Advances in Quality Assessment Of Video Streaming Systems: Algorithms, Methods, Tools

Quality assessment of video has matured significantly in the last 10 years due to a flurry of relevant developments in academia and industry, with relevant initiatives in VQEG, AOMedia, MPEG, ITU-T P.910, and other standardization and advisory bodies . Most advanced video streaming systems are now clearly moving away from good old-fashioned' PSNR and structural similarity type of assessment towards metrics that align better to mean opinion scores from viewers. Several of these algorithms, methods and tools have only been developed in the last 3-5 years and, while they are of significant interest to the research community, their advantages and limitations are not widely known in the research community. This tutorial provides this overview, but also focuses on practical aspects and how to design quality assessment tests that can scale to large datasets

One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment

The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerated encoding, quality assessment is emerging as a significant bottleneck in video compression pipelines. Towards alleviating this burden, we propose a novel Fusion of Unified Quality Evaluators (FUNQUE) framework, by enabling computation sharing and by using a transform that is sensitive to visual perception to boost accuracy. Further, we expand the FUNQUE framework to define a collection of improved low-complexity fused-feature models that advance the state-of-the-art of video quality performance with respect to both accuracy, by 4.2\% to 5.3\%, and computational efficiency, by factors of 3.8 to 11 times!Comment: Version

Domain-Specific Fusion Of Objective Video Quality Metrics

Video processing algorithms like video upscaling, denoising, and compression are now increasingly optimized for perceptual quality metrics instead of signal distortion. This means that they may score well for metrics like video multi-method assessment fusion (VMAF), but this may be because of metric overfitting. This imposes the need for costly subjective quality assessments that cannot scale to large datasets and large parameter explorations. We propose a methodology that fuses multiple quality metrics based on small scale subjective testing in order to unlock their use at scale for specific application domains of interest. This is achieved by employing pseudo-random sampling of the resolution, quality range and test video content available, which is initially guided by quality metrics in order to cover the quality range useful to each application. The selected samples then undergo a subjective test, such as ITU-T P.910 absolute categorical rating, with the results of the test postprocessed and used as the means to derive the best combination of multiple objective metrics using support vector regression. We showcase the benefits of this approach in two applications: video encoding with and without perceptual preprocessing, and deep video denoising & upscaling of compressed content. For both applications, the derived fusion of metrics allows for a more robust alignment to mean opinion scores than a perceptually-uninformed combination of the original metrics themselves. The dataset and code is available at https://github.com/isize-tech/VideoQualityFusion

An End-to-End System for Organizing and Sharing Raw and Derived Mass Spectrometry Data

With the increasing amount of work being performed in the field of Mass Spectrometry (MS), a huge amount of data is being generated. This data needs to be properly managed, organized and shared among researchers at various institutions. The problem is further complicated by the different proprietary formats used by manufacturers of MS machines. We demonstrate an end-toend system to automate the process of converting the data to an open format, and to upload the data to a centralized server where it is easily organized and managed. The system allows scientists to browse, download, and use the data with third party tools. The user-view is simple and hides the underlying data-management system.