QoS monitoring in real-time streaming overlays based on lock-free data structures

Peer-to-peer streaming is a well-known technology for the large-scale distribution of real-time audio/video contents. Delay requirements are very strict in interactive real-time scenarios (such as synchronous distance learning), where playback lag should be of the order of seconds. Playback continuity is another key aspect in these cases: in presence of peer churning and network congestion, a peer-to-peer overlay should quickly rearrange connections among receiving nodes to avoid freezing phenomena that may compromise audio/video understanding. For this reason, we designed a QoS monitoring algorithm that quickly detects broken or congested links: each receiving node is able to independently decide whether it should switch to a secondary sending node, called “fallback node”. The architecture takes advantage of a multithreaded design based on lock-free data structures, which improve the performance by avoiding synchronization among threads. We will show the good responsiveness of the proposed approach on machines with different computational capabilities: measured times prove both departures of nodes and QoS degradations are promptly detected and clients can quickly restore a stream reception. According to PSNR and SSIM, two well-known full-reference video quality metrics, QoE remains acceptable on receiving nodes of our resilient overlay also in presence of swap procedures.

Modeling and estimating the subjects’ diversity of opinions in video quality assessment: a neural network based approach

Subjective experiments are considered the most reliable way to assess the perceived visual quality. However, observers’ opinions are characterized by large diversity: in fact, even the same observer is often not able to exactly repeat his first opinion when rating again a given stimulus. This makes the Mean Opinion Score (MOS) alone, in many cases, not sufficient to get accurate information about the perceived visual quality. To this aim, it is important to have a measure characterizing to what extent the observed or predicted MOS value is reliable and stable. For instance, the Standard deviation of the Opinions of the Subjects (SOS) could be considered as a measure of reliability when evaluating the quality subjectively. However, we are not aware of the existence of models or algorithms that allow to objectively predict how much diversity would be observed in subjects’ opinions in terms of SOS. In this work we observe, on the basis of a statistical analysis made on several subjective experiments, that the disagreement between the quality as measured by means of different objective video quality metrics (VQMs) can provide information on the diversity of the observers’ ratings on a given processed video sequence (PVS). In light of this observation we: i) propose and validate a model for the SOS observed in a subjective experiment; ii) design and train Neural Networks (NNs) that predict the average diversity that would be observed among the subjects’ ratings for a PVS starting from a set of VQMs values computed on such a PVS; iii) give insights into how the same NN based approach can be used to identify potential anomalies in the data collected in subjective experiments.

Investigating Prediction Accuracy of Full Reference Objective Video Quality Measures through the ITS4S Dataset

Large subjectively annotated datasets are crucial to the development and testing of objective video quality measures (VQMs). In this work we focus on the recently released ITS4S dataset. Relying on statistical tools, we show that the content of the dataset is rather heterogeneous from the point of view of quality assessment. Such diversity naturally makes the dataset a worthy asset to validate the accuracy of video quality metrics (VQMs). In particular we study the ability of VQMs to model the reduction or the increase of the visibility of distortion due to the spatial activity in the content. The study reveals that VQMs are likely to overestimate the perceived quality of processed video sequences whose source is characterized by few spatial details. We then propose an approach aiming at modeling the impact of spatial activity on distortion visibility when objectively assessing the visual quality of a content. The effectiveness of the proposal is validated on the ITS4S dataset as well as on the Netflix public dataset.

Analyzing the performance of autoencoder-based objective quality metrics on audio-visual content

The development of audio-visual quality models faces a number of challenges, including the integration of audio and video sensory channels and the modeling of their interaction characteristics. Commonly, objective quality metrics estimate the quality of a single component (audio or video) of the content. Machine learning techniques, such as autoencoders, offer as a very promising alternative to develop objective assessment models. This paper studies the performance of a group of autoencoder-based objective quality metrics on a diverse set of audio-visual content. To perform this test, we use a large dataset of audio-visual content (The UnB-AV database), which contains degradations in both audio and video components. The database has accompanying subjective scores collected on three separate subjective experiments. We compare our autoencoder-based methods, which take into account both audio and video components (multi-modal), against several objective (single-modal) audio and video quality metrics. The main goal of this work is to verify the gain or loss in performance of these single-modal metrics, when tested on audio-visual sequences.