Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350002, China.
Sensors (Basel). 2021 Sep 26;21(19):6429. doi: 10.3390/s21196429.
Video coding technology makes the required storage and transmission bandwidth of video services decrease by reducing the bitrate of the video stream. However, the compressed video signals may involve perceivable information loss, especially when the video is overcompressed. In such cases, the viewers can observe visually annoying artifacts, namely, Perceivable Encoding Artifacts (PEAs), which degrade their perceived video quality. To monitor and measure these PEAs (including blurring, blocking, ringing and color bleeding), we propose an objective video quality metric named Saliency-Aware Artifact Measurement (SAAM) without any reference information. The SAAM metric first introduces video saliency detection to extract interested regions and further splits these regions into a finite number of image patches. For each image patch, the data-driven model is utilized to evaluate intensities of PEAs. Finally, these intensities are fused into an overall metric using Support Vector Regression (SVR). In experiment section, we compared the SAAM metric with other popular video quality metrics on four publicly available databases: LIVE, CSIQ, IVP and FERIT-RTRK. The results reveal the promising quality prediction performance of the SAAM metric, which is superior to most of the popular compressed video quality evaluation models.
视频编码技术通过降低视频流的比特率来减少视频服务所需的存储和传输带宽。然而，压缩后的视频信号可能会涉及到可感知的信息丢失，特别是当视频被过度压缩时。在这种情况下，观众可以观察到视觉上令人讨厌的伪影，即可感知的编码伪影（PEAs），这会降低他们感知到的视频质量。为了监测和测量这些 PEA（包括模糊、块、振铃和颜色渗透），我们提出了一种无需任何参考信息的基于显着性感知的伪影测量（SAAM）客观视频质量度量。SAAM 度量首先引入视频显着性检测来提取感兴趣的区域，并进一步将这些区域划分为有限数量的图像补丁。对于每个图像补丁，使用数据驱动的模型来评估 PEA 的强度。最后，使用支持向量回归（SVR）将这些强度融合为一个整体度量。在实验部分，我们将 SAAM 度量与其他流行的视频质量度量标准在四个公开可用的数据库上进行了比较：LIVE、CSIQ、IVP 和 FERIT-RTRK。结果表明，SAAM 度量具有有前途的质量预测性能，优于大多数流行的压缩视频质量评估模型。
