Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation

• URL: http://arxiv.org/abs/2312.02605v1
• Date: Tue, 5 Dec 2023 09:26:09 GMT
• Title: Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation
• Authors: Tianhao Peng, Ge Gao, Heming Sun, Fan Zhang and David Bull
• Abstract summary: We present a novel model-agnostic pruning scheme based on gradient decay and adaptive layer-wise distillation. 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.
• Score: 17.980800481385195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/

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