Compression Efficiency Analysis of Wyner-Ziv Video Coding with Motion Compensated Side Information Interpolation

The Wyner-Ziv video coding (WZVC) rate distortion performance is highly dependent on the quality of the side information, an estimation of the original frame, created at the decoder. This paper, characterizes the WZVC efficiency when motion compensated frame interpolation (MCFI) techniques are used to generate the side information, a difficult problem in WZVC especially because the decoder only has available some reference decoded frames. The proposed WZVC compression efficiency rate model relates the power spectral of the estimation error to the accuracy of the MCFI motion field. Then, some interesting conclusions may be derived related to the impact of the motion field smoothness and the correlation to the true motion trajectories on the compression performance.

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.

Adaptive deblocking filter for transform domain Wyner-Ziv video coding

Wyner-Ziv (WZ) video coding is a particular case of distributed video coding, the recent video coding paradigm based on the Slepian-Wolf and Wyner-Ziv theorems that exploits the source correlation at the decoder and not at the encoder as in predictive video coding. Although many improvements have been done over the last years, the performance of the state-of-the-art WZ video codecs still did not reach the performance of state-of-the-art predictive video codecs, especially for high and complex motion video content. This is also true in terms of subjective image quality mainly because of a considerable amount of blocking artefacts present in the decoded WZ video frames. This paper proposes an adaptive deblocking filter to improve both the subjective and objective qualities of the WZ frames in a transform domain WZ video codec. The proposed filter is an adaptation of the advanced deblocking filter defined in the H.264/AVC (advanced video coding) standard to a WZ video codec. The results obtained confirm the subjective quality improvement and objective quality gains that can go up to 0.63 dB in the overall for sequences with high motion content when large group of pictures are used.

Statistical motion learning for improved transform domain Wyner-Ziv video coding

Wyner - Ziv (WZ) video coding is a particular case of distributed video coding (DVC), the recent video coding paradigm based on the Slepian - Wolf and Wyner - Ziv theorems which exploits the source temporal correlation at the decoder and not at the encoder as in predictive video coding. Although some progress has been made in the last years, WZ video coding is still far from the compression performance of predictive video coding, especially for high and complex motion contents. The WZ video codec adopted in this study is based on a transform domain WZ video coding architecture with feedback channel-driven rate control, whose modules have been improved with some recent coding tools. This study proposes a novel motion learning approach to successively improve the rate-distortion (RD) performance of the WZ video codec as the decoding proceeds, making use of the already decoded transform bands to improve the decoding process for the remaining transform bands. The results obtained reveal gains up to 2.3 dB in the RD curves against the performance for the same codec without the proposed motion learning approach for high motion sequences and long group of pictures (GOP) sizes.

Application of neural networks for pavement evaluation

As vias de comunicação são indispensáveis para o desenvolvimento de uma nação, económica e socialmente. Num mundo globalizado, onde tudo deve chegar ao seu destino no menor espaço de tempo, as vias de comunicação assumem um papel vital. Assim, torna-se essencial construir e manter uma rede de transportes eficiente. Apesar de não ser o método mais eficiente, o transporte rodoviário é muitas vezes o mais económico e possibilita o transporte porta-a-porta, sendo em muitos casos o único meio de transporte possível. Por estas razões, o modo rodoviário tem uma quota significativa no mercado dos transportes, seja de passageiros ou mercadorias, tornando-o extremamente importante na rede de transportes de um país. Os países europeus fizeram um grande investimento na criação de extensas redes de estradas, cobrindo quase todo o seu território. Neste momento, começa-se a atingir o ponto onde a principal preocu+ação das entidades gestoras de estradas deixa de ser a construção de novas vias, passando a focar-se na necessidade de manutenção e conservação das vias existentes. Os pavimentos rodoviários, como todas as outras construções, requerem manutenção de forma a garantir bons níveis de serviço com qualidade, conforto e segurança. Devido aos custos inerentes às operações de manutenção de pavimentos, estas devem rigorosamente e com base em critérios científicos bem definidos. Assim, pretende-se evitar intervenções desnecessárias, mas também impedir que os danos se tornem irreparáveis e economicamente prejudiciais, com repercussões na segurança dos utilizadores. Para se estimar a vida útil de um pavimento é essencial realizar primeiro a caracterização estrutural do mesmo. Para isso, torna-se necessário conhecer o tipo de estrutura de um pavimento, nomeadamente a espessura e o módulo de elasticidade constituintes. A utilização de métodos de ensaio não destrutivos é cada vez mais reconhecida como uma forma eficaz para obter informações sobre o comportamento estrutural de pavimentos. Para efectuar estes ensaios, existem vários equipamentos. No entanto, dois deles, o Deflectómetro de Impacto e o Radar de Prospecção, têm demonstrado ser particularmente eficientes para avaliação da capacidade de carga de um pavimento, sendo estes equipamentos utilizados no âmbito deste estudo. Assim, para realização de ensaios de carga em pavimentos, o equipamento Deflectómetro de Impacto tem sido utilizado com sucesso para medir as deflexões à superfície de um pavimento em pontos pré-determinados quando sujeito a uma carga normalizada de forma a simular o efeito da passagem da roda de um camião. Complementarmente, para a obtenção de informações contínuas sobre a estrutura de um pavimento, o equipamento Radar de Prospecção permite conhecer o número de camadas e as suas espessuras através da utilização de ondas electromagnéticas. Os dados proporcionam, quando usados em conjunto com a realização de sondagens à rotação e poços em alguns locais, permitem uma caracterização mais precisa da condição estrutural de um pavimento e o estabelecimento de modelos de resposta, no caso de pavimentos existentes. Por outro lado, o processamento dos dados obtidos durante os ensaios “in situ” revela-se uma tarefa morosa e complexa. Actualmente, utilizando as espessuras das camadas do pavimento, os módulos de elasticidade das camadas são calculados através da “retro-análise” da bacia de deflexões medida nos ensaios de carga. Este método é iterativo, sendo que um engenheiro experiente testa várias estruturas diferentes de pavimento, até se obter uma estrutura cuja resposta seja o mais próximo possível da obtida durante os ensaios “in Situ”. Esta tarefa revela-se muito dependente da experiência do engenheiro, uma vez que as estruturas de pavimento a serem testadas maioritariamente do seu raciocínio. Outra desvantagem deste método é o facto de apresentar soluções múltiplas, dado que diferentes estruturas podem apresentar modelos de resposta iguais. A solução aceite é, muitas vezes, a que se julga mais provável, baseando-se novamente no raciocínio e experiência do engenheiro. A solução para o problema da enorme quantidade de dados a processar e das múltiplas soluções possíveis poderá ser a utilização de Redes Neuronais Artificiais (RNA) para auxiliar esta tarefa. As redes neuronais são elementos computacionais virtuais, cujo funcionamento é inspirado na forma como os sistemas nervosos biológicos, como o cérebro, processam a informação. Estes elementos são compostos por uma série de camadas, que por sua vez são compostas por neurónios. Durante a transmissão da informação entre neurónios, esta é modificada pela aplicação de um coeficiente, denominado “peso”. As redes neuronais apresentam uma habilidade muito útil, uma vez que são capazes de mapear uma função sem conhecer a sua fórmula matemática. Esta habilidade é utilizada em vários campos científicos como o reconhecimento de padrões, classificação ou compactação de dados. De forma a possibilitar o uso desta característica, a rede deverá ser devidamente “treinada” antes, processo realizado através da introdução de dois conjuntos de dados: os valores de entrada e os valores de saída pretendidos. Através de um processo cíclico de propagação da informação através das ligações entre neurónios, as redes ajustam-se gradualmente, apresentando melhores resultados. Apesar de existirem vários tipos de redes, as que aparentam ser as mais aptas para esta tarefa são as redes de retro-propagação. Estas possuem uma característica importante, nomeadamente o treino denominado “treino supervisionado”. Devido a este método de treino, as redes funcionam dentro da gama de variação dos dados fornecidos para o “treino” e, consequentemente, os resultados calculados também se encontram dentro da mesma gama, impedindo o aparecimento de soluções matemáticas com impossibilidade prática. De forma a tornar esta tarefa ainda mais simples, foi desenvolvido um programa de computador, NNPav, utilizando as RNA como parte integrante do seu processo de cálculo. O objectivo é tornar o processo de “retro-análise” totalmente automático e prevenir erros induzidos pela falta de experiência do utilizador. De forma a expandir ainda mais as funcionalidades do programa, foi implementado um processo de cálculo que realiza uma estimativa da capacidade de carga e da vida útil restante do pavimento, recorrendo a dois critérios de ruína. Estes critérios são normalmente utilizados no dimensionamento de pavimentos, de forma a prevenir o fendilhamento por fadiga e as deformações permanentes. Desta forma, o programa criado permite a estimativa da vida útil restante de um pavimento de forma eficiente, directamente a partir das deflexões e espessuras das camadas, medidas nos ensaios “in situ”. Todos os passos da caracterização estrutural do pavimento são efectuados pelo NNPav, seja recorrendo à utilização de redes neuronais ou a processos de cálculo matemático, incluindo a correcção do módulo de elasticidade da camada de misturas betuminosas para a temperatura de projecto e considerando as características de tráfego e taxas de crescimento do mesmo. Os testes efectuados às redes neuronais revelaram que foram alcançados resultados satisfatórios. Os níveis de erros na utilização de redes neuronais são semelhantes aos obtidos usando modelos de camadas linear-elásticas, excepto para o cálculo da vida útil com base num dos critérios, onde os erros obtidos foram mais altos. No entanto, este processo revela-se bastante mais rápido e possibilita o processamento dos dados por pessoal com menos experiência. Ao mesmo tempo, foi assegurado que nos ficheiros de resultados é possível analisar todos os dados calculados pelo programa, em várias fases de processamento de forma a permitir a análise detalhada dos mesmos. A possibilidade de estimar a capacidade de carga e a vida útil restante de um pavimento, contempladas no programa desenvolvido, representam também ferramentas importantes. Basicamente, o NNPav permite uma análise estrutural completa de um pavimento, estimando a sua vida útil com base nos ensaios de campo realizados pelo Deflectómetro de Impacto e pelo Radar de Prospecção, num único passo. Complementarmente, foi ainda desenvolvido e implementado no NNPav um módulo destinado ao dimensionamento de pavimentos novos. Este módulo permite que, dado um conjunto de estruturas de pavimento possíveis, seja estimada a capacidade de carga e a vida útil daquele pavimento. Este facto permite a análise de uma grande quantidade de estruturas de pavimento, e a fácil comparação dos resultados no ficheiro exportado. Apesar dos resultados obtidos neste trabalho serem bastante satisfatórios, os desenvolvimentos futuros na aplicação de Redes Neuronais na avaliação de pavimentos são ainda mais promissores. Uma vez que este trabalho foi limitado a uma moldura temporal inerente a um trabalho académico, a possibilidade de melhorar ainda mais a resposta das RNA fica em aberto. Apesar dos vários testes realizados às redes, de forma a obter as arquitecturas que apresentassem melhores resultados, as arquitecturas possíveis são virtualmente ilimitadas e pode ser uma área a aprofundar. As funcionalidades implementadas no programa foram as possíveis, dentro da moldura temporal referida, mas existem muitas funcionalidades a serem adicinadas ou expandidas, aumentando a funcionalidade do programa e a sua produtividade. Uma vez que esta é uma ferramenta que pode ser aplicada ao nível de gestão de redes rodoviárias, seria necessário estudar e desenvolver redes similares de forma a avaliar outros tipos de estruturas de pavimentos. Como conclusão final, apesar dos vários aspectos que podem, e devem ser melhorados, o programa desenvolvido provou ser uma ferramenta bastante útil e eficiente na avaliação estrutural de pavimentos com base em métodos de ensaio não destrutivos.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

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.

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.

Short-term wind power forecasting in Portugal by neural networks and wavelet transform

This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.

Scalable Unified Transform Architecture for Advanced Video Coding Embedded Systems

A novel high throughput and scalable unified architecture for the computation of the transform operations in video codecs for advanced standards is presented in this paper. This structure can be used as a hardware accelerator in modern embedded systems to efficiently compute all the two-dimensional 4 x 4 and 2 x 2 transforms of the H.264/AVC standard. Moreover, its highly flexible design and hardware efficiency allows it to be easily scaled in terms of performance and hardware cost to meet the specific requirements of any given video coding application. Experimental results obtained using a Xilinx Virtex-5 FPGA demonstrated the superior performance and hardware efficiency levels provided by the proposed structure, which presents a throughput per unit of area relatively higher than other similar recently published designs targeting the H.264/AVC standard. Such results also showed that, when integrated in a multi-core embedded system, this architecture provides speedup factors of about 120x concerning pure software implementations of the transform algorithms, therefore allowing the computation, in real-time, of all the above mentioned transforms for Ultra High Definition Video (UHDV) sequences (4,320 x 7,680 @ 30 fps).

Side information creation for efficient Wyner-Ziv video coding: Classifying and reviewing

Video coding technologies have played a major role in the explosion of large market digital video applications and services. In this context, the very popular MPEG-x and H-26x video coding standards adopted a predictive coding paradigm, where complex encoders exploit the data redundancy and irrelevancy to 'control' much simpler decoders. This codec paradigm fits well applications and services such as digital television and video storage where the decoder complexity is critical, but does not match well the requirements of emerging applications such as visual sensor networks where the encoder complexity is more critical. The Slepian Wolf and Wyner-Ziv theorems brought the possibility to develop the so-called Wyner-Ziv video codecs, following a different coding paradigm where it is the task of the decoder, and not anymore of the encoder, to (fully or partly) exploit the video redundancy. Theoretically, Wyner-Ziv video coding does not incur in any compression performance penalty regarding the more traditional predictive coding paradigm (at least for certain conditions). In the context of Wyner-Ziv video codecs, the so-called side information, which is a decoder estimate of the original frame to code, plays a critical role in the overall compression performance. For this reason, much research effort has been invested in the past decade to develop increasingly more efficient side information creation methods. This paper has the main objective to review and evaluate the available side information methods after proposing a classification taxonomy to guide this review, allowing to achieve more solid conclusions and better identify the next relevant research challenges. After classifying the side information creation methods into four classes, notably guess, try, hint and learn, the review of the most important techniques in each class and the evaluation of some of them leads to the important conclusion that the side information creation methods provide better rate-distortion (RD) performance depending on the amount of temporal correlation in each video sequence. It became also clear that the best available Wyner-Ziv video coding solutions are almost systematically based on the learn approach. The best solutions are already able to systematically outperform the H.264/AVC Intra, and also the H.264/AVC zero-motion standard solutions for specific types of content. (C) 2013 Elsevier B.V. All rights reserved.

Non-coding RNAs: multi-tasking molecules in the cell

In the last years it has become increasingly clear that the mammalian transcriptome is highly complex and includes a large number of small non-coding RNAs (sncRNAs) and long noncoding RNAs (lncRNAs). Here we review the biogenesis pathways of the three classes of sncRNAs, namely short interfering RNAs (siRNAs), microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs). These ncRNAs have been extensively studied and are involved in pathways leading to specific gene silencing and the protection of genomes against virus and transposons, for example. Also, lncRNAs have emerged as pivotal molecules for the transcriptional and post-transcriptional regulation of gene expression which is supported by their tissue-specific expression patterns, subcellular distribution, and developmental regulation. Therefore, we also focus our attention on their role in differentiation and development. SncRNAs and lncRNAs play critical roles in defining DNA methylation patterns, as well as chromatin remodeling thus having a substantial effect in epigenetics. The identification of some overlaps in their biogenesis pathways and functional roles raises the hypothesis that these molecules play concerted functions in vivo, creating complex regulatory networks where cooperation with regulatory proteins is necessary. We also highlighted the implications of biogenesis and gene expression deregulation of sncRNAs and lncRNAs in human diseases like cancer.

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.