ReLaX-VQA：用于增强视频质量评估的残差片段与层级堆叠提取方法
ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
AI总结：
本文提出无参考视频质量评估模型ReLaX-VQA，通过帧差选择时空片段并结合残差网络与视觉Transformer的层级堆叠特征，在四个UGC数据集上取得领先性能。
摘要：
随着用户与分享平台之间交换的用户生成内容（UGC）快速增长，对真实场景下视频质量评估的需求日益明显。UGC通常由消费级设备采集，并在到达最终用户之前经历多轮压缩（转码）。因此，采用原始内容作为参考的传统质量指标并不适用。在本文中，我们提出ReLaX-VQA，一种新颖的无参考视频质量评估（NR-VQA）模型，旨在解决在不参考原始未压缩视频的情况下评估多样化视频内容质量的挑战。ReLaX-VQA利用帧差智能地选择时空片段，并结合与采样帧相关的不同空间特征表达。这些特征随后被用于更好地捕捉相邻帧质量中的空间和时间变异性。此外，该模型通过在来自残差网络（Residual Networks）和视觉Transformer（Vision Transformers）的深度神经网络特征中采用层级堆叠技术来增强抽象能力。在四个UGC数据集上的广泛测试表明，ReLaX-VQA持续优于现有的NR-VQA方法，平均SRCC达到0.8658，PLCC达到0.8873。开源代码和训练好的模型可在https://github.com/xinyiW915/ReLaX-VQA获取，这将促进NR-VQA的进一步研究和应用。
英文摘要：
With the rapid growth of User-Generated Content (UGC) exchanged between users and sharing platforms, the need for video quality assessment in the wild is increasingly evident. UGC is typically acquired using consumer devices and undergoes multiple rounds of compression (transcoding) before reaching the end user. Therefore, traditional quality metrics that employ the original content as a reference are not suitable. In this paper, we propose ReLaX-VQA, a novel No-Reference Video Quality Assessment (NR-VQA) model that aims to address the challenges of evaluating the quality of diverse video content without reference to the original uncompressed videos. ReLaX-VQA uses frame differences to select spatio-temporal fragments intelligently together with different expressions of spatial features associated with the sampled frames. These are then used to better capture spatial and temporal variabilities in the quality of neighbouring frames. Furthermore, the model enhances abstraction by employing layer-stacking techniques in deep neural network features from Residual Networks and Vision Transformers. Extensive testing across four UGC datasets demonstrates that ReLaX-VQA consistently outperforms existing NR-VQA methods, achieving an average SRCC of 0.8658 and PLCC of 0.8873. Open-source code and trained models that will facilitate further research and applications of NR-VQA can be found at https://github.com/xinyiW915/ReLaX-VQA.
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。
