Subjective performance evaluation of bitrate allocation strategies for MPEG and JPEG Pleno point cloud compression.

The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as JPEG and MPEG to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual artifacts that negatively impact the perceived quality of media, which can only be reliably measured through subjective visual quality assessment experiments. While MPEG standards have been subjectively evaluated in previous studies on multiple occasions, no work has yet assessed the performance of the recent JPEG Pleno standard in comparison to them. In this study, a comprehensive performance evaluation of JPEG and MPEG standards for point cloud compression is conducted. The impact of different configuration parameters on the performance of the codecs is first analyzed with the help of objective quality metrics. The results from this analysis are used to define three rate allocation strategies for each codec, which are employed to compress a set of point clouds at four target rates. The set of distorted point clouds is then subjectively evaluated following two subjective quality assessment protocols. Finally, the obtained results are used to compare the performance of these compression standards and draw insights about best coding practices.

Attention driven foveated video quality assessment.

Contrast sensitivity of the human visual system to visual stimuli can be significantly affected by several mechanisms, e.g., vision foveation and attention. Existing studies on foveation based video quality assessment only take into account static foveation mechanism. This paper first proposes an advanced foveal imaging model to generate the perceived representation of video by integrating visual attention into the foveation mechanism. For accurately simulating the dynamic foveation mechanism, a novel approach to predict video fixations is proposed by mimicking the essential functionality of eye movement. Consequently, an advanced contrast sensitivity function, derived from the attention driven foveation mechanism, is modeled and then integrated into a wavelet-based distortion visibility measure to build a full reference attention driven foveated video quality (AFViQ) metric. AFViQ exploits adequately perceptual visual mechanisms in video quality assessment. Extensive evaluation results with respect to several publicly available eye-tracking and video quality databases demonstrate promising performance of the proposed video attention model, fixation prediction approach, and quality metric.

Driver-response relationships between frontal EEG and respiration during affective audiovisual stimuli.

The complementary nature and the coordinative tendencies of brain and body are essential to the way humans function. Although static features from brain and body signals have been shown to reflect emotions, the dynamic interrelation of the two systems during emotional processes is still in its infancy. This study aims at investigating the way brain signals captured by Electroencephalography (EEG) and bodily responses reflected in respiration interact when watching music clips. A non-linear measure is applied to frontal EEG and respiration to determine the driver/driven relationship between these two modalities. The results reveal a unidirectional dependence from respiration to EEG which adds evidence to the bodily-feedback theory.

Multivariate spectral analysis for identifying the brain activations during olfactory perception.

Olfactory perception is a complex phenomenon associated with other processes such as cognition and emotion. Due to this complexity, there are still open issues and challenges regarding olfactory psychophysiology. One challenge concerns the investigation of the hedonic dimension of olfaction, and how it affects the power of the brain oscillations. Although there are some EEG studies exploring the changes in the power of the brain oscillations during olfactory perception, they use simple power spectral analysis techniques and vary much in terms of the reported findings. To reduce this variability, we propose the use of multivariate spectral analysis, to reveal only the frequency patterns of the EEG signals that contribute the most to olfactory perception. The goal is to investigate how these frequency patterns are affected by hedonically different odors throughout the cortex.

The impact of expertise on brain computer interface based salient image retrieval.

Autonomous decision making modules in computer vision application allow recognition and classification of different objects, persons, and events in images and video sequences and also make it possible to classify different sensor readings (e.g. images) according to their scientific saliencies. In this paper, we propose a new approach to create the training set for these algorithms by retrieving salient images using electroencephalogram (EEG) and brain computer interface (BCI) and rapid image presentation. To this end, two groups of subjects, namely, expert and novice subjects were asked to participate in our experiments. We show that a relatively high retrieval accuracy can be achieved for most of the subjects. Furthermore, to assess the impact of expertise on the retrieval process, we study their EEG signals separately and show that there is a clear difference in their brainwaves while observing salient images.

An efficient P300-based brain-computer interface for disabled subjects.

A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed.

Application of the evidence framework to brain-computer interfaces.

A brain-computer interface (BCI) is a communication system, that implements the principle of "think and make it happen without any physical effort". This means a BCI allows a user to act on his environment only by using his thoughts, without using peripheral nerves and muscles. Nearly all BCIs contain as a core part a machine learning algorithm, which learns from training data a function, that can be used to discriminate different brain activities. In the present work we use a Bayesian framework for machine learning, the evidence framework [1], [2] to develop a variant of linear discriminant analysis for the use in a BCI based on electroencephalographic measurements (EEG). Properties of the resulting algorithm are: a) a continuous probabilistic output is given, b) fast estimation of regularization constants, and c) the possibility to select among different feature sets, the one which is most promising for classification. The algorithm has been tested on one dataset from the BCI competition 2002 and two datasets from the BCI competition 2003 and provides a classification accuracy of 95%, 81%, and 79% respectively.