Deep learning-based video quality assessment (deep VQA) has demonstrated
significant potential in surpassing conventional metrics, with promising
improvements in terms of correlation with human perception. However, the
practical deployment of such deep VQA models is often limited due to their high
computational complexity and large memory requirements. To address this issue,
we aim to significantly reduce the model size and runtime of one of the
state-of-the-art deep VQA methods, RankDVQA, by employing a two-phase workflow
that integrates pruning-driven model compression with multi-level knowledge
distillation. The resulting lightweight quality metric, RankDVQA-mini, requires
less than 10% of the model parameters compared to its full version (14% in
terms of FLOPs), while still retaining a quality prediction performance that is
superior to most existing deep VQA methods. The source code of the
RankDVQA-mini has been released at
this https URL for public evaluation.

通过使用剪枝驱动的模型压缩和多层级知识蒸馏的两阶段工作流程，我们成功地显著减小了排名型深度视频质量评估方法 RankDVQA 的模型大小和运行时间，得到了一个轻量级的质量度量指标 RankDVQA-mini，其参数量不到原始版本的 10%（FLOPs 为原版本的 14%），但在质量预测性能上超过了大部分现有的深度视频质量评估方法。