Improved Predictive Coding for Animation-Based Video Compression

This paper addresses the limitations of generative face video compression (GFVC) under conditions of substantial head movement and complex facial deformations. Previous GFVC frameworks focused on perceptual compression and reconstruct videos only with the goal of perceptual quality. As a result, they often have a large disparity relative to conventional codecs when evaluated for pixel fidelity. We propose a robust framework for learned predictive coding process aiming for both perceptual quality and improved performance in terms of pixel fidelity under low bitrate conditions. Our method proposes a dual residual learning strategy. Specifically, it learns the frame residual between the animated frame and the ground truth i.e. spatial residual coding and further exploits redundancies between neighboring frame residuals i.e temporal residual coding. We specially formulate a low bitrate conditional residual coding mechanisms for both spatial and temporal residual coding. In addition, we propose a zero-cost residual alignment mechanism to refine prediction accuracy of frame residuals. Through end-to-end optimization, the proposed framework achieves a balance between perceptual quality, pixel fidelity and compression efficiency. We conduct experimental evaluations on test sequences and conditions proposed under the JVET-AH0114 standard to show significant performance gains relative to HEVC and VVC standards in terms of perceptual metrics. Compared to other GFVC frameworks, our proposed framework achieves state of the art performance on perceptual metrics and pixel fidelity metrics. It is also competitive with HDAC, HEVC and VVC in terms of pixel fidelity at low bitrates. Source Code is publicly available at https://github.com/Goluck-Konuko/animation-based-codecs