Abstract
Existing frameworks for learned video compression suffer from a dilemma between inaccurate temporal alignment and error propagation for motion estimation and compensation (ME/MC). The separate-transform framework employs distinct transforms for intra-frame and inter-frame compression to yield impressive rate-distortion (R-D) performance but causes evident error propagation, while the unified-transform framework eliminates error propagation via shared transforms but is inferior in ME/MC in shared latent domains. To address this limitation, in this paper, we propose a novel unifiedtransform framework with dual-domain progressive temporal alignment and quality-conditioned mixture-of-expert (QCMoE) to enable quality-consistent and error-propagation-free streaming for learned video compression. Specifically, we propose dualdomain progressive temporal alignment for ME/MC that leverages coarse pixel-domain alignment and refined latent-domain alignment to significantly enhance temporal context modeling in a coarse-to-fine fashion. The coarse pixel-domain alignment efficiently handles simple motion patterns with optical flow estimated from a single reference frame, while the refined latent-domain alignment develops a Flow-Guided Deformable Transformer (FGDT) over latents from multiple reference frames to achieve long-term motion refinement (LTMR) for complex motion patterns. Furthermore, we design a QCMoE module for continuous bit-rate adaptation that dynamically assigns different experts to adjust quantization steps per pixel based on target quality and content rather than relies on a single quantization step. QCMoE allows continuous and consistent rate control with appealing R-D performance. Experimental results show that the proposed method achieves competitive R-D performance compared with the state-of-the-arts, while successfully eliminating error propagation.
Abstract (translated)
现有的基于学习的视频压缩框架在时间对齐的准确性与运动估计和补偿（ME/MC）中的误差传播之间存在着一个难题。分离变换框架通过为帧内和帧间压缩使用不同的变换来获得出色的率失真（R-D）性能，但会导致明显的误差传播问题；统一变换框架则通过共享变换消除了误差传播的影响，但在共享潜在领域中对ME/MC的处理效果较差。 为了克服这一限制，在本文中我们提出了一种新的统一变换框架，该框架结合了双域渐进式时间对齐和基于质量条件的专家混合（QCMoE），从而能够在无误差传播的情况下实现学习视频压缩的质量一致性流媒体传输。具体来说，我们提出了针对ME/MC的双域渐进式时间对齐方法，这种方法利用粗略像素域对齐与精细潜在域对齐相结合的方式，以一种从粗到细的方法显著增强了时态上下文建模。 粗略像素域对齐能够高效地处理基于单一参考帧估算出光流的简单运动模式。而精细化潜在域对齐则通过在多参考帧的潜在值上发展流动引导可变形变换器（FGDT），实现了复杂运动模式下的长期运动细化（LTMR）。 此外，我们设计了一个QCMoE模块来实现连续比特率自适应调整，该模块根据目标质量和内容动态地分配不同的专家以调节每个像素的量化步骤，而不是依赖单一的量化步长。QCMoE模块允许进行连续且一致的速率控制，并取得令人满意的R-D性能。 实验结果表明，所提出的方法在与现有最佳方法相比时能够达到竞争性的率失真性能，同时成功地消除了误差传播问题。
URL
https://arxiv.org/abs/2512.10450
