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 https://chenfeng-bristol.github.io/RankDVQA-mini/ for public evaluation.

翻译：基于深度学习的视频质量评估（deep VQA）在超越传统指标方面展现出显著潜力，尤其在与人眼感知的相关性上取得了令人鼓舞的改进。然而，此类深度VQA模型的高计算复杂度和大内存需求往往限制了其实际部署。为解决这一问题，我们旨在通过采用一种结合剪枝驱动模型压缩与多级知识蒸馏的两阶段工作流，显著减小当前最先进的深度VQA方法之一RankDVQA的模型尺寸和运行时间。由此产生的轻量级质量评估指标RankDVQA-mini的模型参数仅为完整版本的不到10%（FLOPs层面为14%），同时其质量预测性能仍优于现有大多数深度VQA方法。RankDVQA-mini的源代码已在https://chenfeng-bristol.github.io/RankDVQA-mini/上发布，供公开评估。