Screen sharing is a common feature in video conferencing applications, especially for remote work and presentations. However, internet conditions such as limited bandwidth, packet loss, and compression can significantly reduce the visual quality of shared screen content. Most existing video quality metrics are designed for natural scenes and were not benchmarked on screen content. In this work, we present a large-scale subjective dataset of screen content videos captured from video conferencing apps. The dataset includes 1,600 distorted videos with corresponding subjective quality scores. Subjective scores were collected using crowdsourced pairwise comparisons. The dataset provides a valuable resource for developing and benchmarking video quality metrics tailored to screen content. The evaluation of objective metrics revealed that several general-purpose quality metrics outperform both full-reference and no-reference metrics. The dataset is available at the following link: https://videoprocessing.github.io/screen-content-dataset.

Banding, a common video quality artifact, it refers to the noticeable gradient steps or abrupt transitions between adjacent colors or shades within an image or video, often as a resulting from low color depth or compression algorithms. Assessing banded video quality is vital, especially with the rise of high-quality screens. Several banding metrics have been proposed in recent years to quantify and evaluate the presence of banding artifacts in videos. However, the visibility of banding strongly depends on viewing conditions such as screen brightness, contrast, ambient lighting, and others. This results in inconsistencies in the correlation between banding metrics and the subjective quality perceived across various screens. In this study, we conduct subjective experiments under varied viewing conditions and demonstrate the limited reliability of banding metrics.

In recent years, display intensity and contrast have increased considerably. Many displays support high dynamic range (HDR) and 10-bit color depth. Since high bit-depth is an emerging technology, video content is still largely shot and transmitted with a bit depth of 8 bits or less per color component. Insufficient bit-depths produce distortions called false contours or banding, and they are visible on high contrast screens. To deal with such distortions, researchers have proposed algorithms for bit-depth enhancement (dequantization). Such techniques convert videos with low bit-depth (LBD) to videos with high bit-depth (HBD). The quality of converted LBD video, however, is usually lower than that of the original HBD video, and many consumers prefer to keep the original HBD versions. In this paper, we propose an algorithm to determine whether a video has undergone conversion before compression. This problem is complex; it involves detecting outcomes of different dequantization algorithms in the presence of compression that strongly affects the least-significant bits (LSBs) in the video frames. Our algorithm can detect bit-depth enhancement and demonstrates good generalization capability, as it is able to determine whether a video has undergone processing by dequantization algorithms absent from the training dataset.