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.

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.

On the requirements of interpolating polynomials for path motion constraints

In the framework of multibody dynamics, the path motion constraint enforces that a body follows a predefined curve being its rotations with respect to the curve moving frame also prescribed. The kinematic constraint formulation requires the evaluation of the fourth derivative of the curve with respect to its arc length. Regardless of the fact that higher order polynomials lead to unwanted curve oscillations, at least a fifth order polynomials is required to formulate this constraint. From the point of view of geometric control lower order polynomials are preferred. This work shows that for multibody dynamic formulations with dependent coordinates the use of cubic polynomials is possible, being the dynamic response similar to that obtained with higher order polynomials. The stabilization of the equations of motion, always required to control the constraint violations during long analysis periods due to the inherent numerical errors of the integration process, is enough to correct the error introduced by using a lower order polynomial interpolation and thus forfeiting the analytical requirement for higher order polynomials.

Improved matching criterion for frame rate upconversion with trilateral filtering

Frame rate upconversion (FRUC) is an important post-processing technique to enhance the visual quality of low frame rate video. A major, recent advance in this area is FRUC based on trilateral filtering which novelty mainly derives from the combination of an edge-based motion estimation block matching criterion with the trilateral filter. However, there is still room for improvement, notably towards reducing the size of the uncovered regions in the initial estimated frame, this means the estimated frame before trilateral filtering. In this context, proposed is an improved motion estimation block matching criterion where a combined luminance and edge error metric is weighted according to the motion vector components, notably to regularise the motion field. Experimental results confirm that significant improvements are achieved for the final interpolated frames, reaching PSNR gains up to 2.73 dB, on average, regarding recent alternative solutions, for video content with varied motion characteristics.

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.

Measuring and Controlling the Chaotic Motion of Profits

The study of economic systems has generated deep interest in exploring the complexity of chaotic motions in economy. Due to important developments in nonlinear dynamics, the last two decades have witnessed strong revival of interest in nonlinear endogenous business chaotic models. The inability to predict the behavior of dynamical systems in the presence of chaos suggests the application of chaos control methods, when we are more interested in obtaining regular behavior. In the present article, we study a specific economic model from the literature. More precisely, a system of three ordinary differential equations gather the variables of profits, reinvestments and financial flow of borrowings in the structure of a firm. Firstly, using results of symbolic dynamics, we characterize the topological entropy and the parameter space ordering of kneading sequences, associated with one-dimensional maps that reproduce significant aspects of the model dynamics. The analysis of the variation of this numerical invariant, in some realistic system parameter region, allows us to quantify and to distinguish different chaotic regimes. Finally, we show that complicated behavior arising from the chaotic firm model can be controlled without changing its original properties and the dynamics can be turned into the desired attracting time periodic motion (a stable steady state or into a regular cycle). The orbit stabilization is illustrated by the application of a feedback control technique initially developed by Romeiras et al. [1992]. This work provides another illustration of how our understanding of economic models can be enhanced by the theoretical and numerical investigation of nonlinear dynamical systems modeled by ordinary differential equations.

Evaluation of the respiratory motion effect in small animal PET images with GATE Monte Carlo simulations

The rapid growth in genetics and molecular biology combined with the development of techniques for genetically engineering small animals has led to increased interest in in vivo small animal imaging. Small animal imaging has been applied frequently to the imaging of small animals (mice and rats), which are ubiquitous in modeling human diseases and testing treatments. The use of PET in small animals allows the use of subjects as their own control, reducing the interanimal variability. This allows performing longitudinal studies on the same animal and improves the accuracy of biological models. However, small animal PET still suffers from several limitations. The amounts of radiotracers needed, limited scanner sensitivity, image resolution and image quantification issues, all could clearly benefit from additional research. Because nuclear medicine imaging deals with radioactive decay, the emission of radiation energy through photons and particles alongside with the detection of these quanta and particles in different materials make Monte Carlo method an important simulation tool in both nuclear medicine research and clinical practice. In order to optimize the quantitative use of PET in clinical practice, data- and image-processing methods are also a field of intense interest and development. The evaluation of such methods often relies on the use of simulated data and images since these offer control of the ground truth. Monte Carlo simulations are widely used for PET simulation since they take into account all the random processes involved in PET imaging, from the emission of the positron to the detection of the photons by the detectors. Simulation techniques have become an importance and indispensable complement to a wide range of problems that could not be addressed by experimental or analytical approaches.

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.

FMRI 3D registration based on Fourier space subsets using neural networks

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

Does diaphragmatic breathing technique really improve its range of motion? An objective assessment

Purpose - To verify the results of a diaphragmatic breathing technique (DBT) on diaphragmatic range of motion in healthy subjects. Methods - A total of 51 healthy subjects (10 male; 41 female), mean age 20 years old and a body mass index (BMI) ranging from 15.6 to 34.9 kg/m2, were enrolled in this study. Diaphragmatic range of motion was assessed by M-mode ultrasound imaging. Measurements were made before and after the DBT implementation in a standard protocol, based on 3 seconds of inspiration starting from a maximum expiration. Differences between assessments were analyzed by descriptive statistics and t-test (p < 0.05). Results - Mean value range of motion before DBT was 55.3 ± 13.4 mm and after DBT was 63.8 ± 13.2 mm showing a significant improvement of 8.5 ± 14.7 mm (p < 0.001). A strong correlation between the slope and the range of motion was found (r = 0.71, p < 0.001). Conclusions - Based on ultrasound measurements, it has been proved that DBT really contributes to a higher diaphragmatic range of motion. Future studies are needed in order to understand the influence of protocol parameters (e.g. inspiration time). Clinical implications - In the contest of evidence-based practice in physiotherapy, it has been showed by objective measurements that DBT improves the diaphragm range of motion, translating into a more efficient ventilatory function and thus can be used in clinical setting. To our knowledge this is the first study to assess the effects of DBT on range of motion of diaphragm muscle with ultrasound imaging.

A reliable M-mode ultrasound protocol for the assessment of diaphragm motion

Diaphragm is the principal inspiratory muscle. Different techniques have been used to assess diaphragm motion. Among them, M-mode ultrasound has gain particular interest since it is non-invasive and accessible. However it is operator-dependent and no objective acquisition protocol has been established. Purpose: to establish a reliable method for the assessment of the diaphragmatic motion via the M-mode ultrasound.