279 research outputs found
Mobile graphics: SIGGRAPH Asia 2017 course
Peer ReviewedPostprint (published version
Low complexity in-loop perceptual video coding
The tradition of broadcast video is today complemented with user generated content, as portable devices support video coding. Similarly, computing is becoming ubiquitous, where Internet of Things (IoT) incorporate heterogeneous networks to communicate with personal and/or infrastructure devices. Irrespective, the emphasises is on bandwidth and processor efficiencies, meaning increasing the signalling options in video encoding. Consequently, assessment for pixel differences applies uniform cost to be processor efficient, in contrast the Human Visual System (HVS) has non-uniform sensitivity based upon lighting, edges and textures. Existing perceptual assessments, are natively incompatible and processor demanding, making perceptual video coding (PVC) unsuitable for these environments. This research allows existing perceptual assessment at the native level using low complexity techniques, before producing new pixel-base image quality assessments (IQAs). To manage these IQAs a framework was developed and implemented in the high efficiency video coding (HEVC) encoder. This resulted in bit-redistribution, where greater bits and smaller partitioning were allocated to perceptually significant regions. Using a HEVC optimised processor the timing increase was < +4% and < +6% for video streaming and recording applications respectively, 1/3 of an existing low complexity PVC solution. Future work should be directed towards perceptual quantisation which offers the potential for perceptual coding gain
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Hand gesture recognition using deep learning neural networks
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHuman Computer Interaction (HCI) is a broad field involving different types of interactions including gestures. Gesture recognition concerns non-verbal motions used as a means of communication in HCI. A system may be utilised to identify human gestures to convey information for device control. This represents a significant field within HCI involving device interfaces and users. The aim of gesture recognition is to record gestures that are formed in a certain way and then detected by a device such as a camera. Hand gestures can be used as a form of communication for many different applications. It may be used by people who possess different disabilities, including those with hearing-impairments, speech impairments and stroke patients, to communicate and fulfil their basic needs.
Various studies have previously been conducted relating to hand gestures. Some studies proposed different techniques to implement the hand gesture experiments. For image processing there are multiple tools to extract features of images, as well as Artificial Intelligence which has varied classifiers to classify different types of data. 2D and 3D hand gestures request an effective algorithm to extract images and classify various mini gestures and movements. This research discusses this issue using different algorithms. To detect 2D or 3D hand gestures, this research proposed image processing tools such as Wavelet Transforms and Empirical Mode Decomposition to extract image features. The Artificial Neural Network (ANN) classifier which used to train and classify data besides Convolutional Neural Networks (CNN). These methods were examined in terms of multiple parameters such as execution time, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood, negative likelihood, receiver operating characteristic, area under ROC curve and root mean square. This research discusses four original contributions in the field of hand gestures. The first contribution is an implementation of two experiments using 2D hand gesture video where ten different gestures are detected in short and long distances using an iPhone 6 Plus with 4K resolution. The experiments are performed using WT and EMD for feature extraction while ANN and CNN for classification. The second contribution comprises 3D hand gesture video experiments where twelve gestures are recorded using holoscopic imaging system camera. The third contribution pertains experimental work carried out to detect seven common hand gestures. Finally, disparity experiments were performed using the left and the right 3D hand gesture videos to discover disparities. The results of comparison show the accuracy results of CNN being 100% compared to other techniques. CNN is clearly the most appropriate method to be used in a hand gesture system.Imam Abdulrahman bin Faisal Universit
Adaptive Streaming: From Bitrate Maximization to Rate-Distortion Optimization
The fundamental conflict between the increasing consumer demand for better Quality-of-Experience (QoE) and the limited supply of network resources has become significant challenges to modern video delivery systems.
State-of-the-art adaptive bitrate (ABR) streaming algorithms are dedicated to drain available bandwidth in hope to improve viewers' QoE, resulting in inefficient use of network resources.
In this thesis, we develop an alternative design paradigm, namely rate-distortion optimized streaming (RDOS), to balance the contrast demands from video consumers and service providers.
Distinct from the traditional bitrate maximization paradigm, RDOS must operate at any given point along the rate-distortion curve, as specified by a trade-off parameter.
The new paradigm has found plausible explanations in information theory, economics, and visual perception.
To instantiate the new philosophy, we decompose adaptive streaming algorithms into three mutually independent components, including throughput predictor, reward function, and bitrate selector.
We provide a unified framework to understand the connections among all existing ABR algorithms.
The new perspective also illustrates the fundamental limitations of each algorithm by going behind its underlying assumptions.
Based on the insights, we propose novel improvements to each of the three functional components.
To alleviate a series of unrealistic assumptions behind bitrate-based QoE models, we develop a theoretically-grounded objective QoE model.
The new objective QoE model combines the information from subject-rated streaming videos and the prior knowledge about human visual system (HVS) in a principled way.
By analyzing a corpus of psychophysical experiments, we show the QoE function estimation can be formulated as a projection onto convex sets problem.
The proposed model presents strong generalization capability over a broad range of source contents, video encoders, and viewing conditions.
Most importantly, the QoE model disentangles bitrate with quality, making it an ideal component in the RDOS framework.
In contrast to the existing throughput estimators that approximate the marginal probability distribution over all connections, we optimize the throughput predictor conditioned on each client.
Although there are lack of training data for each Internet Protocol connection, we can leverage the latest advances in meta learning to incorporate the knowledge embedded in similar tasks.
With a deliberately designed objective function, the algorithm learns to identify similar structures among different network characteristics from millions of realistic throughput traces.
During the test phase, the model can quickly adapt to connection-level network characteristics with only a small amount of training data from novel streaming video clients with a small number of gradient steps.
The enormous space of streaming videos, constantly progressing encoding schemes, and great diversity of throughput characteristics make it extremely challenging for modern data-driven bitrate selectors that are trained with limited samples to generalize well.
To this end, we propose a Bayesian bitrate selection algorithm by adaptively fusing an online, robust, and short-term optimal controller with an offline, susceptible, and long-term optimal planner.
Depending on the reliability of the two controllers in certain system states, the algorithm dynamically prioritizes the one of the two decision rules to obtain the optimal decision.
To faithfully evaluate the performance of RDOS, we construct a large-scale streaming video dataset -- the Waterloo Streaming Video database.
It contains a wide variety of high quality source contents, encoders, encoding profiles, realistic throughput traces, and viewing devices.
Extensive objective evaluation demonstrates the proposed algorithm can deliver identical QoE to state-of-the-art ABR algorithms at a much lower cost.
The improvement is also supported by so-far the largest subjective video quality assessment experiment
Intelligent Circuits and Systems
ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering
A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks
In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs
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