Augmented LDPC Graph for Distributed Video Coding with Multiple Side Information

The advances made in channel-capacity codes, such as turbo codes and low-density parity-check (LDPC) codes, have played a major role in the emerging distributed source coding paradigm. LDPC codes can be easily adapted to new source coding strategies due to their natural representation as bipartite graphs and the use of quasi-optimal decoding algorithms, such as belief propagation. This paper tackles a relevant scenario in distributedvideo coding: lossy source coding when multiple side information (SI) hypotheses are available at the decoder, each one correlated with the source according to different correlation noise channels. Thus, it is proposed to exploit multiple SI hypotheses through an efficient joint decoding technique withmultiple LDPC syndrome decoders that exchange information to obtain coding efficiency improvements. At the decoder side, the multiple SI hypotheses are created with motion compensated frame interpolation and fused together in a novel iterative LDPC based Slepian-Wolf decoding algorithm. With the creation of multiple SI hypotheses and the proposed decoding algorithm, bitrate savings up to 8.0% are obtained for similar decoded quality.

A flexible side information generation framework for distributed video coding

One of the most efficient approaches to generate the side information (SI) in distributed video codecs is through motion compensated frame interpolation where the current frame is estimated based on past and future reference frames. However, this approach leads to significant spatial and temporal variations in the correlation noise between the source at the encoder and the SI at the decoder. In such scenario, it would be useful to design an architecture where the SI can be more robustly generated at the block level, avoiding the creation of SI frame regions with lower correlation, largely responsible for some coding efficiency losses. In this paper, a flexible framework to generate SI at the block level in two modes is presented: while the first mode corresponds to a motion compensated interpolation (MCI) technique, the second mode corresponds to a motion compensated quality enhancement (MCQE) technique where a low quality Intra block sent by the encoder is used to generate the SI by doing motion estimation with the help of the reference frames. The novel MCQE mode can be overall advantageous from the rate-distortion point of view, even if some rate has to be invested in the low quality Intra coding blocks, for blocks where the MCI produces SI with lower correlation. The overall solution is evaluated in terms of RD performance with improvements up to 2 dB, especially for high motion video sequences and long Group of Pictures (GOP) sizes.

Low Complexity Intra Mode Selection for Efficient Distributed Video Coding

Motion compensated frame interpolation (MCFI) is one of the most efficient solutions to generate side information (SI) in the context of distributed video coding. However, it creates SI with rather significant motion compensated errors for some frame regions while rather small for some other regions depending on the video content. In this paper, a low complexity Infra mode selection algorithm is proposed to select the most 'critical' blocks in the WZ frame and help the decoder with some reliable data for those blocks. For each block, the novel coding mode selection algorithm estimates the encoding rate for the Intra based and WZ coding modes and determines the best coding mode while maintaining a low encoder complexity. The proposed solution is evaluated in terms of rate-distortion performance with improvements up to 1.2 dB regarding a WZ coding mode only solution.

Advanced side information creation techniques and framework for Wyner-Ziv video coding

Recently, several distributed video coding (DVC) solutions based on the distributed source coding (DSC) paradigm have appeared in the literature. Wyner-Ziv (WZ) video coding, a particular case of DVC where side information is made available at the decoder, enable to achieve a flexible distribution of the computational complexity between the encoder and decoder, promising to fulfill novel requirements from applications such as video surveillance, sensor networks and mobile camera phones. The quality of the side information at the decoder has a critical role in determining the WZ video coding rate-distortion (RD) performance, notably to raise it to a level as close as possible to the RD performance of standard predictive video coding schemes. Towards this target, efficient motion search algorithms for powerful frame interpolation are much needed at the decoder. In this paper, the RD performance of a Wyner-Ziv video codec is improved by using novel, advanced motion compensated frame interpolation techniques to generate the side information. The development of these type of side information estimators is a difficult problem in WZ video coding, especially because the decoder only has available some reference, decoded frames. Based on the regularization of the motion field, novel side information creation techniques are proposed in this paper along with a new frame interpolation framework able to generate higher quality side information at the decoder. To illustrate the RD performance improvements, this novel side information creation framework has been integrated in a transform domain turbo coding based Wyner-Ziv video codec. Experimental results show that the novel side information creation solution leads to better RD performance than available state-of-the-art side information estimators, with improvements up to 2 dB: moreover, it allows outperforming H.264/AVC Intra by up to 3 dB with a lower encoding complexity.

Distributed Model Predictive Control for Housing with Hourly Auction of Available Energy

This paper presents a distributed model predictive control (DMPC) for indoor thermal comfort that simultaneously optimizes the consumption of a limited shared energy resource. The control objective of each subsystem is to minimize the heating/cooling energy cost while maintaining the indoor temperature and used power inside bounds. In a distributed coordinated environment, the control uses multiple dynamically decoupled agents (one for each subsystem/house) aiming to achieve satisfaction of coupling constraints. According to the hourly power demand profile, each house assigns a priority level that indicates how much is willing to bid in auction for consume the limited clean resource. This procedure allows the bidding value vary hourly and consequently, the agents order to access to the clean energy also varies. Despite of power constraints, all houses have also thermal comfort constraints that must be fulfilled. The system is simulated with several houses in a distributed environment.

A denoising approach for iterative side information creation in distributed video coding

In distributed video coding, motion estimation is typically performed at the decoder to generate the side information, increasing the decoder complexity while providing low complexity encoding in comparison with predictive video coding. Motion estimation can be performed once to create the side information or several times to refine the side information quality along the decoding process. In this paper, motion estimation is performed at the decoder side to generate multiple side information hypotheses which are adaptively and dynamically combined, whenever additional decoded information is available. The proposed iterative side information creation algorithm is inspired in video denoising filters and requires some statistics of the virtual channel between each side information hypothesis and the original data. With the proposed denoising algorithm for side information creation, a RD performance gain up to 1.2 dB is obtained for the same bitrate.

Design and performance of a novel low-density parity-check code for distributed video coding

Low-density parity-check (LDPC) codes are nowadays one of the hottest topics in coding theory, notably due to their advantages in terms of bit error rate performance and low complexity. In order to exploit the potential of the Wyner-Ziv coding paradigm, practical distributed video coding (DVC) schemes should use powerful error correcting codes with near-capacity performance. In this paper, new ways to design LDPC codes for the DVC paradigm are proposed and studied. The new LDPC solutions rely on merging parity-check nodes, which corresponds to reduce the number of rows in the parity-check matrix. This allows to change gracefully the compression ratio of the source (DCT coefficient bitplane) according to the correlation between the original and the side information. The proposed LDPC codes reach a good performance for a wide range of source correlations and achieve a better RD performance when compared to the popular turbo codes.

Distributed process execution in collaborative networks

Processes are a central entity in enterprise collaboration. Collaborative processes need to be executed and coordinated in a distributed Computational platform where computers are connected through heterogeneous networks and systems. Life cycle management of such collaborative processes requires a framework able to handle their diversity based on different computational and communication requirements. This paper proposes a rational for such framework, points out key requirements and proposes it strategy for a supporting technological infrastructure. Beyond the portability of collaborative process definitions among different technological bindings, a framework to handle different life cycle phases of those definitions is presented and discussed. (c) 2007 Elsevier Ltd. All rights reserved.

Comparison of different breast planning techniques and algorithms for radiation therapy treatment

This work aims at investigating the impact of treating breast cancer using different radiation therapy (RT) techniques – forwardly-planned intensity-modulated, f-IMRT, inversely-planned IMRT and dynamic conformal arc (DCART) RT – and their effects on the whole-breast irradiation and in the undesirable irradiation of the surrounding healthy tissues. Two algorithms of iPlan BrainLAB treatment planning system were compared: Pencil Beam Convolution (PBC) and commercial Monte Carlo (iMC). Seven left-sided breast patients submitted to breast-conserving surgery were enrolled in the study. For each patient, four RT techniques – f-IMRT, IMRT using 2-fields and 5-fields (IMRT2 and IMRT5, respectively) and DCART – were applied. The dose distributions in the planned target volume (PTV) and the dose to the organs at risk (OAR) were compared analyzing dose–volume histograms; further statistical analysis was performed using IBM SPSS v20 software. For PBC, all techniques provided adequate coverage of the PTV. However, statistically significant dose differences were observed between the techniques, in the PTV, OAR and also in the pattern of dose distribution spreading into normal tissues. IMRT5 and DCART spread low doses into greater volumes of normal tissue, right breast, right lung and heart than tangential techniques. However, IMRT5 plans improved distributions for the PTV, exhibiting better conformity and homogeneity in target and reduced high dose percentages in ipsilateral OAR. DCART did not present advantages over any of the techniques investigated. Differences were also found comparing the calculation algorithms: PBC estimated higher doses for the PTV, ipsilateral lung and heart than the iMC algorithm predicted.

Algoritmos evolutivos na determinação das máximas injeções nodais em redes de energia elétrica

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica - Ramo de Energia

Distributed MPC for Thermal Comfort and Load Allocation with Energy Auction

This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited shared energy resource using an integrated demand-side management approach that involves a power price auction and an appliance loads allocation scheme. The control objective for each subsystem (house or building) aims to minimize the energy cost while maintaining the indoor temperature inside comfort limits. In a distributed coordinated multi-agent ecosystem, each house or building control agent achieves its objectives while sharing, among them, the available energy through the introduction of particular coupling constraints in their underlying optimization problem. Coordination is maintained by a daily green energy auction bring in a demand-side management approach. Also the implemented distributed MPC algorithm is described and validated with simulation studies.

Correlation modeling for a distributed scalable video codec based on the HEVC standard

The growing heterogeneity of networks, devices and consumption conditions asks for flexible and adaptive video coding solutions. The compression power of the HEVC standard and the benefits of the distributed video coding paradigm allow designing novel scalable coding solutions with improved error robustness and low encoding complexity while still achieving competitive compression efficiency. In this context, this paper proposes a novel scalable video coding scheme using a HEVC Intra compliant base layer and a distributed coding approach in the enhancement layers (EL). This design inherits the HEVC compression efficiency while providing low encoding complexity at the enhancement layers. The temporal correlation is exploited at the decoder to create the EL side information (SI) residue, an estimation of the original residue. The EL encoder sends only the data that cannot be inferred at the decoder, thus exploiting the correlation between the original and SI residues; however, this correlation must be characterized with an accurate correlation model to obtain coding efficiency improvements. Therefore, this paper proposes a correlation modeling solution to be used at both encoder and decoder, without requiring a feedback channel. Experiments results confirm that the proposed scalable coding scheme has lower encoding complexity and provides BD-Rate savings up to 3.43% in comparison with the HEVC Intra scalable extension under development. © 2014 IEEE.

Enabling Global Experiments With Interactive Reconfiguration And Steering By Multiple Users

In global scientific experiments with collaborative scenarios involving multinational teams there are big challenges related to data access, namely data movements are precluded to other regions or Clouds due to the constraints on latency costs, data privacy and data ownership. Furthermore, each site is processing local data sets using specialized algorithms and producing intermediate results that are helpful as inputs to applications running on remote sites. This paper shows how to model such collaborative scenarios as a scientific workflow implemented with AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic), a decentralized framework offering a feasible solution to run the workflow activities on distributed data centers in different regions without the need of large data movements. The AWARD workflow activities are independently monitored and dynamically reconfigured and steering by different users, namely by hot-swapping the algorithms to enhance the computation results or by changing the workflow structure to support feedback dependencies where an activity receives feedback output from a successor activity. A real implementation of one practical scenario and its execution on multiple data centers of the Amazon Cloud is presented including experimental results with steering by multiple users.

Perceptually driven video error protection using a distributed source coding approach

In video communication systems, the video signals are typically compressed and sent to the decoder through an error-prone transmission channel that may corrupt the compressed signal, causing the degradation of the final decoded video quality. In this context, it is possible to enhance the error resilience of typical predictive video coding schemes using as inspiration principles and tools from an alternative video coding approach, the so-called Distributed Video Coding (DVC), based on the Distributed Source Coding (DSC) theory. Further improvements in the decoded video quality after error-prone transmission may also be obtained by considering the perceptual relevance of the video content, as distortions occurring in different regions of a picture have a different impact on the user's final experience. In this context, this paper proposes a Perceptually Driven Error Protection (PDEP) video coding solution that enhances the error resilience of a state-of-the-art H.264/AVC predictive video codec using DSC principles and perceptual considerations. To increase the H.264/AVC error resilience performance, the main technical novelties brought by the proposed video coding solution are: (i) design of an improved compressed domain perceptual classification mechanism; (ii) design of an improved transcoding tool for the DSC-based protection mechanism; and (iii) integration of a perceptual classification mechanism in an H.264/AVC compliant codec with a DSC-based error protection mechanism. The performance results obtained show that the proposed PDEP video codec provides a better performing alternative to traditional error protection video coding schemes, notably Forward Error Correction (FEC)-based schemes. (C) 2013 Elsevier B.V. All rights reserved.