利用梯度衰减与逐层蒸馏加速学习型视频编解码器
Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation
AI总结：
本文提出基于梯度衰减与自适应逐层蒸馏的模型无关剪枝方案，应用于FVC、DCVC和DCVC-HEM三种学习型视频编解码器，在BD-PSNR下降小于0.3dB的情况下实现最高65%的MACs减少和2倍加速。
摘要：
近年来，端到端学习型视频编解码器已展现出在压缩效率方面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都伴随着较高的计算复杂度和延迟，尤其是在解码端，这限制了它们在实际应用中的部署。在本文中，我们提出了一种新颖的模型无关剪枝方案，该方案基于梯度衰减和自适应逐层蒸馏。梯度衰减在稀疏化过程中增强了参数探索，同时防止稀疏度失控，并且优于标准的直通估计（Straight-Through Estimation）。自适应逐层蒸馏根据中间特征的失真情况在不同阶段调节稀疏训练。这种分阶段设计以最小的计算开销高效地更新参数。所提出的方法已应用于三种流行的端到端学习型视频编解码器：FVC、DCVC和DCVC-HEM。结果证实，我们的方法在BD-PSNR下降不到0.3dB的情况下，实现了高达65%的MACs减少和2倍加速。支持代码和补充材料可从以下网址下载：https://jasminepp.github.io/lightweightdvc/
英文摘要：
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: https://jasminepp.github.io/lightweightdvc/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
