Bitstream-based video quality modeling and analysis of HTTP-based adaptive streaming

Die Verbreitung erschwinglicher Videoaufnahmetechnologie und verbesserte Internetbandbreiten ermöglichen das Streaming von hochwertigen Videos (Auflösungen > 1080p, Bildwiederholraten ≥ 60fps) online. HTTP-basiertes adaptives Streaming ist die bevorzugte Methode zum Streamen von Videos, bei der Videoparameter an die verfügbare Bandbreite angepasst wird, was sich auf die Videoqualität auswirkt. Adaptives Streaming reduziert Videowiedergabeunterbrechnungen aufgrund geringer Netzwerkbandbreite, wirken sich jedoch auf die wahrgenommene Qualität aus, weswegen eine systematische Bewertung dieser notwendig ist. Diese Bewertung erfolgt üblicherweise für kurze Abschnitte von wenige Sekunden und während einer Sitzung (bis zu mehreren Minuten). Diese Arbeit untersucht beide Aspekte mithilfe perzeptiver und instrumenteller Methoden. Die perzeptive Bewertung der kurzfristigen Videoqualität umfasst eine Reihe von Labortests, die in frei verfügbaren Datensätzen publiziert wurden. Die Qualität von längeren Sitzungen wurde in Labortests mit menschlichen Betrachtern bewertet, die reale Betrachtungsszenarien simulieren. Die Methodik wurde zusätzlich außerhalb des Labors für die Bewertung der kurzfristigen Videoqualität und der Gesamtqualität untersucht, um alternative Ansätze für die perzeptive Qualitätsbewertung zu erforschen. Die instrumentelle Qualitätsevaluierung wurde anhand von bitstrom- und hybriden pixelbasierten Videoqualitätsmodellen durchgeführt, die im Zuge dieser Arbeit entwickelt wurden. Dazu wurde die Modellreihe AVQBits entwickelt, die auf den Labortestergebnissen basieren. Es wurden vier verschiedene Modellvarianten von AVQBits mit verschiedenen Inputinformationen erstellt: Mode 3, Mode 1, Mode 0 und Hybrid Mode 0. Die Modellvarianten wurden untersucht und schneiden besser oder gleichwertig zu anderen aktuellen Modellen ab. Diese Modelle wurden auch auf 360°- und Gaming-Videos, HFR-Inhalte und Bilder angewendet. Darüber hinaus wird ein Langzeitintegrationsmodell (1 - 5 Minuten) auf der Grundlage des ITU-T-P.1203.3-Modells präsentiert, das die verschiedenen Varianten von AVQBits mit sekündigen Qualitätswerten als Videoqualitätskomponente des vorgeschlagenen Langzeitintegrationsmodells verwendet. Alle AVQBits-Varianten, das Langzeitintegrationsmodul und die perzeptiven Testdaten wurden frei zugänglich gemacht, um weitere Forschung zu ermöglichen.The pervasion of affordable capture technology and increased internet bandwidth allows high-quality videos (resolutions > 1080p, framerates ≥ 60fps) to be streamed online. HTTP-based adaptive streaming is the preferred method for streaming videos, adjusting video quality based on available bandwidth. Although adaptive streaming reduces the occurrences of video playout being stopped (called “stalling”) due to narrow network bandwidth, the automatic adaptation has an impact on the quality perceived by the user, which results in the need to systematically assess the perceived quality. Such an evaluation is usually done on a short-term (few seconds) and overall session basis (up to several minutes). In this thesis, both these aspects are assessed using subjective and instrumental methods. The subjective assessment of short-term video quality consists of a series of lab-based video quality tests that have resulted in publicly available datasets. The overall integral quality was subjectively assessed in lab tests with human viewers mimicking a real-life viewing scenario. In addition to the lab tests, the out-of-the-lab test method was investigated for both short-term video quality and overall session quality assessment to explore the possibility of alternative approaches for subjective quality assessment. The instrumental method of quality evaluation was addressed in terms of bitstream- and hybrid pixel-based video quality models developed as part of this thesis. For this, a family of models, namely AVQBits has been conceived using the results of the lab tests as ground truth. Based on the available input information, four different instances of AVQBits, that is, a Mode 3, a Mode 1, a Mode 0, and a Hybrid Mode 0 model are presented. The model instances have been evaluated and they perform better or on par with other state-of-the-art models. These models have further been applied to 360° and gaming videos, HFR content, and images. Also, a long-term integration (1 - 5 mins) model based on the ITU-T P.1203.3 model is presented. In this work, the different instances of AVQBits with the per-1-sec scores output are employed as the video quality component of the proposed long-term integration model. All AVQBits variants as well as the long-term integration module and the subjective test data are made publicly available for further research

AVQBits-adaptive video quality model based on bitstream information for various video applications

The paper presents AVQBits, a versatile, bitstream-based video quality model. It can be applied in several contexts such as video service monitoring, evaluation of video encoding quality, of gaming video QoE, and even of omnidirectional video quality. In the paper, it is shown that AVQBits predictions closely match video quality ratings obained in various subjective tests with human viewers, for videos up to 4K-UHD resolution (Ultra-High Definition, 3840 x 2180 pixels) and framerates up 120 fps. With the different variants of AVQBits presented in the paper, video quality can be monitored either at the client side, in the network or directly after encoding. The no-reference AVQBits model was developed for different video services and types of input data, reflecting the increasing popularity of Video-on-Demand services and widespread use of HTTP-based adaptive streaming. At its core, AVQBits encompasses the standardized ITU-T P.1204.3 model, with further model instances that can either have restricted or extended input information, depending on the application context. Four different instances of AVQBits are presented, that is, a Mode 3 model with full access to the bitstream, a Mode 0 variant using only metadata such as codec type, framerate, resoution and bitrate as input, a Mode 1 model using Mode 0 information and frame-type and -size information, and a Hybrid Mode 0 model that is based on Mode 0 metadata and the decoded video pixel information. The models are trained on the authors’ own AVT-PNATS-UHD-1 dataset described in the paper. All models show a highly competitive performance by using AVT-VQDB-UHD-1 as validation dataset, e.g., with the Mode 0 variant yielding a value of 0.890 Pearson Correlation, the Mode 1 model of 0.901, the hybrid no-reference mode 0 model of 0.928 and the model with full bitstream access of 0.942. In addition, all four AVQBits variants are evaluated when applying them out-of-the-box to different media formats such as 360° video, high framerate (HFR) content, or gaming videos. The analysis shows that the ITU-T P.1204.3 and Hybrid Mode 0 instances of AVQBits for the considered use-cases either perform on par with or better than even state-of-the-art full reference, pixel-based models. Furthermore, it is shown that the proposed Mode 0 and Mode 1 variants outperform commonly used no-reference models for the different application scopes. Also, a long-term integration model based on the standardized ITU-T P.1203.3 is presented to estimate ratings of overall audiovisual streaming Quality of Experience (QoE) for sessions of 30 s up to 5 min duration. In the paper, the AVQBits instances with their per-1-sec score output are evaluated as the video quality component of the proposed long-term integration model. All AVQBits variants as well as the long-term integration module are made publicly available for the community for further research

Subjective quality evaluation of Point Clouds using remote testing

Subjective quality assessment serves as a method to evaluate the perceptual quality of 3D point clouds. These evaluations can be conducted using lab-based or remote or crowdsourcing tests. The lab-based tests are time-consuming and less cost-effective. As an alternative, remote or crowd tests can be used, offering a time and cost-friendly approach. Remote testing enables larger and more diverse participant pools. However, this raises the question of its applicability due to variability in participants' display devices and environments for the evaluation of the point cloud. In this paper, the focus is on investigating the applicability of remote testing by using the Absolute Category Rating (ACR) test method for assessing the subjective quality of point clouds in different tests. We compare the results of lab and remote tests by replicating lab-based tests. In the first test, we assess the subjective quality of a static point cloud geometry for two different types of geometrical degradations, namely Gaussian noise, and octree-pruning. In the second test, we compare the performance of two different compression methods (G-PCC and V-PCC) to assess the subjective quality of coloured point cloud videos. Based on the results obtained using correlation and Standard deviation of Opinion Scores (SOS) analysis, the remote testing paradigm can be used for evaluating point clouds

Strong impact of TGF-β1 gene polymorphisms on breast cancer risk in Indian women: a case-control and population-based study

Introduction: TGF-β1 is a multi-functional cytokine that plays an important role in breast carcinogenesis. Critical role of TGF-β1 signaling in breast cancer progression is well documented. Some TGF-β1 polymorphisms influence its expression; however, their impact on breast cancer risk is not clear. Methods: We analyzed 1222 samples in a candidate gene-based genetic association study on two distantly located and ethnically divergent case-control groups of Indian women, followed by a population-based genetic epidemiology study analyzing these polymorphisms in other Indian populations. The c.29C>T (Pro10Leu, rs1982073 or rs1800470) and c.74G>C (Arg25Pro, rs1800471) polymorphisms in the TGF-β1 gene were analyzed using direct DNA sequencing, and peripheral level of TGF-β1 were measured by ELISA. Results: c.29C>T substitution increased breast cancer risk, irrespective of ethnicity and menopausal status. On the other hand, c.74G>C substitution reduced breast cancer risk significantly in the north Indian group (p  =  0.0005) and only in the pre-menopausal women. The protective effect of c.74G>C polymorphism may be ethnicity-specific, as no association was seen in south Indian group. The polymorphic status of c.29C>T was comparable among Indo-Europeans, Dravidians and Tibeto-Burmans. Interestingly, we found that Tibeto-Burmans lack polymorphism at c.74G>C locus as true for the Chinese populations. However, the Brahmins of Nepal (Indo-Europeans) showed polymorphism in 2.08% of alleles. Mean TGF-β1 was significantly elevated in patients in comparison to controls (p<0.001). Conclusion: c.29C>T and c.74G>C polymorphisms in the TGF-β1 gene significantly affect breast cancer risk, which correlates with elevated TGF-β1 level in the patients. The c.29C>T locus is polymorphic across ethnically different populations, but c.74G>C locus is monomorphic in Tibeto-Burmans and polymorphic in other Indian populations

Age dependent associations of genotypes with confounding covariates in north Indian population by Cox regression analysis.

<p>The odds are of BMI “>23 kg/m², Diet “Non vegetarian”, Religion “Hindu”, Family history “Yes”, Personal habits “Yes”, Age at menarche “>14 yrs), Age at 1^st full term pregnancy “>19 yrs” and Menopausal status “Yes” against BMI “≤23 kg/m², Diet “Vegetarian”, Religion “Muslim”, Family history “No”, Personal habits “No”, Age at menarche “≤14 yrs), Age at 1^st full term pregnancy “≤19 yrs” and Menopausal status “No”.</p>*<p>Statistically significant (p<0.001).</p

Distribution of the two polymorphisms in Indian populations.

<p>Distribution of the two polymorphisms in Indian populations.</p

+29 C>T polymorphism allele and genotype data comparison between cases and controls.

<p>Allele and genotype frequency is followed by percent values in parenthesis.</p>*<p>Indicates statistical significant value at 95% level of confidence.</p