Compressed Video Quality Index Based on Saliency-Aware Artifact Detection

Abstract

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.

First Gradually, Then Suddenly: Understanding the Impact of Image Compression on Object Detection Using Deep Learning

“…• Inclusion of more deep-learning-powered object detection models; • Expansion of the set of detection performance metrics (e.g., LRP [41], PDQ [42]); • Incorporation of image quality metrics (based on feature similarity, such as FSIM [43], or salience-aware artifact detection [44], among many others); • Consideration of related computer vision tasks such as instance segmentation; • Investigation of other compression algorithms, both transformative (e.g., JPEG2000 [45]) and generative/predictive (e.g., WebP [46], PDE-based methods [47]).…”

Video Saliency Object Detection with Motion Quality Compensation

“…Its main task is segmenting the most interesting target regions in videos and separating salient objects from their backgrounds in dynamic environments [1][2][3][4][5]. VSOD has been widely applied in many advanced computer vision tasks, such as video quality assessment [6], video compression [7], and face recognition [8].…”