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Enrollment in state-funded pre-K programs prior to kindergarten entry has become increasingly common. As each state develops its own model for pre-K, rigorous studies of the impacts of state-specific programs are needed. This study investigates impacts of the Arkansas Better Chance (ABC) initiative at kindergarten entry using a regression-discontinuity design. In this approach, study selection criteria are known and modeled, rather than simply comparing children who attended ABC with potentially dissimilar children who did not attend. Statistically significant impacts of ABC pre-K participation were found across three key academic domains related to children’s kindergarten readiness—vocabulary, mathematics, and print awareness skills. These results suggest that the ABC pre-K program is effective and thus that it provides a potential model for expansion of large-scale public pre-K initiatives in other states.

This paper explores a way to apply Item Response Theory (IRT), one of the popular statistical methodologies in measurement and psychometrics, to evaluate Financial Transmission Rights (FTR) paths in the U.S. electricity market. FTR is an energy derivative product to hedge congestion cost risks inherent in constrained transmission lines. In New England, with about 1200 pricing locations, the theoretical combinations of FTR paths amount to 1.4 million in prevailing flows alone. With capital constraints, it is imperative that FTR market participants build the capability to evaluate FTR paths to bid on. IRT provides a framework of how well tests work, and how individual items work on tests, estimating respondents&rsquo, latent abilities, and individual item parameters. IRT is utilized to analyze historical electricity data of 2019 for a daily congestion cost of eight customer load zones and one hub in the U.S., New England, for the evaluation of FTR paths. In the analysis, an item represents an FTR path, while item difficulty, item discrimination, and a latent trait variable for the path correspond to the path profitability, risk level, and daily congestion ability, respectively. This paper explores the experimental procedures by which IRT, a psychometric tool, may also be applicable in complex energy markets, providing a consistent and standardized analytical framework to address the issues of selection and prioritization among multiple opportunities. FTR path evaluation is conducted in three steps to determine bid priority paths in FTR auctions: parameter significance tests, ranking on path profitability and risk level, and weighting scores of individual rankings on the two criteria.

Various efforts are used to preserve American history including relying on formal education, distributing information (text, video or visual aids) on social channels, displaying artifacts in historical centers or more recently, virtual reality applications posted on a shared medium. However, many of the newly developed applications are designed specifically for dedicated hardware rather than for a broad audience, thus creating a barrier for disseminating cultural values. In this paper, we propose a web-based Augmented Reality (AR) application, namely PalmitoAR, which provides an intuitive way of observing one of the most significant historical Civil War battlefields, Palmito Ranch Battlefield located in Cameron County, Texas. The proposed AR application is designed to resurrect a series of events through (i) a printed map of Palmito Ranch with embedded markers that enables viewers to experience the battle without being present at the site, (ii) a mobile device with a WebGL supported browser that allows 3D contents to be rendered, and (iii) an AR library (A-Frame.io) that enables enthusiasts to recreate similar work. Our methodology strongly relies on the benefits of a simple, robust algorithm for AR marker recognition to position 3D models in a specific context and time. As a result, the proposed AR application is complementary to existing work and provides a seamless experience for a wide range of viewers. We evaluated and improved the application with the help of twenty-six users to gather perspectives on the specific benefits of employing AR in learning about battlefields and reenactment. The technology acceptance model was adapted to access an individual&rsquo, s acceptance of information technology.

Supplemental material, ERO766291_Online_Appendix_ for State Prekindergarten Effects on Early Learning at Kindergarten Entry: An Analysis of Eight State Programs by W. Steven Barnett, Kwanghee Jung, Allison Friedman-Krauss, Ellen C. Frede, Milagros Nores, Jason T. Hustedt, Carollee Howes and Marijata Daniel-Echols in AERA Open

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

The Psychotic Symptom Rating Scales (PSYRATS) is an instrument designed to quantify the severity of delusions and hallucinations and is typically used in research studies and clinical settings focusing on people with psychosis and schizophrenia. It is comprised of the auditory hallucinations (AHS) and delusions subscales (DS), but these subscales do not necessarily reflect the psychological constructs causing intercorrelation between clusters of scale items. Identification of these constructs is important in some clinical and research contexts because item clustering may be caused by underlying etiological processes of interest. Previous attempts to identify these constructs have produced conflicting results. In this study, we compiled PSYRATS data from 12 sites in 7 countries, comprising 711 participants for AHS and 520 for DS. We compared previously proposed and novel models of underlying constructs using structural equation modeling. For the AHS, a novel 4-dimensional model provided the best fit, with latent variables labeled Distress (negative content, distress, and control), Frequency (frequency, duration, and disruption), Attribution (location and origin of voices), and Loudness (loudness item only). For the DS, a 2-dimensional solution was confirmed, with latent variables labeled Distress (amount/intensity) and Frequency (preoccupation, conviction, and disruption). The within-AHS and within-DS dimension intercorrelations were higher than those between subscales, with the exception of the AHS and DS Distress dimensions, which produced a correlation that approached the range of the within-scale correlations. Recommendations are provided for integrating these underlying constructs into research and clinical applications of the PSYRATS.

Refractory psychosis units currently have little information regarding which symptoms profiles should be expected to respond to treatment. In the current study, we provide this information using structural equation modeling of Positive and Negative Syndrome Scale (PANSS) ratings at admission and discharge on a sample of 610 patients admitted to a treatment refractory psychosis program at a Canadian tertiary care unit between 1990 and 2011. The hypothesized five-dimensional structure of the PANSS fit the data well at both admission and discharge, and the latent variable scores are reported as a function of symptom dimension and diagnostic category. The results suggest that, overall, positive symptoms (POS) responded to treatment better than all other symptoms dimensions, but for the schizoaffective and bipolar groups, greater response on POS was observed relative to the schizophrenia and major depression groups. The major depression group showed the most improvement on negative symptoms and emotional distress, and the bipolar group showed the most improvement on disorganization. Schizophrenia was distinct from schizoaffective disorder in showing reduced treatment response on all symptom dimensions. These results can assist refractory psychosis units by providing information on how PANSS symptom dimensions respond to treatment and how this depends on diagnostic category.

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also incorporates direct and modulating effects of input variables on specific latent variables and on connections between latent variables, respectively. An alternating least square (ALS) algorithm is developed for parameter estimation. An improved bootstrap method called a modified moving block bootstrap method is used to assess reliability of parameter estimates, which deals with time dependence between consecutive observations effectively. We analyze synthetic and real data to illustrate the feasibility of the proposed method.

Multiple-set canonical correlation analysis and principal components analysis are popular data reduction techniques in various fields, including psychology. Both techniques aim to extract a series of weighted composites or components of observed variables for the purpose of data reduction. However, their objectives of performing data reduction are different. Multiple-set canonical correlation analysis focuses on describing the association among several sets of variables through data reduction, whereas principal components analysis concentrates on explaining the maximum variance of a single set of variables. In this paper, we provide a unified framework that combines these seemingly incompatible techniques. The proposed approach embraces the two techniques as special cases. More importantly, it permits a compromise between the techniques in yielding solutions. For instance, we may obtain components in such a way that they maximize the association among multiple data sets, while also accounting for the variance of each data set. We develop a single optimization function for parameter estimation, which is a weighted sum of two criteria for multiple-set canonical correlation analysis and principal components analysis. We minimize this function analytically. We conduct simulation studies to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of functional neuroimaging data to illustrate its empirical usefulness.

We propose functional multiple-set canonical correlation analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical correlation analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical correlation analysis, computationally, the method solves a matrix eigen-analysis problem through the adoption of a basis expansion approach to approximating data and weight functions. We apply the proposed method to functional magnetic resonance imaging (fMRI) data to identify networks of neural activity that are commonly activated across subjects while carrying out a working memory task.

This paper introduces an development of high-definition(HD) 3DTV experimental broadcasting system and its experimental service that has been launched via three broadcasting network including terrestrial, cable and satellite in the late of 2010. This 3DTV experimental broadcasting service employs an innovative 3DTV broadcasting mechanism, called as dual stream method in which left and right images are encoded by using each video encoder and multiplexed using MPEG-2 system technology for guaranteeing backward-compatibility with legacy DTV. This paper specifically describes the developed experimental broadcasting system, concentrating on 3DTV re-multiplexer and 3DTV receiver. Finally, implementation and terrestrial 3DTV experimental broadcasting service results will be introduced from the point of objective and subjective evaluation.

The growth curve model (GCM), also known as GMANOVA, is a useful technique for investigating patterns of change in repeated measurement data over time and examining the effects of predictor variables on temporal trajectories. The reduced rank feature had been introduced previously to GCM for capturing redundant information in the criterion variables in a parsimonious way. In this paper, a ridge type of regularization was incorporated to obtain better estimates of parameters. Separate ridge parameters were allowed in column and row regressions, and the generalized singular value decomposition (GSVD) was applied for rank reduction. It was shown that the regularized estimates of parameters could be obtained in closed form for fixed values of ridge parameters. Permutation tests were used to identify the best dimensionality in the solution, and the K-fold cross validation method was used to choose optimal values of the ridge parameters. A bootstrap method was used to assess the reliability of parameter estimates. The proposed model was further extended to a mixture of GMANOVA and MANOVA. Illustrative examples were given to demonstrate the usefulness of the proposed method.

State-funded prekindergarten (preK) programs are increasingly common across the country. This study estimated the effects of eight state-funded preK programs (Arkansas, California, Michigan, New Jersey, New Mexico, Oklahoma, South Carolina, and West Virginia) on children’s learning using a regression discontinuity design. These programs vary with respect to the population served, program design, and context. Weighted average effect sizes from instrumental variables analyses across these states are 0.24 for language (vocabulary), 0.44 for math, and 1.10 for emergent literacy. Differences in effect sizes by domain suggest that preK programs should attend more to enhancing learning beyond simple literacy skills. State preK programs appear to differ in their effects. We offer recommendations for more rigorous, regular evaluation.

INDSCAL (INdividual Differences SCALing) is a useful technique for investigating both common and unique aspects of K similarity data matrices. The model postulates a common stimulus configuration in a low-dimensional Euclidean space, while representing differences among the K data matrices by differential weighting of dimensions by different data sources. Since Carroll and Chang proposed their algorithm for INDSCAL, several issues have been raised: non-symmetric solutions, negative saliency weights, and the degeneracy problem. Orthogonal INDSCAL (O-INDSCAL) which imposes orthogonality constraints on the matrix of stimulus configuration has been proposed to overcome some of these difficulties. Two algorithms have been proposed for O-INDSCAL, one by Ten Berge, Knol, and Kiers, and the other by Trendafilov. In this paper, an acceleration technique called minimal polynomial extrapolation is incorporated in Ten Berge et al.’s algorithm. Simulation studies are conducted to compare the performance of the three algorithms (Ten Berge et al.’s original algorithm, the accelerated algorithm, and Trendafilov’s). Possible extensions of the accelerated algorithm to similar situations are also suggested.

In this paper, we present a depth-image-based rendering (DIBR) technique for three-dimensional television (3DTV) service over terrestrial-digital multimedia broadcasting (T-DMB). 3DTV service over T-DMB is very attractive because the single user environment of T-DMB is suitable to glassless 3D viewing. However, the bit budget for the transmission of additional data in 3DTV service is very limited in T-DMB. DIBR can resolve this problem because the depth information as the additional data can be compressed effectively. However, DIBR has also some problems such as the backward compatibility with the conventional T-DMB, a large computational cost for rendering virtual images and generation of holes by disocclusion. To solve these problems, we first present the service architecture for DIBR-based 3DTV service over T-DMB, which can maintain the backward compatibility with the conventional T-DMB. Second, we propose a new depth preprocessing method based on adaptive smoothing. In the proposed depth preprocessing method, two adaptive smoothing filters of the discontinuity-preserving smoothing and the gradient direction-based smoothing are sequentially conducted. As a result, both the reduction of bitrates required for the depth transmission and the reduction of the holes can be achieved. Finally, we present look-up table (LUT)-based simultaneous method for generating an auto-stereoscopic image so that problems of the limited memory and the large computational cost can be resolved in T-DMB player. Various experiments show that the proposed DIBR technique can be efficiently employed for 3DTV service over T-DMB.

The effectiveness of the Tools of the Mind (Tools) curriculum in improving the education of 3- and 4-year-old children was evaluated by means of a randomized trial. The Tools curriculum, based on the work of Vygotsky, focuses on the development of self-regulation at the same time as teaching literacy and mathematics skills in a way that is socially mediated by peers and teachers and with a focus on play. The control group experienced an established district-created model described as a “balanced literacy curriculum with themes.” Teachers and students were randomly assigned to either treatment or control classrooms. Children (88 Tools and 122 control) were compared on social behavior, language, and literacy growth. The Tools curriculum was found to improve classroom quality and children's executive function as indicated by lower scores on a problem behavior scale. There were indications that Tools also improved children's language development, but these effects were smaller and did not reach conventional levels of statistical significance in multi-level models or after adjustments for multiple comparisons. Our findings indicate that a developmentally appropriate curriculum with a strong emphasis on play can enhance learning and development so as to improve both the social and academic success of young children. Moreover, it is suggested that to the extent child care commonly increases behavior problems this outcome may be reversed through the use of more appropriate curricula that actually enhance self-regulation.

Since 1980, the number of state pre-kindergarten (pre-K) programs has more than doubled, with 38 states enrolling more than one million children in 2006 alone. This study evaluates how five state pre-K programs affected children's receptive vocabulary, math, and print awareness skills. Taking advantage of states' strict enrollment policies determined by a child's date of birth, a regression-discontinuity design was used to estimate effects in Michigan, New Jersey, Oklahoma, South Carolina, and West Virginia. For receptive vocabulary, only New Jersey and Oklahoma yielded significant standardized impacts, though two of the three other coefficients were in a direction indicating positive effects. For math, all the coefficients were positive but only Michigan and New Jersey yielded reliable results. The largest impacts were for print awareness, where all five coefficients were positive and four were reliable in Michigan, New Jersey, South Carolina, and West Virginia. The five states were not randomly selected and, on average, have higher quality program standards than non-studied states, precluding formal extrapolation to the nation at large. However, our sample of states differed in many other ways, permitting the conclusion that state pre-K programs can have positive effects on children's cognitive skills, though the magnitude of these effects varies by state and outcome. © 2008 by the Association for Public Policy Analysis and Management.

An experimental study was conducted comparing the effects of dual language, or two-way immersion (TWI) and monolingual English immersion (EI) preschool education programs on children's learning. Three-and four-year old children were randomly assigned by lottery to either a newly established TWI Spanish/English program or a monolingual English program in the same district. Children in the study were from both Spanish and English home language backgrounds. All classrooms in the study used the High/Scope curriculum, and all met high standards for teacher qualifications, ratio, and class size. The TWI program alternated between English and Spanish weekly by rotating children between two classrooms (and teachers) each week. Programs were compared on measures of children's growth in language, emergent literacy, and mathematics. Children in both types of classrooms experienced substantial gains in language, literacy, and mathematics. No significant differences between treatment groups were found on English language measures. Among the native Spanish speakers, the TWI program produced large gains in Spanish vocabulary compared to the EI program. Both TWI and EI approaches boosted the learning and development of children including ELL students, as judged by standard score gains. TWI also improved the Spanish language development of English language learners (ELL) and native English speaking children without losses in English language learning.

Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

This article is a revision of the previous edition article by J. de Leeuw, volume 20, pp. 13512–13519, © 2001, Elsevier Ltd.

International audience; Background: Task-based functional neuroimaging studies of schizophrenia have not yet replicated the increased coordinated hyperactivity in speech-related brain regions that is reported with symptom-capture and resting-state studies of hallucinations. This may be due to suboptimal selection of cognitive tasks. Methods: In the current study, we used a task that allowed experimental manipulation of control over verbal material and compared brain activity between 23 schizophrenia patients (10 hallucinators, 13 nonhallucinators), 22 psychiatric (bipolar), and 27 healthy controls. Two conditions were presented, one involving inner verbal thought (in which control over verbal material was required) and another involving speech perception (SP; in which control verbal material was not required). Results: A functional connectivity analysis resulted in a left-dominant temporal-frontal network that included speech-related auditory and motor regions and showed hypercoupling in past-week hallucinating schizophrenia patients (relative to nonhallucinating patients) during SP only. Conclusions: These findings replicate our previous work showing generalized speech-related functional network hypercoupling in schizophrenia during inner verbal thought and SP, but extend them by suggesting that hypercoupling is related to past-week hallucination severity scores during SP only, when control over verbal material is not required. This result opens the possibility that practicing control over inner verbal thought processes may decrease the likelihood or severity of hallucinations.

Multiple-set canonical correlation analysis and principal components analysis are popular data reduction techniques in various fields, including psychology. Both techniques aim to extract a series of weighted composites or components of observed variables for the purpose of data reduction. However, their objectives of performing data reduction are different. Multiple-set canonical correlation analysis focuses on describing the association among several sets of variables through data reduction, whereas principal components analysis concentrates on explaining the maximum variance of a single set of variables. In this paper, we provide a unified framework that combines these seemingly incompatible techniques. The proposed approach embraces the two techniques as special cases. More importantly, it permits a compromise between the techniques in yielding solutions. For instance, we may obtain components in such a way that they maximize the association among multiple data sets, while also accounting for the variance of each data set. We develop a single optimization function for parameter estimation, which is a weighted sum of two criteria for multiple-set canonical correlation analysis and principal components analysis. We minimize this function analytically. We conduct simulation studies to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of functional neuroimaging data to illustrate its empirical usefulness.

In this paper, we introduce a stereoscopic video broadcasting system using repeated-content. By using the repeated-content, the proposed system provides high quality stereoscopic video service and has full backward compatibility with legacy digital television service in the same channel capacity. In our experiment, we have verified the feasibility of the proposed system based on legacy broadcasting platforms.

This paper presents a three-dimensional television (3DTV) trial broadcasting service that has been recently launched in Korea. The 3DTV trial broadcasting service employs a delivery mechanism of 3D content, called as dual stream method that delivers left and right images over each encoded stream based on MPEG-2 system technology, guaranteeing backward-compatibility. Specifically, this dual stream method is regarded as a proper solution for providing higher image quality both 2D and 3D video. This paper also presents a service mechanism along with signaling method, which enables 2D and 3D service to broadcast in a time-scheduled manner. Finally, we summarize the results of high-definition (HD) 3D trial broadcasting service.

INTRODUCTION This study investigates the educational effects of a state-funded prekindergarten education program for children at ages 3 and 4 that came about as a result of a court order. As part of the landmark New Jersey Supreme Court school-funding case, Abbott v. Burke , the court established the Abbott Preschool Program. Beginning in the 1999–2000 school year, 3- and 4-year-old children in the highest poverty districts in the state were able to receive a high-quality preschool education that would prepare them to enter school with the knowledge and skills necessary to meet the New Jersey Preschool Teaching and Learning Expectations: Standards of Quality (New Jersey Department of Education [NJDOE], 2004b) and the New Jersey Kindergarten Core Curriculum Content Standards (NJDOE, 2004a). Through a New Jersey Department of Education (DOE) and Department of Human Services (DHS) partnership, Abbott preschool classrooms combine a DOE-funded 6-hour, 180-day component with a DHS-funded wrap-around program that provides daily before- and after-care and summer programs. In total, the full-day, full-year program is available 10 hours per day, 245 days a year. Enrollment in the Abbott Preschool Program has increased dramatically since its inception in 1999 (see Fig. 11.1). During the 2005–2006 school year, its seventh year, the 31 Abbott districts served more than 40,500 3- and 4-year-old children in preschool – 78% of a possible 52,160 children. The enrollment for the 2006–2007 school year was 39,678 children with a DOE budget of almost $500 million.

In this paper, we present a new caption insertion method for three-dimensional (3D) broadcasting service. Caption insertion technique for broadcasting service is very important because the caption emphasizes significant information during a broadcasting program and can help the hearing impairment people. In recent years, some caption insertion method has been proposed for 3D broadcasting called next generation broadcasting service. However, recently proposed caption insertion methods possess problems such as the visual discomfort during the disparity of 3D caption. To solve this problem, we propose a new caption insertion method by using histogram based K-means clustering based on depth image based rendering (DIBR). Through the proposed method, visual discomfort during the disparity of 3D caption can be relieved. Furthermore, proposed method provides selectivity of caption disparity for various user viewing environments by using clustering technique. Experimental results show the contents with the proposed 3D caption insertion method.

This paper presents a 3D (three dimensional) mobile broadcasting service based on depth-image-based rendering (DIBR) technique in terrestrial digital multimedia broadcasting (T-DMB). In designing and developing a 3D visual service based on mobile broadcasting system, we must consider system requirements such as the minimization of additional bit-rates for 3D depth information due to the limitation of transmission channel bandwidth, the assurance of backward compatibility with existing T-DMB, and the maximization of 3D effect while reducing eye fatigue. Therefore, the 3D mobile broadcasting service based on DIBR technique can be one of the solutions to meet such requirements because the allocated bit-rates of depth image with DIBR scheme is less than additional video bit-rates of another 3D format, while keeping 3D quality and guaranteeing backward-compatibility with T-DMB. In this paper, we introduce an implementation of DIBR-based 3D T-DMB system that supports the real-time rendering with good image quality and depth effect at the receiver, verifying that it could be available in the mobile broadcasting. The verification is achieved through objective and subjective evaluation, based on the simulation and implementation of the system. Finally, we will confirm that DIBR-based 3D mobile broadcasting service would be commercialized in near future.

In this work, we demonstrate a 3D DMB player which provides users with realistic 3D services over terrestrial-digital multimedia broadcasting (T-DMB). Since the bit-budget for the transmission of additional data stream is strictly limited in the current state of T-DMB, the 3D services are based on depth-image-based rendering (DIBR) that the depth information as the additional data can be compressed efficiently. This demonstration presents a depth preprocessing procedure for reducing holes (disocclusion area) in transmitters and a fast rendering procedure using look-up-table (LUT) in receivers. By means of the 3D services, viewers can consumes realistic and exciting 3D contents in mobile 3D DMB players.

In this paper, we present a depth image based rendering technique for 3D data service over T-DMB. 3D service over T-DMB is very attractive because the single user environment of T-DMB is suitable to glassless 3D viewing. However, the bit budget for depth transmission over T-DMB is very limited with 32 Kbps for data service. Depth image based rendering can resolve this problem because the corresponding depth sequence can be compressed effectively. However, depth image based rendering has also some problems such as a large computational cost for generating virtual images and generation of holes by disocclusion. Therefore, we propose the new depth preprocessing method based on adaptive smoothing and the simultaneous method for generating an auto-stereoscopic image. In the proposed depth preprocessing method, the discontinuity preserving smoothing, followed by the adaptive smoothing based on gradient direction, is conducted by generalizing the conduction function of the anisotropic smoothing. As a result, the reduction of both bit rate required for transmission and holes is achieved. Additionally, the simultaneous method for generating an auto-stereoscopic image can resolve problems of limited memory and large computational complexity. Some experiments show that the proposed scheme can be efficiently employed for 3D data service over T-DMB.

In this paper, we introduce a 2D/3D mixed service in terrestrial digital multimedia broadcasting (T-DMB) system using depth-image-based rendering (DIBR). The 2D/3D mixed service is the 3D service type that 3D contents are shown partially while a 2D video sequence is displayed in the entire screen, or vice versa. This service is very attractive because partial display of 3D contents can maximize the effect of 3D services and can reduce the viewer's fatigue while watching a 3D display. Especially, we can provide more flexible 3D services with a low amount of data by using DIBR. Moreover, 3D contents can be manipulated efficiently by XML-based metadata. The proposed 2D/3D mixed service is tested using various contents in the 3D display with parallax barrier. The proposed 2D/3D mixed service can be employed efficiently as the additional data services in conventional T-DMB structure.