In this paper, we investigate how visualization factors, such as disparity, mobility, angular resolution, and viewpoint interpolation, influence the quality of experience (QoE) in a stereoscopic multiview environment. In order to do so, we set up a dedicated testing room and conducted subjective experiments. We also developed a framework that emulates a supermultiview environment. This framework can be used to investigate and assess the effects of angular resolution and viewpoint interpolation on the QoE produced by multiview systems, and provide relevant cues as to how the baselines of cameras and interpolation strategies in such systems affect user experience. Aspects such as visual comfort, model fluidity, sense of immersion, and the three-dimensional (3D) experience as a whole have been assessed for several test cases. Obtained results suggest that user experience in an motion parallax environment is not as critically influenced by configuration parameters such as disparity as initially thought. In addition, extensive subjective tests have indicated that while users are very sensitive to angular resolution in multiview 3D systems, this sensitivity tends not to be as critical when a user is performing a task that involves a great amount of interaction with the multiview content. These tests have also indicated that interpolating intermediate viewpoints can be effective in reducing the required view density without degrading the perceived QoE.

In this paper we investigate how configuration and visualization parameters influence the quality of experience in a 3D interactive environment, more specifically in a motion parallax setup. In order to do so, we designed a dedicated testing room and conducted subjective experiments with a team of evaluators. The tests considered parameters such as different disparities, amount of parallax, monitor sizes and visualization angles. Factors such as visual comfort, sense of immersion and the 3D experience as a whole have been assessed. The users were also asked to execute a task in the 3D motion parallax environment assessing the difficulty to complete the task for different parameter configurations. Obtained results suggest that user experience in an immersive environment is not as critically influenced by configuration parameters such as disparity and amount of parallax as initially thought. They also indicate that a better understanding of how this experience is influenced still requires the design and conduction of more comprehensive testing procedures.

In this work, we propose a novel approach to detect and track, in videoconference sequences, six landmarks on eyes: the four corners and the pupils. Detection is based on the Inner Product Detector (IPD), and tracking on the Lucas-Kanade (LK) technique. The novelty of our method consists in the integration between detection and tracking, the evaluation of the temporal consistency to decrease the false positive rates, and the use of geometrical constraints to infer the position of missing points. In our experiments, we use five high definition video sequences with four subjects, different types of background, fast movements, blurring and occlusion. The obtained results have shown that the proposed technique is capable of detecting and tracking landmarks with good reliability.

In this paper we evaluate the impact of different encoding configurations such as compression ratios, frame rates and resolution on the perceived quality of high definition video-conference applications. After generating a high quality video database, degraded sequences had their quality assessed by state-of-the-art automatic metrics. Results have shown that, for low rates, it is preferable to decrease the frame rate and resolution than to increase the compression ratio. Also, a higher frame rate tends to be more important than spatial resolution in terms of perceived quality.

In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start with the generation of a large real image database containing pictures taken by human users in a variety of situations, and the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth, we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur (gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features. We test this paradigm by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure. Experimental results show that this leads to an improved performance that better reflects the images' ground truth. Finally, based upon the real image database, we show that the proposed method also outperforms other algorithms and metrics in realistic blur scenarios.

We address a scenario where energy-constrained sensors in a wireless sensor network can choose among different distributed coding schemes to encode their data. We propose a framework where the network is described as a graph, with sensors representing the nodes, and where communication and processing costs are associated to edge weights and the coding schemes associated to states of operation. After describing data transitions and edge costs, we show that a shortest-path algorithm can be used to find the optimum network configuration, i.e., the one that leads to the lowest overall energy consumption

We address the problem of energy consumption reduction for wireless sensor networks, where each of the sensors has limited power and acquires data that should be transmitted to a central node. The final goal is to have a reconstructed version of the data measurements at the central node, with the sensors spending as little energy as possible, for a given data reconstruction accuracy. In our scenario, sensors in the network have a choice of different coding schemes to achieve varying levels of compression. The compression algorithms considered are based on the lifting factorization of the wavelet transform, and exploit the natural data flow in the network to aggregate data by computing partial wavelet coefficients that are refined as data flows towards the central node. The proposed algorithm operates by first selecting a routing strategy through the network. Then, for each route, an optimal combination of data representation algorithms i.e. assignment at each node, is selected. A simple heuristic is used to determine the data representation technique to use once path merges are taken into consideration. We demonstrate that by optimizing the coding algorithm selection the overall energy consumption can be significantly reduced when compared to the case when data is just quantized and forwarded to the central node. Moreover, the proposed algorithm provides a tool to compare different routing techniques and identify those that are most efficient overall, for given node locations. We evaluate the algorithm using both a second-order autoregressive (AR) model and empirical data from a real wireless sensor network deployment.

We address the problem of compression for wireless sensor networks, where each of the sensors has limited power, and acquires data that should be sent to a central node. The final goal is to have a reconstructed version of the sampled field at the central node, with the sensors spending as little energy as possible. We propose a distributed compression algorithm for multihop, distributed sensor networks based on the lifting factorization of the wavelet transform that exploits the natural data flow in the network to aggregate data by computing partial wavelet coefficients that are refined as the data flows towards the central node. A key result of our work is that by performing partial computations we greatly reduce unnecessary transmission, significantly reducing the overall energy consumption.

We address the problem of compression for wireless sensor networks, where each of the sensors has limited power, and acquires data that should be sent to a remote central node. The final goal is to have a reconstructed version of the sampled field at the central node, with the sensors spending as little energy as possible. We propose a distributed wavelet algorithm, based on the lifting scheme, as a means to decorrelate data at the nodes by exchanging information between neighboring sensors. A key result of our work is that by using a locally adaptive distributed transform it is possible to optimize overall power consumption by operating at the right trade-off point between local processing and transmission costs.

The matching pursuits (MP) coding method has been employed successfully in video coding. In it, the motion compensated frame difference is decomposed on an overcomplete dictionary of atoms, generating a sequence of pairs specifying the atoms used and their corresponding coefficients. A particular rate-distortion trade-off is achieved by setting both the number of atoms and the quantizers of their coefficients. Several strategies have been presented in order to set this trade-off. In this paper we propose a novel method for performing matching pursuits quantization, based on the notion of decomposition in generalized bit-planes. Such decompositions generate just a sequence of indexes. They provide an elegant solution to the trade-off between quantization of coefficients and number of passes in the MP algorithm. We show that they can be regarded as generalizations of decompositions followed by linear quantization of the coefficients. In addition, we state a theorem that sets bounds for their R-D performance. We test the effectiveness of the proposed method using the framework of Neff and Zakhor's MP video encoder. The results obtained are promising, presenting, without any ad-hoc assumptions about the R-D behavior of the coded frames or any increase in computational complexity, a significant improvement over the classical MP video coders. Also, the results are as good as the ones obtained employing more sophisticated strategies.