Selective reconstruction of low motion regions in distributed video coding

The research work disclosed in this publication is partially funded by the Strategic Educational Pathways Scholarship Scheme (Malta). The scholarship is part-financed by the European Union - European Social Fund. (ESF 1.25).The Distributed Video Coding (DVC) paradigm offers lightweight encoding capabilities which are suitable for devices with limited computational resources. Moreover, DVC techniques can theoretically achieve the same coding efficiency as the traditional video coding schemes which employ more complex encoders. However, the performance of practical DVC architectures is still far from such theoretical bounds, mainly due to the inaccurate Side Information (SI) predicted at the decoder. The work presented in this paper shows that the soft-input values predicted at the decoder may not correctly predict the Wyner-Ziv coefficients, even for regions containing low motion. This generally degrades compression efficiency. To mitigate this, the proposed system predicts the quality of the SI for regions with low motion and then employs a technique which avoids correcting mismatch at locations where the SI and WZ falls within different quantization intervals but the prediction error is within an acceptable range. The experimental results demonstrate that the average Peak Signal-to-Noise Ratio (PSNR) is improved by up to 0.39dB compared to the state-of-the-art DVC architectures, like the DISCOVER codec.peer-reviewe

Multi-View Video Packet Scheduling

In multiview applications, multiple cameras acquire the same scene from different viewpoints and generally produce correlated video streams. This results in large amounts of highly redundant data. In order to save resources, it is critical to handle properly this correlation during encoding and transmission of the multiview data. In this work, we propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras are transmitted over a bottleneck channel to clients that reconstruct the multiview images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. We propose a problem formulation for the optimization of the packet scheduling policies, which adapt to variations in the scene content. Then, we design a low complexity scheduling algorithm based on a trellis search that selects the subset of candidate packets to be transmitted towards effective multiview reconstruction at clients. Extensive simulation results confirm the gain of our scheduling algorithm when inter-source correlation information is used in the scheduler, compared to scheduling policies with no information about the correlation or non-adaptive scheduling policies. We finally show that increasing the optimization horizon in the packet scheduling algorithm improves the transmission performance, especially in scenarios where the level of correlation rapidly varies with time

Adaptive rounding operator for efficient Wyner-Ziv video coding

The research work disclosed in this publication is partially funded by the Strategic Educational Pathways Scholarship Scheme (Malta). The scholarship is part-financed by the European Union – European Social Fund. (ESF 1.25).The Distributed Video Coding (DVC) paradigm can theoretically reach the same coding efficiencies of predictive block-based video coding schemes, like H.264/AVC. However, current DVC architectures are still far from this ideal performance. This is mainly attributed to inaccuracies in the Side Information (SI) predicted at the decoder. The work in this paper presents a coding scheme which tries to avoid mismatch in the SI predictions caused by small variations in light intensity. Using the appropriate rounding operator for every coefficient, the proposed method significantly reduces the correlation noise between the Wyner-Ziv (WZ) frame and the corresponding SI, achieving higher coding efficiencies. Experimental results demonstrate that the average Peak Signal-to-Noise Ratio (PSNR) is improved by up to 0.56dB relative to the DISCOVER codec.peer-reviewe

Decoding face categories in diagnostic subregions of primary visual cortex

Higher visual areas in the occipitotemporal cortex contain discrete regions for face processing, but it remains unclear if V1 is modulated by top-down influences during face discrimination, and if this is widespread throughout V1 or localized to retinotopic regions processing task-relevant facial features. Employing functional magnetic resonance imaging (fMRI), we mapped the cortical representation of two feature locations that modulate higher visual areas during categorical judgements – the eyes and mouth. Subjects were presented with happy and fearful faces, and we measured the fMRI signal of V1 regions processing the eyes and mouth whilst subjects engaged in gender and expression categorization tasks. In a univariate analysis, we used a region-of-interest-based general linear model approach to reveal changes in activation within these regions as a function of task. We then trained a linear pattern classifier to classify facial expression or gender on the basis of V1 data from ‘eye’ and ‘mouth’ regions, and from the remaining non-diagnostic V1 region. Using multivariate techniques, we show that V1 activity discriminates face categories both in local ‘diagnostic’ and widespread ‘non-diagnostic’ cortical subregions. This indicates that V1 might receive the processed outcome of complex facial feature analysis from other cortical (i.e. fusiform face area, occipital face area) or subcortical areas (amygdala)