232 research outputs found

    Performance analysis and application development of hybrid WiMAX-WiFi IP video surveillance systems

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    Traditional Closed Circuit Television (CCTV) analogue cameras installed in buildings and other areas of security interest necessitates the use of cable lines. However, analogue systems are limited by distance; and storing analogue data requires huge space or bandwidth. Wired systems are also prone to vandalism, they cannot be installed in a hostile terrain and in heritage sites, where cabling would distort original design. Currently, there is a paradigm shift towards wireless solutions (WiMAX, Wi-Fi, 3G, 4G) to complement and in some cases replace the wired system. A wireless solution of the Fourth-Generation Surveillance System (4GSS) has been proposed in this thesis. It is a hybrid WiMAX-WiFi video surveillance system. The performance analysis of the hybrid WiMAX-WiFi is compared with the conventional WiMAX surveillance models. The video surveillance models and the algorithm that exploit the advantages of both WiMAX and Wi-Fi for scenarios of fixed and mobile wireless cameras have been proposed, simulated and compared with the mathematical/analytical models. The hybrid WiMAX-WiFi video surveillance model has been extended to include a Wireless Mesh configuration on the Wi-Fi part, to improve the scalability and reliability. A performance analysis for hybrid WiMAX-WiFi system with an appropriate Mobility model has been considered for the case of mobile cameras. A security software application for mobile smartphones that sends surveillance images to either local or remote servers has been developed. The developed software has been tested, evaluated and deployed in low bandwidth Wi-Fi wireless network environments. WiMAX is a wireless metropolitan access network technology that provides broadband services to the connected customers. Major modules and units of WiMAX include the Customer Provided Equipment (CPE), the Access Service Network (ASN) which consist one or more Base Stations (BS) and the Connectivity Service Network (CSN). Various interfaces exist between each unit and module. WiMAX is based on the IEEE 802.16 family of standards. Wi-Fi, on the other hand, is a wireless access network operating in the local area network; and it is based on the IEEE 802.11 standards

    Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks

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    Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet-of-Things applications, surveillance systems and semantic crawlers of large video repositories, the video capture and the CNN-based semantic analysis parts do not tend to be co-located. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification models that directly ingest AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video bitstreams and applying complex optical flow calculations prior to CNN processing, we only retain motion vector and select texture information at significantly-reduced bitrates and apply no additional processing prior to CNN ingestion. Based on three CNN architectures and two action recognition datasets, we achieve 11%-94% saving in bitrate with marginal effect on classification accuracy. A model-based selection between multiple CNNs increases these savings further, to the point where, if up to 7% loss of accuracy can be tolerated, video classification can take place with as little as 3 kbps for the transport of the required compressed video information to the system implementing the CNN models

    Sequence-Level Reference Frames In Video Coding

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    The proliferation of low-cost DRAM chipsets now begins to allow for the consideration of substantially-increased decoded picture buffers in advanced video coding standards such as HEVC, VVC, and Google VP9. At the same time, the increasing demand for rapid scene changes and multiple scene repetitions in entertainment or broadcast content indicates that extending the frame referencing interval to tens of minutes or even the entire video sequence may offer coding gains, as long as one is able to identify frame similarity in a computationally- and memory-efficient manner. Motivated by these observations, we propose a “stitching” method that defines a reference buffer and a reference frame selection algorithm. Our proposal extends the referencing interval of inter-frame video coding to the entire length of video sequences. Our reference frame selection algorithm uses well-established feature descriptor methods that describe frame structural elements in a compact and semantically-rich manner. We propose to combine such compact descriptors with a similarity scoring mechanism in order to select the frames to be “stitched” to reference picture buffers of advanced inter-frame encoders like HEVC, VVC, and VP9 without breaking standard compliance. Our evaluation on synthetic and real-world video sequences with the HEVC and VVC reference encoders shows that our method offers significant rate gains, with complexity and memory requirements that remain manageable for practical encoders and decoders

    End to end Multi-Objective Optimisation of H.264 and HEVC Codecs

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    All multimedia devices now incorporate video CODECs that comply with international video coding standards such as H.264 / MPEG4-AVC and the new High Efficiency Video Coding Standard (HEVC) otherwise known as H.265. Although the standard CODECs have been designed to include algorithms with optimal efficiency, large number of coding parameters can be used to fine tune their operation, within known constraints of for e.g., available computational power, bandwidth, consumer QoS requirements, etc. With large number of such parameters involved, determining which parameters will play a significant role in providing optimal quality of service within given constraints is a further challenge that needs to be met. Further how to select the values of the significant parameters so that the CODEC performs optimally under the given constraints is a further important question to be answered. This thesis proposes a framework that uses machine learning algorithms to model the performance of a video CODEC based on the significant coding parameters. Means of modelling both the Encoder and Decoder performance is proposed. We define objective functions that can be used to model the performance related properties of a CODEC, i.e., video quality, bit-rate and CPU time. We show that these objective functions can be practically utilised in video Encoder/Decoder designs, in particular in their performance optimisation within given operational and practical constraints. A Multi-objective Optimisation framework based on Genetic Algorithms is thus proposed to optimise the performance of a video codec. The framework is designed to jointly minimize the CPU Time, Bit-rate and to maximize the quality of the compressed video stream. The thesis presents the use of this framework in the performance modelling and multi-objective optimisation of the most widely used video coding standard in practice at present, H.264 and the latest video coding standard, H.265/HEVC. When a communication network is used to transmit video, performance related parameters of the communication channel will impact the end-to-end performance of the video CODEC. Network delays and packet loss will impact the quality of the video that is received at the decoder via the communication channel, i.e., even if a video CODEC is optimally configured network conditions will make the experience sub-optimal. Given the above the thesis proposes a design, integration and testing of a novel approach to simulating a wired network and the use of UDP protocol for the transmission of video data. This network is subsequently used to simulate the impact of packet loss and network delays on optimally coded video based on the framework previously proposed for the modelling and optimisation of video CODECs. The quality of received video under different levels of packet loss and network delay is simulated, concluding the impact on transmitted video based on their content and features

    Resource-Constrained Low-Complexity Video Coding for Wireless Transmission

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    Wireless Deep Video Semantic Transmission

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    In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.Comment: published in IEEE JSA
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