Decebal Constantin Mocanu

Factored four way conditional restricted Boltzmann machines for activity recognition

This paper proposes a new learning algorithm for human activity recognition.Its name is factored four way conditional restricted Boltzmann machine (FFW-CRBM).FFW-CRBMs are capable of simultaneous regression and classification.FFW-CRBMs came together with their own training procedure.The training procedure name is sequential Markov chain contrastive divergence. This paper introduces a new learning algorithm for human activity recognition capable of simultaneous regression and classification. Building upon conditional restricted Boltzmann machines (CRBMs), Factored four way conditional restricted Boltzmann machines (FFW-CRBMs) incorporate a new label layer and four-way interactions among the neurons from the different layers. The additional layer gives the classification nodes a similar strong multiplicative effect compared to the other layers, and avoids that the classification neurons are overwhelmed by the (much larger set of) other neurons. This makes FFW-CRBMs capable of performing activity recognition, prediction and self auto evaluation of classification within one unified framework. As a second contribution, sequential Markov chain contrastive divergence (SMcCD) is introduced. SMcCD modifies Contrastive Divergence to compensate for the extra complexity of FFW-CRBMs during training. Two sets of experiments one on benchmark datasets and one a robotic platform for smart companions show the effectiveness of FFW-CRBMs.

When does lower bitrate give higher quality in modern video services

Due to the difficulties on approximating the human perception with algorithms, increasing the users Quality of Experience (QoE) in modern video services is a challenging task. But more than that, prior to estimating QoE, it is important to know how different types of network impairments actually affect the video quality. This paper takes a closer look at the relation between the network quality of service (QoS) and the video QoE degradation. Using a sophisticated network emulation environment, we benchmark a range of video types and video quality levels under controlled network conditions. Our analysis shows that, along with a number of expected situations come also some counterintuitive QoS-to-QoE conditions. We discuss ways in which a better understanding of the mutual influence between networks and video streams could lead to more efficient utilization of the Internet.

Instantaneous Video Quality Assessment for lightweight devices

Monitoring and controlling the users Quality of Experience (QoE) in modern video services is a challenging proposition, mainly due to the limitations of current video quality assessment algorithms. While subjective QoE methods would better reflect the nature of human perception, these are not suitable in real-time automation cases. On the other hand, the existing objective algorithms are either too complex or too inaccurate, particularly in the context of lightweight devices such as camera sensors or smart phones. This paper introduces a novel objective QoE algorithm, Instantaneous Video Quality Assessment (IVQA), that is comparably as accurate as the most heavyweight algorithm available in the literature but can also be run in real-time. This approach is tested against a selection of ten objective metrics and benchmarked with a subjective user dataset.

Reduced reference image quality assessment via Boltzmann Machines

Monitoring and controlling the users perceived quality, in modern video services is a challenging proposition, mainly due to the limitations of current Image Quality Assessment (IQA) algorithms. Subjective Quality of Experience (QoE) is widely used to get a right impression, but unfortunately this can not be used in real world scenarios. In general, objective QoE algorithms represent a good substitution for the subjective ones, and they are split in three main directions: Full Reference (FR), Reduced Reference (RR), and No Reference (NR). From these three, the RR IQA approach offers a practical solution to assess the quality of an impaired image due to the fact that just a small amount of information is needed from the original image. At the same time, keeping in mind that we need automated QoE algorithms which are context independent, in this paper we introduce a novel stochastic RR IQA metric to assess the quality of an image based on Deep Learning, namely Restricted Boltzmann Machine Similarity Measure (RBMSim). RBMSim was evaluated on two benchmarked image databases with subjective studies, against objective IQA algorithms. The results show that its performance is comparable, or even better in some cases, with widely known FR IQA methods.

A topological insight into restricted Boltzmann machines

Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as density estimators. Thus, their generative and discriminative capabilities, but also their computational time are instrumental to a wide range of applications. Our main contribution is to look at RBMs from a topological perspective, bringing insights from network science. Firstly, here we show that RBMs and Gaussian RBMs (GRBMs) are bipartite graphs which naturally have a small-world topology. Secondly, we demonstrate both on synthetic and real-world datasets that by constraining RBMs and GRBMs to a scale-free topology (while still considering local neighborhoods and data distribution), we reduce the number of weights that need to be computed by a few orders of magnitude, at virtually no loss in generative performance. Thirdly, we show that, for a fixed number of weights, our proposed sparse models (which by design have a higher number of hidden neurons) achieve better generative capabilities than standard fully connected RBMs and GRBMs (which by design have a smaller number of hidden neurons), at no additional computational costs.

Cognitive no-reference video quality assessment for mobile streaming services

The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experience (QoE), entails the use of automated methods (which excludes subjective QoE) that can be executed in real-time (i.e. without delaying the streaming process). This calls for lightweight algorithms that provide accurate results under considerable constraints. Starting from a low complexity no-reference objective algorithm for still images, in this work we contribute a new version that not only works for videos but, is general enough to adjust to a diverse range of video types while not significantly increasing the computational complexity. To achieve the necessary level of flexibility and computational efficiency, our method relies merely on information available at the client side and is equipped with a lightweight Artificial Neural Network which makes the algorithm independent from type of network or video. Its resource efficiency and generality make our method fit to be used in mobile streaming services. To prove the viability of our approach, we show a high level of correlation with the well-known full-reference method SSIM.

An experimental survey of no-reference video quality assessment methods

Purpose The Video Quality Metric (VQM) is one of the most used objective methods to assess video quality, because of its high correlation with the human visual system (HVS). VQM is, however, not viable in real-time deployments such as mobile streaming, not only due to its high computational demands but also because, as a Full Reference (FR) metric, it requires both the original video and its impaired counterpart. In contrast, No Reference (NR) objective algorithms operate directly on the impaired video and are considerably faster but loose out in accuracy. The purpose of this paper is to study how differently NR metrics perform in the presence of network impairments. Design/methodology/approach The authors assess eight NR metrics, alongside a lightweight FR metric, using VQM as benchmark in a self-developed network-impaired video data set. This paper covers a range of methods, a diverse set of video types and encoding conditions and a variety of network impairment test-cases. Findings The authors show the extent by which packet loss affects different video types, correlating the accuracy of NR metrics to the FR benchmark. This paper helps identifying the conditions under which simple metrics may be used effectively and indicates an avenue to control the quality of streaming systems. Originality/value Most studies in literature have focused on assessing streams that are either unaffected by the network (e.g. looking at the effects of video compression algorithms) or are affected by synthetic network impairments (i.e. via simulated network conditions). The authors show that when streams are affected by real network conditions, assessing Quality of Experience becomes even harder, as the existing metrics perform poorly.

Node centrality awareness via swarming effects

Centralization is a weakness in large scale dynamic topologies and, thus, collaboratively electing at runtime the most impactful (central) nodes is necessary to ensure reliability. However, little has been achieved in measuring the centrality of nodes in an accurate, fast, decentralized and with low overhead method. This paper proposes a swarm-inspired approach (DANIS) to detect the nodes that would most impact the network connectivity if removed. The idea lies on the trivial fact that the more accessible a node is, the more resources per time unit it loses. Experiments on random, scale-free and small-world graph topologies indicate that DANIS achieves higher accuracy, faster convergence and fewer communication overhead compared to other methods.

Network performance assessment with Quality of experience benchmarks

Today the performance of network services and devices is mainly assessed using Quality of Services (QoS) factors. These provide statistics about the quality of the network behavior but cannot accurately reflect how the unpredictable impairments which might occur in the network end up affecting the perception of the final beneficiary of these services, i.e. the user. This situation arises because QoS-based performance analysis does not capture the combined end-to-end properties of networks and applications. In this paper, we introduce a new network performance methodology based on Quality of Experience benchmarks, whereby we estimate the quality of the service as it is perceived by the user. We illustrate this approach in the context of video streaming services, showing how to evaluate quality degradation in Software Defined Networks. Our approach is better suited to the evaluation of dynamic networks and helps better pinpointing the critical factors that affect the applications the most.

Cognitive streaming on android devices

As the number of mobile devices increases, so do the complexity of wireless networks and the users requirements. This tendency makes necessary for Multimedia Services to take the needed actions to adapt to the upcoming technology. A prominent example of this type of services is HTTP Adaptive Video Streaming Applications. In this research, we have studied how the latest HTTP Adaptive Streaming techniques, mainly developed for standard computers, could be adapted and used in mobile wireless devices. Furthermore, inspired by these solutions, which usually make use of Reinforcement Learning (RL) algorithms to find the suitable streaming rate, we have conceived a novel smart video player client in Java for Android platform using the Dynamic Adaptive Streaming over HTTP (DASH) protocol. We have assessed the performance of our proposed solution in a self-developed wireless test-bed under different network conditions. Thus, we have seen that by including in the reward function contributions regarding the download speed of the video segments, especially needed due to the fluctuating nature of the wireless networks, and the segments already buffered, improves drastically the overall performance of the video client. Besides that, we have discovered that, in a cognitive adaptive approach, bandwidth constraints affect the users experience more substantially, while impairments such as packet loss can be prevented.