and an extension adapting to different motion strengths during inference is introduced. The experimental analysis compares the proposed approach to learning-based coders and traditional hybrid video coding. Especially at high rates, the approach exhibits promising rate-distortion performance. The proposed method achieves average Bjontegaard Delta savings of up to 21% over HEVC, and outperforms state-of-the-art learned video coders.
Keywords:
Convolutional neural networks
deep learning
discrete wavelet transforms
motion compensation
motion estimation
scalability
video codecs
video coding
video compression
video signal processing