Search

Fiducial marker-based tracking is an effective method for pose estimation in coffined environments, such as the International Thermonuclear Experimental Reactor. In this paper, we propose a novel framework for marker detection and identification that is moderately robust to occlusion, even while using a relatively small number of marks. The proposed approach (ITER-Tag) consists of a hybrid pipeline that extracts marker candidates from images using classical methods and identifies uncoded markers using a shallow feed forward neural network. The method requires minimal parameter tuning, data collection and annotation. The methods can be easily adapted to different use cases with varying number and positions of the marks. We test the robustness of our approach in three different use cases in ITER’s divertor, using either retro reflective markers or laser engravings and achieve high detectability rates. We demonstrate how the proposed approach can be used to accurately and robustly retrieve the six-degree-of-freedom pose of the targets. acceptedVersion

We present a computational accommodation-invariant near-eye display, which relies on imaging with coherent light and utilizes static optics together with convolutional neural network-based preprocessing. The network and the display optics are co-optimized to obtain a depth-invariant display point spread function, and thus relieve the conflict between accommodation and ocular vergence cues that typically exists in conventional near-eye displays. We demonstrate through simulations that the computational near-eye display designed based on the proposed approach can deliver sharp images within a depth range of 3 diopters for an effective aperture (eyepiece) size of 10 mm. Thus, it provides a competitive alternative to the existing accommodation-invariant displays. acceptedVersion

We address the problem of 3D scene rendering on projection-based light field displays and optimizing the input display images to obtain the best possible visual output. We discuss a display model comprising a set of projectors, an anisotropic diffuser and a viewing manifold. Based on this model, we render an initial set of projector images to be further optimized for the best perception at a specified set of viewing positions. We propose a least squares method, which minimizes the channel-wise color difference between the generated images for different viewer positions, and their ground-true counterparts. We formulate a constrained optimization problem and solve it iteratively by the descent method. acceptedVersion

We address the problem of 3D scene rendering on near-eye integral imaging displays and evaluation of different rendering methods in terms of human perception. We compare three rendering techniques in terms of perceived spatial resolution at different focused depths, simulating the display in virtual environment and representing the eye through a thin-lens camera model. acceptedVersion

We present a multifocal computational near-eye display that employs a static diffractive optical element (DOE) in tandem with a refractive lens. The DOE is co-optimized with the con-volutional neural network-based preprocessing to achieve desired multifocal display point spread function in an optimal manner. In the simulations, we demonstrate a multifocal display that can deliver sharp images for three distinct depths sampling the dioptric depth range uniformly from 3 diopters to infinity. acceptedVersion

We present an accommodation-invariant computational neareye display based on the extended depth of field imaging. The eyepiece of the display consists of a diffractive optical element (DOE) that is used in tandem with a conventional refractive lens. The DOE is co-designed with the pre-processing convolutional neural network, which is analogous to the post-processing deblurring networks in image capture. We demonstrate through simulations that such system achieves accommodation-invariant imaging within 2 Diopters depth range without significantly sacrificing spatial resolution. acceptedVersion

Light fields representing Lambertian scenes are characterized by specific epipolar plane images, which support in Fourier domain is limited by the minimum and maximum scene depth. This feature of the light field has been taken into account when designing the shearlet-based light field reconstruction algorithm. However, this feature does not hold for the case of non-Lambertian scenes. Therefore, the original algorithm performs sub-optimally for such scenes. In this work, we extend the shearlet based reconstruction algorithm to enable light fields reconstruction for scenes composed of surfaces with non-Lambertian properties. Instead of assuming Lambertianity and the corresponding Fourier-domain support, we estimate the real Fourier-domain support of the given light field of a non-Lambertian scene. Based on the estimated support, we adapt the Shearlet frame to properly span that support. The results demonstrate that the new method yields a faithful reconstruction of densely sampled light fields of non-Lambertian scenes from a small number of captured views. acceptedVersion

We propose simple and efficient method that produces content-adaptive superpixels, i.e. smaller segments in content-dense areas and larger segments in content-sparse areas. Previous adaptive methods distribute superpixels over the image according to image content. In contrast, we transform the image itself to redistribute the content density uniformly across the image area. This transformation is guided by a significance map, which characterizes the ‘importance’ of each pixel. Arbitrary superpixel algorithm can be utilized to segment the transformed image into regular superpixels, providing a suitable representation for subsequent tasks. Regular superpixels in the transformed image induce content-adaptive superpixels in the original image facilitating the improved segmentation accuracy. acceptedVersion

Calibration of multiple cameras is a critical step in most vision enhancement systems. Target-based calibration approaches are known to provide accurate and stable results. However, they require manually performed capture procedures. This paper presents a generalization of a widely used single-camera target-based calibration algorithm to the case of n cameras. In order to obtain fully repeatable results, we propose the elimination of the manual capture step using a programmable robotic arm. Furthermore, we investigate the use of the position feedback provided by the robot. This is done specifically for the case of calibrating cameras without assumptions on their positions and overlapping of their fields of view. Results show that automatically captured images provide more accurate calibration results than the classical approach. Additionally, calibration of fully non-overlapping setups is made possible through our approach. acceptedVersion

Shearlet Transform (ST) is one of the most effective methods for Densely-Sampled Light Field (DSLF) reconstruction from a Sparsely-Sampled Light Field (SSLF). However, ST requires a precise disparity estimation of the SSLF. To this end, in this paper a state-of-the-art optical flow method, i.e. PWC-Net, is employed to estimate bidirectional disparity maps between neighboring views in the SSLF. Moreover, to take full advantage of optical flow and ST for DSLF reconstruction, a novel learning-based method, referred to as Flow-Assisted Shearlet Transform (FAST), is proposed in this paper. Specifically, FAST consists of two deep convolutional neural networks, i.e. disparity refinement network and view synthesis network, which fully leverage the disparity information to synthesize novel views via warping and blending and to improve the novel view synthesis performance of ST. Experimental results demonstrate the superiority of the proposed FAST method over the other state-of-the-art DSLF reconstruction methods on nine challenging real-world SSLF sub-datasets with large disparity ranges (up to 26 pixels). acceptedVersion

We present a learning-based optimization framework for depth of field extension, combining rigorous modeling of coded aperture imaging system and convolutional neural network based deblurring. The coded mask discretization is defined for desired depth range using wave optics based imaging model. Such approach significantly decreases the number of parameters to be optimized and increases the convergence speed of the network. We verify the proposed algorithm in different scenarios achieving superior or comparable performance with respect to existing methods. acceptedVersion

Shearlet Transform (ST) is one of the most effective approaches for light field reconstruction from Sparsely-Sampled Light Fields (SSLFs). This demo paper presents a comprehensive implementation of ST for light field reconstruction using one of the most popular machine learning libraries, i.e. Tensor Flow. The flexible architecture of TensorFlow allows for the easy deployment of ST across different platforms (CPUs, GPUs, TPUs) running varying operating systems with high efficiency and accuracy. acceptedVersion

Shearlet Transform (ST) is one of the most effective algorithms for the Densely-Sampled Light Field (DSLF) reconstruction from a Sparsely-Sampled Light Field (SSLF) with a large disparity range. However, ST requires a precise estimation of the disparity range of the SSLF in order to design a shearlet system with decent scales and to pre-shear the sparsely-sampled Epipolar-Plane Images (EPIs) of the SSLF. To overcome this limitation, a novel coarse-to-fine DSLF reconstruction method, referred to as Mask-Accelerated Shearlet Transform (MAST), is proposed in this paper. Specifically, a state-of-the-art learning-based optical flow method, FlowNet2, is employed to estimate the disparities of a SSLF. The estimated disparities are then utilized to roughly estimate the densely-sampled EPIs for the sparsely-sampled EPIs of the SSLF. Finally, an elaborately-designed soft mask for a coarsely-inpainted EPI is exploited to perform an iterative refinement on this EPI. Experimental results on nine challenging horizontal-parallax real-world SSLF datasets with large disparity ranges (up to 35 pixels) demonstrate the effectiveness and efficiency of the proposed method over the other state-of-the-art approaches. acceptedVersion

The simplicity of the holographic stereogram (HS) makes it an attractive option in comparison to the more complex coherent computer generated hologram (CGH) methods. The cost of its simplicity is that the HS cannot accurately reconstruct deep scenes due to the lack of correct accommodation cues. The exact nature of the accommodation cues present in HSs, however, has not been investigated. In this paper, we provide analysis of the relation between the hologram sampling properties and the perceived accommodation response. The HS can be considered as a generator of a discrete light field (LF) and can thus be examined by considering the light ray oriented nature of the hologram diffracted light. We further support the analysis by employing a numerical reconstruction tool simulating the viewing process of the human eye. The simulation results demonstrate that HSs can provide accommodation cues depending on the choice of hologram segmentation size. It is further demonstrated that the accommodation response can be enhanced at the expense of loss in perceived spatial resolution. acceptedVersion

We present an accurate model of integral imaging display based on wave optics. The model enables accurate characterization of the display through simulated perceived images by the human visual system. Thus, it is useful to investigate the capabilities of the display in terms of various quality factors such as depth of field and resolution, as well as delivering visual cues such as focus. Furthermore, due to the adopted wave optics formalism, simulation and analysis of more advanced techniques such as wavefront coding for increased depth of field are also possible.

We propose an improved depth compression scheme which relies on depth decimation guided by super-pixel segmentation of the aligned color data. We modify the latter to ensure border congruency of segmenation refinement levels. Furthermore, a modification of our multi-modal regularized reconstruction is presented. We address also the problem of possible misalignments between color and depth maps. Such misalignments produce edge outliers which mislead the error optimization in the coding process. We propose an efficient encoding scheme of such outliers in so called “yieldflow” protocol. We compare our new and imporved method against a number of state-of-art approaches and demonstrate that it performs favorably especially in the low bit rate region.

Plenoptic cameras can capture the light field of a 3D scene in a single shot, which makes them attractive for several applications, such as depth estimation and refocusing. The difficulties in accurate calibration of available plenoptic camera designs, however, makes it also difficult to reliably assess such applications. This arises a need to have a ground-truth plenoptic data. We propose an accurate and efficient way to simulate the defocused plenoptic camera based on the geometric optics principles and the concept of densely sampled light field. In particular, we utilize the open-source computer graphics rendering software tool Blender and rely on a set of conventional 2D pinhole images of the scene captured from several viewpoints within the aperture of the main lens of the plenoptic camera. Elemental-image wise examination of plenoptic data and testing of post processing algorithms verifies the accuracy of the simulation.

The paper proposes a method for lossless compression of subaperture image stacks obtained by rectifying light-field images captured by a plenoptic camera. We exploit the similarities between two subaperture images using a predictive coding algorithm, where the current view is predicted from one reference view. Context modeling is the main technique used to reduce the image file size. A suitable image segmentation and a template context are used by the context tree algorithm for encoding up to the smallest detail in each subaperture image. Entropy coding is configured by a residual analysis module. The results show improved performance compared to the state-of-the-art encoders.

Holographic stereograms (HSs) constitute one of the most widely used types of computer-generated holograms. The scene information required to calculate the HSs can be acquired by conventional digital cameras. It is, however, usually required that the scene should be captured from dense set of view points. Therefore, relieving this requirement is critical in the sense of easing the capture process. In this paper, in the capture stage of holographic stereograms, we employ our previously presented light field reconstruction algorithm [1], where we utilize sparse representation of light fields in the shearlet domain and reconstruct dense light fields from their highly under-sampled versions. The simulation results demonstrate that we can relieve the dense view sampling requirement of HSs, e.g. by as high as 8 χ 8 sub-sampling factor, and still keep the perceived image quality of holographic reconstructions at satisfactory levels. This enables, for example, replacing the scanning camera setups with the more convenient multi-camera arrangements.

The goal of this work is to provide a low complexity video decoding solution for High Efficiency Video Coding (HEVC) streams in applications where only a region of the video frames is needed to be decoded. This paper studies the problem of creating selfcontained (i.e., independently decodable) partitions in the HEVC streams. Further, the requirements for building self-contained regions are described, and an encoder-side solution is proposed based on HEVC tile feature. A particular application of self-contained tiles targets the type of light-field 3D displays, which employ a dense set of optical engines to recreate the light field. Correspondingly, such 3D displays require a dense set of input views and therefore the partial decoding of bitstreams helps providing less complex and consequently real-time decoding and processing. The simulation results show a significant increase in decoding speed at the cost of a minor increase in storage capacity.

Intensity-based pointwise algorithms for 2D evaluation of activity in optical metrology with dynamic speckle analysis are studied. They are applied to a temporal sequence of correlated speckle patterns formed at laser illumination of the object surface. A new algorithm is proposed that provides the same quality of the 2D activity map but at less computational effort. acceptedVersion

This article considers techniques for accelerating a light field reconstruction algorithm operating in shearlet domain. In the proposed approach, an independent reconstruction of epipolar images (EPIs) is replaced with a consecutive tree-structured reconstruction. It aims at decreasing the number of iterations necessary for an EPI reconstruction by using already processed EPIs as initial values in the reconstruction stage. Two algorithms for structuring such processing trees are presented. The reconstruction performance of the proposed algorithms is illustrated on a real dataset. The underlying differences between the algorithms are discussed and numerical results of computation speeds are presented. acceptedVersion

Plane sweeping is an increasingly popular algorithm for generating depth estimates from multiview images. It avoids image rectification, and can align the matching process with slanted surfaces, improving accuracy and robustness. However, the size of the search space increases significantly when different surface orientations are considered. We present an efficient way to perform plane sweeping without individually computing reprojection and similarity metrics on image pixels for all cameras, all orientations and all distances. The procedure truly excels when the amount of views is increased and scales efficiently with the number of different plane orientations. It relies on approximation to generate the costs, but the differences are shown to be small. In practice, it provides results equivalent to conventional matching but faster, making it suitable for applying in many existing implementations.

Some video applications work with parallel bitstreams however only parts of them are required. Decoding the streams entirely might be prohibitive due to high number of video streams and / or high resolution or bitrate of the streams. In such cases, a random access also referred as to partial decoding has to be supported. It can be achieved with a conforming decoder, if the video is separated into individual slices, and the unnecessary slices are dropped from the bit stream. Such a feature is essential in 3D lightfield displays in which partial decoding of bitstreams helps providing less complex and thus real-time decoding and processing. This paper studies the problem of creating self-contained (i.e., independently decodable) slices in an H.264 stream. The requirements that need to be fulfilled in order to build selfcontained slices are described, and an encoder-side solution is proposed. The advantages of self-contained slices are examined in the context of a light-field rendering application that relies on partial decoding. acceptedVersion

In this paper, we emphasize the importance of fixed-pattern noise (FPN) that occurs in Time-of-Flight (ToF) devices that operate in the so-called low-sensing environment. We propose a method for designing FIR filters that can be used for suppressing the FPN. We illustrate by means of two experiments the importance of dealing with the FPN, before conventional denoising algorithms are applied. acceptedVersion

'View-plus-depth' is a popular 3D image representation format, in which the color 2D image is augmented with a gray-scale image representing the scene depth map aligned with the color pixels. In this paper, we propose a novel depth map compression method aimed at finding an optimal spatial depth scale and down-sampling (sparsifying) the depth image over it. The down-sampled depth image is then compressed by a combination of a new arithmetic and a predictive coder. Our approach is motivated by the current achievements in multi-sensor 3D scene sensing where low-resolution depth map captured by time-of-flight sensors is successfully up-sampled and aligned with high resolution RGB images. In our approach, color image segmentation in terms of super-pixels is used for finding the optimal depth scale and corresponding down-sampling. In contrast to other segmentation methods, it results in an isotropic and balanced low-resolution depth image, which is easily compressible. A bilateral regularizer is used for reconstructing the original-size depth map out of the low-resolution one and for splitting and predictive coding of segments with high reconstruction error. The scheme compares favorably with other methods for depth map compression. acceptedVersion

Some of the best performing local stereo-matching approaches use cross-bilateral filters for proper cost aggregation. The recent attempts have been directed toward efficient approximations of such filter aimed at higher speed. In this paper, we suggest a simple yet efficient coarse-to-fine cost volume aggregation scheme, which employs pyramidal decomposition of the cost volume followed by edge-avoiding reconstruction and aggregation. The scheme substantially reduces the computational complexity while providing fair quality of the estimated disparity maps compared to other approximated bilateral filtering schemes. In fact, the speed of the proposed technique is comparable with the speed of fixed kernel aggregation implemented through integral images.

In this work we study the limits of stereoscopic depth perception on an observer tracking 3D display, which provides head parallax. We perform two experiments where human participants are asked to observe two random dot sinusoidal gratings and choose the less “groovy” one. We use gratings with various amplitude and lateral wavelength, and visualize them with and without head parallax. In each experiment we measure the error rate, decision time and head movements. We present comparative results, which show how these parameters are affected by the presence of head parallax.

A plenoptic camera consists of microlenses that enable the measuring of directional information. A common image formation model of the plenoptic camera is based on geometrical optics and provides an object reconstruction with a resolution equal to the number of microlenses. We use a more accurate model that takes into account the wave properties of light. A discrete formulation of this model is done in order to develop the inverse imaging technique which is efficient for super resolution object reconstruction even from noisy data. The proposed algorithm is based on a special modification of the IDD-BM3D deblurring algorithm. The achieved resolution highly exceeds the number of microlenses which is what we call super resolution imaging. Simulations show that the proposed strategy essentially outperforms the previously suggested method based on the wavefield modeling.

In this paper, we propose a practical approach for robust rectification of stereo camera setups without use of calibration pattern. Our solution simplifies the process to a non-general case of rectification to avoid explicit use of Fundamental Matrix estimation. The solution shows better or comparable robustness than some of recent solutions, but for much lower computational cost and code complexity. acceptedVersion

This work addresses the problem of denoising of range data obtained by ToF continuous signal modulation camera working in low-power mode. The proposed approach is based on non-local mean filtering applied over extensive spatio-temporal block search in complex-valued signal domain. The extensive search allows for using shorter integration times of the range sensors and leads to an effective overcomplete structure suitable for denoising. The filter structure is optimized for real-time operation and achieves O(1) performance for arbitrary patch size by utilizing summed area tables and look-up table data fetching. The experimental results show practically the same performance while compared with state-of-the-art approaches, for greatly improved speed.

A Fourier-domain representation of an image exhibits a property where a translation of the image is included in the phase term. This property extends to the Discrete Cosine Transform, where the translation is encoded in the signs of the coefficients. An interpretation of this property for use as a similarity measure in local stereo matching is explored with an efficient way of comparing the transformed blocks to generate a dense disparity map. The method is empirically demonstrated to work also with the Haar wavelet transform, which offers faster computation and improved quality. Results show that presented transform based similarity measures provide better disparity estimates than box aggregated Sum of Absolute Differences and the Census transform when using the percentage of bad pixels as the quality measure. In terms of mean squared error, they achieve better results than SAD but are marginally below Census. The simplicity of making the comparisons results in good scaling with the number of disparity estimates, making the suggested method perform better than SAD also computationally.

In this paper, we present a complete framework of an end-to-end error resilient transmission of 3D video over DVB-H and provide an analysis of transmission parameters. We perform the analysis for various layering, protection strategy and prediction structure using different contents and different channel conditions.

In this paper, a real-time image segmentation algorithm is presented. It utilizes both color and depth information retrieved from a multi-sensor capture system, which combines stereo camera pairs with time-of-flight range sensor. The algorithm targets low complexity and fast implementation which can be achieved through parallelization. Applications, such as immersive videoconferencing and lecturer segmentation for augmented reality lecture presentation, can benefit from the designed algorithm.

Presence of depth and compression artifacts have been identified as major factors that influence subjective quality of experience and satisfaction of 3D imagery visualized on a portable auto-stereoscopic display. The juxtaposition between these two factors has been investigated through psychometric subjective experiment, where participants have evaluated the overall quality of stereoscopic images with varying both presences of depth and compression levels. The results reveal that compression artifacts dominate the quality of experience compared to varying depth range.

Mobile TV has recently received a lot of attention worldwide with the advances in technologies such as Digital Multimedia Broadcasting (DMB), Digital Video Broadcasting - Handheld (DVB-H) and MediaFLO. On the other hand 3DTV is a new approach to watching TV, introducing the third dimension for a more realistic and interactive experience. With the merge of these two technologies it will be possible to have 3DTV products and services based on portable platforms with switchable 2D/3D autostereoscopic displays. The paper presents the European Mobile3DTV project approach toward achieving such a merge. The project specifically addresses the mobile 3DTV delivery over DVB-H system. It develops a technology demonstration system comprising suitable stereo-video content-creation techniques; efficient, scalable and flexible stereo-video encoders with error resilience and error-concealment capabilities, tailored for robust transmission over DVB-H; and also the corresponding stereo-video decoders and players working on a portable terminal device equipped with an autostereoscopic display.

This paper addresses the problem of reconstruction of a monochromatic light field based on data points, irregularly distributed within a volume of interest. Such set up is considered to serve a wide area of applications related with three-dimensional display and beam shaping, where physically inconsistent input data is commonly available. Finite-dimensional models of scalar light fields are used to state the reconstruction problem as matrix inversion. Regularized inversion is done by the Tikhonov method, implemented by the iterative algorithm of conjugate gradients. The problem proves to be ill-posed, where the data points inconsistency is fully compensated by the regularized inversion, showing to be attractive for any application.

In this paper, we address the problem of anti-aliasing filtering of images to be displayed on auto-stereoscopic displays. Auto-stereoscopic displays are constructed to create 3D visual effect by no special glasses but utilizing extra optical layer to cast different images to different directions. The topology of such layer is a compromise between the number of different views generated and the spatial resolution per view being fraction of the full 2D spatial resolution. Usually, the compromise is achieved by slanted and non-rectangular sub-sampling grids causing however corresponding aliasing artefacts. These artefacts are especially visible and annoying when 2D imagery, such as graphics and text, is to be displayed on auto-stereoscopic displays. In our work, we design efficient anti-aliasing filters to mitigate this effect. Two classes of filters are studied for a 3D display case. The first class is the class of non-separable filters, which takes into account the non-rectangular topology of the particular sub-sampling grid and the effect of inter-view crosstalk, and aims at suppressing the respective aliasing replicas appearing in non-rectangular positions on the 2D Fourier plane. The second class is the class of efficient separable 2D filters based on 1D anti-aliasing filter design. We demonstrate that the latter class results in subjectively higher quality images. Studying this particular case further, we design filters for different types of imagery, distinguishing between text and graphics and also between ‘smooth’ and ‘sharp’ target anti-aliased images. As it is difficult to quantify the results by objective measures, we illustrate them by visual examples. Subjective inspections have also confirmed the feasibility of our approach.

We present receiver-side components of the mobile 3DTV technology being developed. We focus on the DVB-T/H receiving module, the 3D video decoder and player, and the auto-stereoscopic display. These three components have been integrated within the OMAP 3430 EVM. We have developed software decapsulator which decapsulates MPE-FEC tables out of MPEG2 transform streams into RTP to feed the H.264 based video decoder modified to decode side-by-side stereo video. An interface card has been developed to interface a parallax barrier auto-stereoscopic display to the system. We provide details about the implementation of these modules and focus on some image processing techniques aimed at correct display of the stereo video content.

This paper presents modelling and analysis of a mobile 3DTV broadcast system for handheld devices. The underlying technology behind our system is the DVB-H specification which brings ordinary TV broadcast services to battery-powered handheld receivers. In our system, we simulated the broadcast of stereo video content over DVB-H to mobile handheld devices with autostereoscopic displays. In this way, mobile users can watch 3D content without need for eyeglasses or any special equipment.

Date of Conference: 28-30 May 2008 Conference name: 2008 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video Efficient computation of scalar optical diffraction field due to an object is an essential issue in holographic 3D television systems. The first step in the computation process is to construct an object. As a solution for this step, we assume that an object can be represented by a set of distributed data points over a space. The second step is to determine which algorithm provides better performance. The source model whose performance is investigated is based on superposition of the diffraction fields emanated from the hypothetical light sources located at the given sample points. Its performance is evaluated according to visual quality of the reconstructed field and its algorithmic complexity. Source model provides acceptable reconstructed patterns when the region in which the samples are given has a narrow depth along the longitudinal direction and a wide extent along the transversal directions. Also, the source model gives good results when the cumulative field at the location of each point due to all other sources tends to be independent of that location. ©2008 IEEE.

Autostereoscopic displays utilizing slanted lenticular sheets produce specific artifacts. These artifacts affect the perception of a 3D scene, and are caused by a process which can be modeled as inter-channel crosstalk. We propose methodology for measuring such a crosstalk for arbitrary multiview 3D display. The measured data might be used for optimizing multiview image sets for a given display.

This work presents a multiple description coding (MDC) scheme for compressed three dimensional (3D) meshes based on forward error correction (FEC). It allows flexible allocation of coding redundancy for reliable transmission over error-prone channels. The proposed scheme is based on progressive geometry compression, which is performed by using wavelet transform and modified SPIHT algorithm. The proposed algorithm is optimized for varying packet loss rates (PLR) and channel bandwidth. Modeling distortion-rate function considerably decreases computational complexity of bit allocation.

Date of Conference: 7-9 May 2007 Conference Name: 3DTV Conference, IEEE 2007 Diffraction field computation is an important task in the signal conversion stage of the holographic 3DTV. We consider an abstract setting, where the diffraction field of the desired 3D scene to be displayed is given by discrete samples distributed over 3D space. Based on these samples, a model of the diffraction field should be built to allow the field computation at any desired point. In our previous works, we have proved our concepts for the simplistic 2D case. In this paper, we generalize the earlier proposed techniques, namely the projection onto convex sets and conjugate gradient based techniques and test them for their computational efficiency and memory requirements for a specific 3D case.

In this paper we propose a computationally efficient method for image super-resolution reconstruction. We concentrate on pure translational motion and shift-invariant blur. This is the case of interlaced sampling where each low resolution image is uniformly sampled but the overall sampling is non-uniform with respect to the high resolution grid. The reconstruction problem is considered in a multi-resolution framework using separable B-spline wavelets as basis. This allows performing the reconstruction on a uniform higher resolution grid by digital filtering. A computationally efficient structure ? the transposed modified Farrow structure is used to achieve a near least squares solution without using any matrix inversions or iterations. The reconstruction can also be performed at non-dyadic scales. Our method minimizes the aliasing distortions and gives results comparable with other least squares techniques.

Date of Conference: 8-11 Oct. 2006 Conference name: 2006 International Conference on Image Processing Two important signal processing problems in the display side of a holographic 3DTV are the computation of the diffraction field of a 3D object from its abstract representation, and determination of the best display configuration to synthesize some intended light distribution. To solve the former problem, we worked on the computation of ID diffraction patterns from discrete data distributed over 2D space. The problem is solved using matrix pseudo-inversion which dominates the computational complexity. Then, the light field synthesis problem by a deflectable mirror array device (DMAD) is posed as a constrained linear optimization problem. The formulation makes direct application of common optimization algorithms quite easy. The simulations indicate that developed methods are promising. ©2006 IEEE.