Video streaming

B

Estimating channel-induced distortion in H.264/AVC video without bitstream information

ABSTRACT No-reference video quality monitoring algorithms typically assume the availability of the encoded bitstream in order to assess the quality of the received signal at the decoder side. In some situations this is not possible, e.g. because the bitstream is encrypted or processed by third party decoders. Thus no-reference quality monitoring must be carried out in a blind way, i.e. using only pixel-domain data output by the decoder. In this paper we target this scenario for the specific case of distortion introduced by channel losses. We estimate the missing coding parameters, as well as the channel error pattern, and feed them into a no-reference quality monitoring system which produces accurate estimates of the MSE distortion. The results produced by the proposed method are well correlated (linear correlation coefficient larger than 0.8 over a wide range of packet loss rates) with the distortion computed in full-reference mode

Identificación de la fuente en vídeos de dispositivos móviles

El incesante aumento del número de dispositivos móviles con cámaras integradas ha originado que la mayoría de los vídeos digitales procedan de este tipo de dispositivos. Estos vídeos digitales pueden ser realizados en cualquier momento, en cualquier lugar y con diferentes fines, distribuyéndose en Internet en un corto período de tiempo y mostrando en ocasiones actos ilegales. La necesidad de establecer de forma fiable el origen se hace evidente cuando se utilizan estos vídeos en un contexto forense. En este trabajo se propone un algoritmo para identificar la marca y el modelo del dispositivo móvil que generó el vídeo. Su funcionamiento es como sigue: tras extraer la información relevante del vídeo, un algoritmo de clasificación, basado en el ruido del sensor y la Transformada Wavelet, realiza el proceso de identificación del dispositivo móvil

Perceptual Video Quality Assessment and Enhancement

With the rapid development of network visual communication technologies, digital video has become ubiquitous and indispensable in our everyday lives. Video acquisition, communication, and processing systems introduce various types of distortions, which may have major impact on perceived video quality by human observers. Effective and efficient objective video quality assessment (VQA) methods that can predict perceptual video quality are highly desirable in modern visual communication systems for performance evaluation, quality control and resource allocation purposes. Moreover, perceptual VQA measures may also be employed to optimize a wide variety of video processing algorithms and systems for best perceptual quality. This thesis exploits several novel ideas in the areas of video quality assessment and enhancement. Firstly, by considering a video signal as a 3D volume image, we propose a 3D structural similarity (SSIM) based full-reference (FR) VQA approach, which also incorporates local information content and local distortion-based pooling methods. Secondly, a reduced-reference (RR) VQA scheme is developed by tracing the evolvement of local phase structures over time in the complex wavelet domain. Furthermore, we propose a quality-aware video system which combines spatial and temporal quality measures with a robust video watermarking technique, such that RR-VQA can be performed without transmitting RR features via an ancillary lossless channel. Finally, a novel strategy for enhancing video denoising algorithms, namely poly-view fusion, is developed by examining a video sequence as a 3D volume image from multiple (front, side, top) views. This leads to significant and consistent gain in terms of both peak signal-to-noise ratio (PSNR) and SSIM performance, especially at high noise levels

Video Quality Prediction for Video over Wireless Access Networks (UMTS and WLAN)

Transmission of video content over wireless access networks (in particular, Wireless Local Area Networks (WLAN) and Third Generation Universal Mobile Telecommunication System (3G UMTS)) is growing exponentially and gaining popularity, and is predicted to expose new revenue streams for mobile network operators. However, the success of these video applications over wireless access networks very much depend on meeting the user’s Quality of Service (QoS) requirements. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet user’s QoS requirements. Video quality is affected by distortions caused by the encoder and the wireless access network. The impact of these distortions is content dependent, but this feature has not been widely used in existing video quality prediction models. The main aim of the project is the development of novel and efficient models for video quality prediction in a non-intrusive way for low bitrate and resolution videos and to demonstrate their application in QoS-driven adaptation schemes for mobile video streaming applications. This led to five main contributions of the thesis as follows:(1) A thorough understanding of the relationships between video quality, wireless access network (UMTS and WLAN) parameters (e.g. packet/block loss, mean burst length and link bandwidth), encoder parameters (e.g. sender bitrate, frame rate) and content type is provided. An understanding of the relationships and interactions between them and their impact on video quality is important as it provides a basis for the development of non-intrusive video quality prediction models.(2) A new content classification method was proposed based on statistical tools as content type was found to be the most important parameter. (3) Efficient regression-based and artificial neural network-based learning models were developed for video quality prediction over WLAN and UMTS access networks. The models are light weight (can be implemented in real time monitoring), provide a measure for user perceived quality, without time consuming subjective tests. The models have potential applications in several other areas, including QoS control and optimization in network planning and content provisioning for network/service providers.(4) The applications of the proposed regression-based models were investigated in (i) optimization of content provisioning and network resource utilization and (ii) A new fuzzy sender bitrate adaptation scheme was presented at the sender side over WLAN and UMTS access networks. (5) Finally, Internet-based subjective tests that captured distortions caused by the encoder and the wireless access network for different types of contents were designed. The database of subjective results has been made available to research community as there is a lack of subjective video quality assessment databases.Partially sponsored by EU FP7 ADAMANTIUM Project (EU Contract 214751

Video Content-Based QoE Prediction for HEVC Encoded Videos Delivered over IP Networks

The recently released High Efficiency Video Coding (HEVC) standard, which halves the transmission bandwidth requirement of encoded video for almost the same quality when compared to H.264/AVC, and the availability of increased network bandwidth (e.g. from 2 Mbps for 3G networks to almost 100 Mbps for 4G/LTE) have led to the proliferation of video streaming services. Based on these major innovations, the prevalence and diversity of video application are set to increase over the coming years. However, the popularity and success of current and future video applications will depend on the perceived quality of experience (QoE) of end users. How to measure or predict the QoE of delivered services becomes an important and inevitable task for both service and network providers. Video quality can be measured either subjectively or objectively. Subjective quality measurement is the most reliable method of determining the quality of multimedia applications because of its direct link to users’ experience. However, this approach is time consuming and expensive and hence the need for an objective method that can produce results that are comparable with those of subjective testing. In general, video quality is impacted by impairments caused by the encoder and the transmission network. However, videos encoded and transmitted over an error-prone network have different quality measurements even under the same encoder setting and network quality of service (NQoS). This indicates that, in addition to encoder settings and network impairment, there may be other key parameters that impact video quality. In this project, it is hypothesised that video content type is one of the key parameters that may impact the quality of streamed videos. Based on this assertion, parameters related to video content type are extracted and used to develop a single metric that quantifies the content type of different video sequences. The proposed content type metric is then used together with encoding parameter settings and NQoS to develop content-based video quality models that estimate the quality of different video sequences delivered over IP-based network. This project led to the following main contributions: (1) A new metric for quantifying video content type based on the spatiotemporal features extracted from the encoded bitstream. (2) The development of novel subjective test approach for video streaming services. (3) New content-based video quality prediction models for predicting the QoE of video sequences delivered over IP-based networks. The models have been evaluated using subjective and objective methods

Contribución a los modelos de estimación de la calidad percibida en servicios de vídeo sobre Internet mediante parámetros objetivos

En los últimos años el consumo de servicios de vídeo se ha incrementado de forma notable y se espera que dicha tendencia continúe en los próximos años. Los servicios de streaming de vídeo Over-The-Top (OTT), en los que se centra esta tesis, constituyen uno de los principales motores de dicho crecimiento. A diferencia de los servicios Internet Protocol Television (IPTV), que utilizan una red controlada en la que se pueden implementar mecanismos de Quality of Service (QoS), los servicios de streaming de vídeo OTT se prestan sobre Internet, por lo que llevan asociados interesantes desafíos desde un punto de vista técnico. Uno de los mayores desafíos técnicos a los que se enfrentan los servicios de streaming de vídeo OTT es mantener un nivel de Quality of Experience (QoE) que satisfaga a sus usuarios, por lo que es necesario contar con técnicas y herramientas que permitan monitorizar la calidad percibida por los usuarios de estos servicios. El streaming de vídeo OTT supone un cambio de filosofía en comparación con otras técnicas de streaming más tradicionales como RTP/RTSP. Los servicios de vídeo OTT suelen seguir el paradigma Dynamic Adaptive Streaming over HTTP (DASH), que se basa en sustituir los servidores de streaming tradicionales por servidores web que ponen a disposición de los clientes los contenidos de vídeo codificados en varias versiones con distinto nivel de calidad. Cada una de estas versiones o representaciones está dividida en pequeños fragmentos o segmentos que los clientes pueden solicitar mediante el protocolo HTTP. Los clientes pueden solicitar diferentes niveles de calidad en función de los parámetros que consideren más adecuados (ancho de banda de la red, resolución de pantalla, tipo de códec, etc.), lo que les permite adaptarse a condiciones cambiantes del entorno. Como se puede ver, el paradigma DASH ha trasladado el control de la sesión del servidor al cliente y ha sustituido los servidores de streaming por servidores web que simplemente sirven los segmentos de vídeo que los clientes solicitan. Además se esta simplificación de los servidores de streaming, existen otras ventajas asociadas a DASH, como son la utilización de Content Delivery Network (CDN), la compatibilidad con NATs y firewalls, etc. En esta tesis doctoral se lleva a cabo la propuesta de un conjunto de modelos cuyo objetivo es estimar la calidad percibida por los usuarios de los servicios de vídeo basados en DASH. Más concretamente, partiendo de la definición del servicio como un conjunto de componentes de servicio, se desarrollan modelos parciales que estiman la calidad percibida asociada a cada uno de estos componentes: calidad de vídeo, calidad de audio, degradaciones asociadas a la transmisión, etc. Cada una de estas estimaciones de calidad percibida se combinan en un modelo global que estima la calidad percibida total del servicio