With recent advances in deep learning, numerous algorithms have been developed to enhance video quality, reduce visual artefacts and improve perceptual quality. However, little research has been reported on the quality assessment of enhanced content - the evaluation of enhancement methods is often based on quality metrics that were designed for compression applications. In this paper, we propose a novel blind deep video quality assessment (VQA) method specifically for enhanced video content. It employs a new Recurrent Memory Transformer (RMT) based network architecture to obtain video quality representations, which is optimised through a novel content-quality-aware contrastive learning strategy based on a new database containing 13K training patches with enhanced content. The extracted quality representations are then combined through linear regression to generate video-level quality indices. The proposed method, RMT-BVQA, has been evaluated on the VDPVE (VQA Dataset for Perceptual Video Enhancement) database through a five-fold cross validation. The results show its superior correlation performance when compared to ten existing no-reference quality metrics.

翻译：随着深度学习的近期进展，大量算法被开发用于提升视频质量、减少视觉伪影并改善感知质量。然而，关于增强内容质量评估的研究报道甚少——增强方法的评价通常基于专为压缩应用设计的质量指标。本文提出一种新颖的盲深度视频质量评估（VQA）方法，专门针对增强视频内容。该方法采用基于新型循环记忆Transformer（RMT）的网络架构获取视频质量表征，并通过基于包含1.3万增强内容训练补丁的新型数据库的内容-质量感知对比学习策略进行优化。提取的质量表征随后通过线性回归整合生成视频级质量指数。所提出的RMT-BVQA方法在VDPVE（感知视频增强质量评估数据集）数据库上通过五折交叉验证进行了评估。与十种现有无参考质量指标相比，实验结果表明其具有更优的相关性性能。