Scalable video compression with optimized visual performance and random accessibility

This thesis is concerned with maximizing the coding efficiency, random accessibility and visual performance of scalable compressed video. The unifying theme behind this work is the use of finely embedded localized coding structures, which govern the extent to which these goals may be jointly achieved. The first part focuses on scalable volumetric image compression. We investigate 3D transform and coding techniques which exploit inter-slice statistical redundancies without compromising slice accessibility. Our study shows that the motion-compensated temporal discrete wavelet transform (MC-TDWT) practically achieves an upper bound to the compression efficiency of slice transforms. From a video coding perspective, we find that most of the coding gain is attributed to offsetting the learning penalty in adaptive arithmetic coding through 3D code-block extension, rather than inter-frame context modelling. The second aspect of this thesis examines random accessibility. Accessibility refers to the ease with which a region of interest is accessed (subband samples needed for reconstruction are retrieved) from a compressed video bitstream, subject to spatiotemporal code-block constraints. We investigate the fundamental implications of motion compensation for random access efficiency and the compression performance of scalable interactive video. We demonstrate that inclusion of motion compensation operators within the lifting steps of a temporal subband transform incurs a random access penalty which depends on the characteristics of the motion field. The final aspect of this thesis aims to minimize the perceptual impact of visible distortion in scalable reconstructed video. We present a visual optimization strategy based on distortion scaling which raises the distortion-length slope of perceptually significant samples. This alters the codestream embedding order during post-compression rate-distortion optimization, thus allowing visually sensitive sites to be encoded with higher fidelity at a given bit-rate. For visual sensitivity analysis, we propose a contrast perception model that incorporates an adaptive masking slope. This versatile feature provides a context which models perceptual significance. It enables scene structures that otherwise suffer significant degradation to be preserved at lower bit-rates. The novelty in our approach derives from a set of "perceptual mappings" which account for quantization noise shaping effects induced by motion-compensated temporal synthesis. The proposed technique reduces wavelet compression artefacts and improves the perceptual quality of video

Quality of service differentiation for multimedia delivery in wireless LANs

Delivering multimedia content to heterogeneous devices over a variable networking environment while maintaining high quality levels involves many technical challenges. The research reported in this thesis presents a solution for Quality of Service (QoS)-based service differentiation when delivering multimedia content over the wireless LANs. This thesis has three major contributions outlined below: 1. A Model-based Bandwidth Estimation algorithm (MBE), which estimates the available bandwidth based on novel TCP and UDP throughput models over IEEE 802.11 WLANs. MBE has been modelled, implemented, and tested through simulations and real life testing. In comparison with other bandwidth estimation techniques, MBE shows better performance in terms of error rate, overhead, and loss. 2. An intelligent Prioritized Adaptive Scheme (iPAS), which provides QoS service differentiation for multimedia delivery in wireless networks. iPAS assigns dynamic priorities to various streams and determines their bandwidth share by employing a probabilistic approach-which makes use of stereotypes. The total bandwidth to be allocated is estimated using MBE. The priority level of individual stream is variable and dependent on stream-related characteristics and delivery QoS parameters. iPAS can be deployed seamlessly over the original IEEE 802.11 protocols and can be included in the IEEE 802.21 framework in order to optimize the control signal communication. iPAS has been modelled, implemented, and evaluated via simulations. The results demonstrate that iPAS achieves better performance than the equal channel access mechanism over IEEE 802.11 DCF and a service differentiation scheme on top of IEEE 802.11e EDCA, in terms of fairness, throughput, delay, loss, and estimated PSNR. Additionally, both objective and subjective video quality assessment have been performed using a prototype system. 3. A QoS-based Downlink/Uplink Fairness Scheme, which uses the stereotypes-based structure to balance the QoS parameters (i.e. throughput, delay, and loss) between downlink and uplink VoIP traffic. The proposed scheme has been modelled and tested through simulations. The results show that, in comparison with other downlink/uplink fairness-oriented solutions, the proposed scheme performs better in terms of VoIP capacity and fairness level between downlink and uplink traffic

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Energy Efficiency in Hybrid Mobile and Wireless Networks

Wireless Internet access is almost pervasive nowadays, and many types of wireless networks can be used to access the Internet. However, along with this growth, there is an even greater concern about the energy consumption and efficiency of mobile devices as well as of the supporting networks, triggering the appearance of the concept of green communication. While some efforts have been made towards this direction, challenges still exist and need to be tackled from diverse perspectives. Cellular networks, WLANs, and ad hoc networks in the form of wireless mesh networks are the most popular technologies for wireless Internet access. The availability of such a variety of access networks has also paved the way to explore synergistic approaches for Internet access, leading to the concept of hybrid networks and relay communications. In addition, many mobile devices are being equipped with multiple interfaces, enabling them to operate in hybrid networks. In contrast, the improvements in the battery technology itself have not matched the pace of the emerging mobile applications. The situation becomes more sophisticated when a mobile device functions also as a relay node to forward other station’s data. In the literature, energy efficiency of mobile devices has been addressed from various perspectives such as protocol-level efforts, battery management efforts, etc. However, there is little work on energy efficiency in hybrid mobile and wireless networks and devices with heterogeneous connections. For example, when there are multiple networks available to a mobile device, how to achieve optimum long-term energy consumption of such a device is an open question. Furthermore, in today’s cellular networks, micro-, pico-, and femto-cells are the most popular network topologies in order to support high data rate services and high user density. With the growth of such small-cell solutions, the energy consumption of these networks is also becoming an important concern for operators. Towards this direction, various solutions have been proposed, ranging from deployment strategies for base stations to cooperative techniques etc. However, as base stations have the largest share in a network’s energy consumption, methods that allow lightly-loaded base stations sleep or be switched off are possible means as a feasible step towards green communications. In this dissertation, we tackle the above mentioned problems from two perspectives, i.e., mobile station’s and operator’s perspectives. More specifically, by taking into account the amount of transferred data in uplinks and downlinks individually for various components in a hybrid network, strategies are proposed to reduce mobile station’s battery energy consumption. For this purpose, other parameters such as link distance and remaining battery energy can also be considered for handover decision making, in order to maximize energy efficiency of the mobile station. To optimize long-term energy consumption of the mobile stations operated in such scenarios, a Markov decision process-based methodology is proposed as our contribution to this topic. Moreover, from operator’s perspective, a network energy conservation scheme which may switch off a base station is proposed for micro- or pico-cells scenarios. Both deterministic and probabilistic schemes are proposed for network energy conservation. The problems considered and the solutions proposed in this dissertation advance the frontiers of the research work within the theme of energy efficiency for mobile devices as well as hybrid mobile and wireless networks