Optimal power allocation and joint source–channel coding for wireless DS-CDMA visual sensor networks using the Nash bargaining solution

ABSTRACT In this paper, we propose a scheme for the optimal allocation of power, source coding rate, and channel coding rate for each of the nodes of a wireless Direct Sequence Code Division Multiple Access (DS-CDMA) visual sensor network. The optimization is quality-driven, i.e. the received quality of the video that is transmitted by the nodes is optimized. The scheme takes into account the fact that the sensor nodes may be imaging scenes with varying levels of motion. Nodes that image low-motion scenes will require a lower source coding rate, so they will be able to allocate a greater portion of the total available bit rate to channel coding. Stronger channel coding will mean that such nodes will be able to transmit at lower power. This will both increase battery life and reduce interference to other nodes. Two optimization criteria are considered. One that minimizes the average video distortion of the nodes and one that minimizes the maximum distortion among the nodes. The transmission powers are allowed to take continuous values, whereas the source and channel coding rates can assume only discrete values. Thus, the resulting optimization problem lies in the field of mixed-integer optimization tasks and is solved using Particle Swarm Optimization. Our experimental results show the importance of considering the characteristics of the video sequences when determining the transmission power, source coding rate and channel coding rate for the nodes of the visual sensor network

A No-Reference Bitstream-based Perceptual Model for Video Quality Estimation of Videos Affected by Coding Artifacts and Packet Losses

In this work, we propose a No-Reference (NR) bitstream-based model for predicting the quality of H.264/AVC video sequences, aeffected by both compression artifacts and transmission impairments. The concept of the article is based on a feature extraction procedure, where a large number of features are calculated from the impaired bitstream. Many of the features are mostly proposed in this work, while the specificc set of the features as a whole is applied for the first time for making NR video quality predictions. All feature observations are taken as input to the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. LASSO indicates the most important features, and using only them, it is able to estimate the Mean Opinion Score (MOS) with high accuracy. Indicatively, we point out that only 13 features are able to produce a Pearson Correlation Coefficient of 0:92 with the MOS. Interestingly, the performance statistics we computed in order to assess our method for predicting the Structural Similarity Index and the Video Quality Metric are equally good. Thus, the obtained experimental results verifi ed the suitability of the features selected by LASSO as well as the ability of LASSO in making accurate predictions through sparse modeling

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using featuresthat account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. Thepurpose of this study is to analyze a number of potentially quality-relevant features in order to select the mostsuitable set of features for building the desired models. The proposed sets of features have not been used in theliterature and some of the features are used for the first time in this study. The features are employed by the leastabsolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward per-ceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression onthe reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjec-tively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RRLASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual qualitywith high accuracy, higher than that of ridge, which uses more features. The comparisons with competingworks and two full-reference metrics also verify the superiority of our models

Quality Assessment of Single-Channel EEG for Wearable Devices

International audienceThe recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel. View Full-Tex