Providing adequate Quality of Experience (QoE) to end-users is crucial for streaming service providers. In this paper, in order to realize automatic quality assessment, a No-Reference (NR) bitstream Human-Vision-System-(HVS)-based video quality assessment (VQA) model is proposed. Inspired by discoveries from the neuroscience community, which suggest there is a considerable overlap between active areas of the brain when engaging in video quality assessment and saliency detection tasks, saliency maps are used in the proposed method to improve the quality assessment accuracy. To this end, saliency maps are first generated from features extracted from the HEVC bitstream. Then, saliency map statistics are employed to create a model of visual memory. Finally, a support vector regression pipeline learns an estimate of the video quality from the visual memory, saliency, and frame features. Evaluations on SJTU dataset indicate that the proposed bitstream based no-reference video quality assessment algorithm achieves a competitive performance.

为最终用户提供足够的体验质量（QoE）对于流服务提供商至关重要。为了实现自动质量评估，提出了一种基于无参考（NR）比特流的人类视觉系统（HVS）视频质量评估（VQA）模型。受神经科学界的发现启发，这些发现表明，在进行视频质量评估和显着性检测任务时，大脑活动区域之间存在相当大的重叠，所提出的方法中使用了显着图来提高质量评估的准确性。为此，首先根据从HEVC比特流中提取的特征生成显着图。然后，采用显着性地图统计信息来创建视觉记忆模型。最后，支持向量回归管线从视觉内存，显着性和帧特征中学习视频质量的估计。对SJTU数据集的评估表明，所提出的基于比特流的无参考视频质量评估算法可实现竞争性能。