Near-eye multi-focal (light field) displays are known to be more advantageous than conventional stereoscopic 3D displays, because they can alleviate the vergence-accommodation conflict. While stereoscopic 3D displays are restricted to projecting images on a single focal plane, near-eye light field displays can form multiple focal planes and volumetrically render 3D data. Most existing near-eye light field displays use simplistic, uniformly spaced focal plane configuration (in dioptric space). In this paper, we present a novel technique that optimizes the focal plane configuration based on characteristics of the content to be rendered. We show that this technique can significantly improve the perceived visual quality of content visualized on such displays.

We present a study of depth-disparity calibration for augmented reality applications using binocular optical see-through displays. Two techniques were proposed and compared. The "paired-eyes" technique leverages the Panum's fusional area to help viewer find alignment between the virtual and physical objects. The "separate-eyes" technique eliminates the need of binocular fusion and involves using both eyes sequentially to check the virtual-physical object alignment on retinal images. We conducted a user study to measure the calibration results and assess the subjective experience of users with the proposed techniques.

The paper describes a lossy image codec that uses a noncausal (or bilateral) prediction model coupled with vector quantization. The noncausal prediction model is an alternative to the causal (or unilateral) model that is commonly used in differential pulse code modulation (DPCM) and other codecs with a predictive component. We show how to obtain a recursive implementation of the noncausal image model without compromising its optimality and how to apply this in coding in much the same way as a causal predictor. We report experimental compression results that demonstrate the superiority of using a noncausal model based predictor over using traditional causal predictors. The codec is shown to produce high-quality compressed images at low bit rates such as 0.375 b/pixel. This quality is contrasted with the degraded images that are produced at the same bit rates by codecs using causal predictors or standard discrete cosine transform/Joint Photographic Experts Group-based (DCT/JPEG-based) algorithms.

The parameter structure of noncausal homogeneous Gauss Markov random fields (GMRF) defined on finite lattices is studied. For first-order (nearest neighbor) and a special class of second-order fields, a complete characterization of the parameter space and a fast implementation of the maximum likelihood estimator of the field parameters are provided. For general higher order fields, tight bounds for the parameter space are presented and an efficient procedure for ML estimation is described. Experimental results illustrate the application of the approach presented and the viability of the present method in fitting noncausal models to 2-D data.>

The problem of estimating the parameters of noncausal finite lattice Gauss Markov random fields is addressed. It is shown how the structure of the potential matrix (the inverse of the field covariance matrix) can be used to specify the valid parameter space and formulate a computationally practical maximum likelihood estimation procedure. A modification that enables this to generate accurate parameter estimates from noisy data is provided.>

An approach is developed for noncausal Gauss-Markov random fields (GMRFs) that enables the use of recursive procedures while retaining the noncausality of the field. Recursive representations are established that are equivalent to the original field. This is achieved by first presenting a canonical representation for GMRFs that is based on the inverse of the covariance matrix, which is called the potential matrix. It is this matrix rather than the field covariance that reflects in a natural way the MRF structure. From its properties, two equivalent one-sided representations are derived, each of which is obtained as the successive iterates of a Riccati-type equation. For homogeneous fields, these unilateral descriptions are symmetrized versions of each other, the study of only one Riccati equation being required. It is proven that this Riccati equation converges at a geometric rate, therefore the one-sided representations are asymptotically invariant. These unilateral representations make it possible to process the fields with well-known recursive techniques such as Kalman-Bucy filters and two-point smoothers.>

A recursive procedure is implemented for the enhancement of noncausal Gauss Markov random fields. Experimental results for the enhancement of synthetic as well as real images corrupted by additive white Gaussian noise are provided. These are contrasted with equivalent results obtained by processing the images with recursive filters derived by imposing a causality constraint upon the fields. The results show that the noncausal recursors provide considerable reduction in the mean square error (MSE) of the noisy images without the introduction of undesirable visual effects, such as streaking, that are produced when causality constraints are imposed.>

The three types of parallelism currently available on the Cray are considered, namely, vectorization, microtasking, and macrotasking. Experiences with all three constructs are presented to show the improvements possible with each of them. While a particular machine, the Cray X-MP/48, has been used, many of the observations, comments, and conclusions derived can be generalized to other shared-memory multiprocessor systems.>