Personalizing quality aspects for video communication in constrained heterogeneous environments

The world of multimedia communication is drastically evolving since a few years. Advanced compression formats for audiovisual information arise, new types of wired and wireless networks are developed, and a broad range of different types of devices capable of multimedia communication appear on the market. The era where multimedia applications available on the Internet were the exclusive domain of PC users has passed. The next generation multimedia applications will be characterized by heterogeneity: differences in terms of the networks, devices and user expectations. This heterogeneity causes some new challenges: transparent consumption of multimedia content is needed in order to be able to reach a broad audience. Recently, two important technologies have appeared that can assist in realizing such transparent Universal Multimedia Access. The first technology consists of new scalable or layered content representation schemes. Such schemes are needed in order to make it possible that a multimedia stream can be consumed by devices with different capabilities and transmitted over network connections with different characteristics. The second technology does not focus on the content representation itself, but rather on linking information about the content, so-called metadata, to the content itself. One of the possible uses of metadata is in the automatic selection and adaptation of multimedia presentations. This is one of the main goals of the MPEG-21 Multimedia Framework. Within the MPEG-21 standard, two formats were developed that can be used for bitstream descriptions. Such descriptions can act as an intermediate layer between a scalable bitstream and the adaptation process. This way, format-independent bitstream adaptation engines can be built. Furthermore, it is straightforward to add metadata information to the bitstream description, and use this information later on during the adaptation process. Because of the efforts spent on bitstream descriptions during our research, a lot of attention is devoted to this topic in this thesis. We describe both frameworks for bitstream descriptions that were standardized by MPEG. Furthermore, we focus on our own contributions in this domain: we developed a number of bitstream schemas and transformation examples for different types of multimedia content. The most important objective of this thesis is to describe a content negotiation process that uses scalable bitstreams in a generic way. In order to be able to express such an application, we felt the need for a better understanding of the data structures, in particular scalable bitstreams, on which this content negotiation process operates. Therefore, this thesis introduces a formal model we developed capable of describing the fundamental concepts of scalable bitstreams and their relations. Apart from the definition of the theoretical model itself, we demonstrate its correctness by applying it to a number of existing formats for scalable bitstreams. Furthermore, we attempt to formulate a content negotiation process as a constrained optimization problem, by means of the notations defined in the abstract model. In certain scenarios, the representation of a content negotiation process as a constrained optimization problem does not sufficiently reflect reality, especially when scalable bitstreams with multiple quality dimensions are involved. In such case, several versions of the same original bitstream can meet all constraints imposed by the system. Sometimes one version clearly offers a better quality towards the end user than another one, but in some cases, it is not possible to objectively compare two versions without additional information. In such a situation, a trade-off will have to be made between the different quality aspects. We use Pareto's theory of multi-criteria optimization for formally describing the characteristics of a content negotiation process for scalable bitstreams with multiple quality dimensions. This way, we can modify our definition of a content negotiation process into a multi-criteria optimization problem. One of the most important problems with multi-criteria optimization problems is that multiple candidate optimal solutions may exist. Additional information, e.g. user preferences, is needed if a single optimal solution has to be selected. Such multi-criteria optimization problems are not new. Unfortunately, existing solutions for selecting one optimal version are not suitable in a content negotiation scenario, because they expect detailed understanding of the problem from the decision maker, in our case the end user. In this thesis, we propose a scenario in which a so-called content negotiation agent would give some sample video sequences to the end user, asking him to select which sequence he liked the most. This information would be used for training the agent: a model would be built representing the preferences of the end user, and this model can be used later on for selecting one solution from a set of candidate optimal solutions. Based on a literature study, we propose two candidate algorithms in this thesis that can be used in such a content negotiation agent. It is possible to use these algorithms for constructing a model of the user's preferences by means of a number of examples, and to use this model when selecting an optimal version. The first algorithm considers the quality of a video sequence as a weighted sum of a number of independent quality aspects, and derives a system of linear inequalities from the example decisions. The second algorithm, called 1ARC, is actually a nearest-neighbor approach, where predictions are made based on the similarity with the example decisions entered by the user. This thesis analyzes the strengths and weaknesses of both algorithms from multiple points of view. The computational complexity of both algorithms is discussed, possible parameters that can influence the reliability of the algorithm, and the reliability itself. For measuring this kind of performance, we set up a test in which human subjects are asked to make a number of pairwise decisions between two versions of the same original video sequence. The reliability of the two algorithms we proposed is tested by selecting a part of these decisions for training a model, and by observing if this model is able to predict other decisions entered by the same user. We not only compare both algorithms, but we also observe the result of modifying several parameters on both algorithms. Ultimately, we conclude that the 1ARC algorithm has an acceptable performance, certainly if the training set is sufficiently large. The reliability is better than what would be theoretically achievable by any other algorithm that selects one optimal version from a set of candidate versions, but does not try to capture the user's preferences. Still, the results that we achieve are not as good as what we initially hoped. One possible cause may be the fact that the algorithms we proposed currently do not take sequence characteristics (e.g. the amount of motion) into account. Other improvements may be possible by means of a more accurate description of the quality aspects that we take into account, in particular the spatial resolution, the amount of distortion and the smoothness of a video sequence. Despite the limitations of the algorithms we proposed, in their performance as well as in their application area, we think that this thesis contains an initial and original contribution to the emerging objective of realizing Quality of Experience in multimedia applications

An Efficient Motion Estimation Method for H.264-Based Video Transcoding with Arbitrary Spatial Resolution Conversion

As wireless and wired network connectivity is rapidly expanding and the number of network users is steadily increasing, it has become more and more important to support universal access of multimedia content over the whole network. A big challenge, however, is the great diversity of network devices from full screen computers to small smart phones. This leads to research on transcoding, which involves in efficiently reformatting compressed data from its original high resolution to a desired spatial resolution supported by the displaying device. Particularly, there is a great momentum in the multimedia industry for H.264-based transcoding as H.264 has been widely employed as a mandatory player feature in applications ranging from television broadcast to video for mobile devices. While H.264 contains many new features for effective video coding with excellent rate distortion (RD) performance, a major issue for transcoding H.264 compressed video from one spatial resolution to another is the computational complexity. Specifically, it is the motion compensated prediction (MCP) part. MCP is the main contributor to the excellent RD performance of H.264 video compression, yet it is very time consuming. In general, a brute-force search is used to find the best motion vectors for MCP. In the scenario of transcoding, however, an immediate idea for improving the MCP efficiency for the re-encoding procedure is to utilize the motion vectors in the original compressed stream. Intuitively, motion in the high resolution scene is highly related to that in the down-scaled scene. In this thesis, we study homogeneous video transcoding from H.264 to H.264. Specifically, for the video transcoding with arbitrary spatial resolution conversion, we propose a motion vector estimation algorithm based on a multiple linear regression model, which systematically utilizes the motion information in the original scenes. We also propose a practical solution for efficiently determining a reference frame to take the advantage of the new feature of multiple references in H.264. The performance of the algorithm was assessed in an H.264 transcoder. Experimental results show that, as compared with a benchmark solution, the proposed method significantly reduces the transcoding complexity without degrading much the video quality

3D multiple description coding for error resilience over wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.Petroleum Technology Development Fund (PTDF

Dynamic adaptation of streamed real-time E-learning videos over the internet

Even though the e-learning is becoming increasingly popular in the academic environment, the quality of synchronous e-learning video is still substandard and significant work needs to be done to improve it. The improvements have to be brought about taking into considerations both: the network requirements and the psycho- physical aspects of the human visual system. One of the problems of the synchronous e-learning video is that the head-and-shoulder video of the instructor is mostly transmitted. This video presentation can be made more interesting by transmitting shots from different angles and zooms. Unfortunately, the transmission of such multi-shot videos will increase packet delay, jitter and other artifacts caused by frequent changes of the scenes. To some extent these problems may be reduced by controlled reduction of the quality of video so as to minimise uncontrolled corruption of the stream. Hence, there is a need for controlled streaming of a multi-shot e-learning video in response to the changing availability of the bandwidth, while utilising the available bandwidth to the maximum. The quality of transmitted video can be improved by removing the redundant background data and utilising the available bandwidth for sending high-resolution foreground information. While a number of schemes exist to identify and remove the background from the foreground, very few studies exist on the identification and separation of the two based on the understanding of the human visual system. Research has been carried out to define foreground and background in the context of e-learning video on the basis of human psychology. The results have been utilised to propose methods for improving the transmission of e-learning videos. In order to transmit the video sequence efficiently this research proposes the use of Feed- Forward Controllers that dynamically characterise the ongoing scene and adjust the streaming of video based on the availability of the bandwidth. In order to satisfy a number of receivers connected by varied bandwidth links in a heterogeneous environment, the use of Multi-Layer Feed-Forward Controller has been researched. This controller dynamically characterises the complexity (number of Macroblocks per frame) of the ongoing video sequence and combines it with the knowledge of availability of the bandwidth to various receivers to divide the video sequence into layers in an optimal way before transmitting it into network. The Single-layer Feed-Forward Controller inputs the complexity (Spatial Information and Temporal Information) of the on-going video sequence along with the availability of bandwidth to a receiver and adjusts the resolution and frame rate of individual scenes to transmit the sequence optimised to give the most acceptable perceptual quality within the bandwidth constraints. The performance of the Feed-Forward Controllers have been evaluated under simulated conditions and have been found to effectively regulate the streaming of real-time e-learning videos in order to provide perceptually improved video quality within the constraints of the available bandwidth

Packet prioritizing and delivering for multimedia streaming

Ph.DDOCTOR OF PHILOSOPH

A Framework for pervasive web content delivery

Ph.DDOCTOR OF PHILOSOPH

Click Fraud Detection in Online and In-app Advertisements: A Learning Based Approach

Click Fraud is the fraudulent act of clicking on pay-per-click advertisements to increase a site’s revenue, to drain revenue from the advertiser, or to inflate the popularity of content on social media platforms. In-app advertisements on mobile platforms are among the most common targets for click fraud, which makes companies hesitant to advertise their products. Fraudulent clicks are supposed to be caught by ad providers as part of their service to advertisers, which is commonly done using machine learning methods. However: (1) there is a lack of research in current literature addressing and evaluating the different techniques of click fraud detection and prevention, (2) threat models composed of active learning systems (smart attackers) can mislead the training process of the fraud detection model by polluting the training data, (3) current deep learning models have significant computational overhead, (4) training data is often in an imbalanced state, and balancing it still results in noisy data that can train the classifier incorrectly, and (5) datasets with high dimensionality cause increased computational overhead and decreased classifier correctness -- while existing feature selection techniques address this issue, they have their own performance limitations. By extending the state-of-the-art techniques in the field of machine learning, this dissertation provides the following solutions: (i) To address (1) and (2), we propose a hybrid deep-learning-based model which consists of an artificial neural network, auto-encoder and semi-supervised generative adversarial network. (ii) As a solution for (3), we present Cascaded Forest and Extreme Gradient Boosting with less hyperparameter tuning. (iii) To overcome (4), we propose a row-wise data reduction method, KSMOTE, which filters out noisy data samples both in the raw data and the synthetically generated samples. (iv) For (5), we propose different column-reduction methods such as multi-time-scale Time Series analysis for fraud forecasting, using binary labeled imbalanced datasets and hybrid filter-wrapper feature selection approaches

3D multiple description coding for error resilience over wireless networks

Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.EThOS - Electronic Theses Online ServicePetroleum Technology Development Fund (PTDF)GBUnited Kingdo

Motion Scalability for Video Coding with Flexible Spatio-Temporal Decompositions

PhDThe research presented in this thesis aims to extend the scalability range of the wavelet-based video coding systems in order to achieve fully scalable coding with a wide range of available decoding points. Since the temporal redundancy regularly comprises the main portion of the global video sequence redundancy, the techniques that can be generally termed motion decorrelation techniques have a central role in the overall compression performance. For this reason the scalable motion modelling and coding are of utmost importance, and specifically, in this thesis possible solutions are identified and analysed. The main contributions of the presented research are grouped into two interrelated and complementary topics. Firstly a flexible motion model with rateoptimised estimation technique is introduced. The proposed motion model is based on tree structures and allows high adaptability needed for layered motion coding. The flexible structure for motion compensation allows for optimisation at different stages of the adaptive spatio-temporal decomposition, which is crucial for scalable coding that targets decoding on different resolutions. By utilising an adaptive choice of wavelet filterbank, the model enables high compression based on efficient mode selection. Secondly, solutions for scalable motion modelling and coding are developed. These solutions are based on precision limiting of motion vectors and creation of a layered motion structure that describes hierarchically coded motion. The solution based on precision limiting relies on layered bit-plane coding of motion vector values. The second solution builds on recently established techniques that impose scalability on a motion structure. The new approach is based on two major improvements: the evaluation of distortion in temporal Subbands and motion search in temporal subbands that finds the optimal motion vectors for layered motion structure. Exhaustive tests on the rate-distortion performance in demanding scalable video coding scenarios show benefits of application of both developed flexible motion model and various solutions for scalable motion coding