In recent years, end-to-end learnt video codecs have demonstrated their
potential to compete with conventional coding algorithms in term of compression
efficiency. However, most learning-based video compression models are
associated with high computational complexity and latency, in particular at the
decoder side, which limits their deployment in practical applications. In this
paper, we present a novel model-agnostic pruning scheme based on gradient decay
and adaptive layer-wise distillation. Gradient decay enhances parameter
exploration during sparsification whilst preventing runaway sparsity and is
superior to the standard Straight-Through Estimation. The adaptive layer-wise
distillation regulates the sparse training in various stages based on the
distortion of intermediate features. This stage-wise design efficiently updates
parameters with minimal computational overhead. The proposed approach has been
applied to three popular end-to-end learnt video codecs, FVC, DCVC, and
DCVC-HEM. Results confirm that our method yields up to 65% reduction in MACs
and 2x speed-up with less than 0.3dB drop in BD-PSNR. Supporting code and
supplementary material can be downloaded from:
this https URL

最近几年，端到端学习的视频编码器在压缩效率方面已经显示出与传统编码算法竞争的潜力。然而，大多数基于学习的视频压缩模型与高计算复杂性和延迟相关，特别是在解码器方面，这限制了它们在实际应用中的部署。本文提出了一种基于梯度衰减和自适应分层蒸馏的新型模型不可知剪枝方案。梯度衰减增强了稀疏化过程中的参数探索，同时防止过度稀疏，比标准的直通估计方法更优。自适应分层蒸馏根据中间特征的失真情况，在不同阶段有效地调整稀疏训练，以最小的计算开销更新参数。所提出的方法已应用于三种流行的端到端学习的视频编码器，即 FVC、DCVC 和 DCVC-HEM。结果表明，我们的方法在 MACS 上减少了高达 65％，速度提升了 2 倍，并且 BD-PSNR 下降不到 0.3dB。支持代码和补充材料可从此 https URL 下载。