6449041
Adaptive frame prediction for foveation scalable video coding
Embedded rate scalable video coding allows for the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. This is a very attractive feature for many multimedia communication applications. Motion estimation (ME) /motion compensation (MC) techniques are widely employed in various video coding systems to reduce temporal information redundancy. One of the major challenging problems in ME/MC based rate scalable video coding is how to generate the prediction frame from the previous frame to match the current frame. This problem is more difficult in rate scalable coding than in fixed rate coding because the decoding data rate is unavailable to the encoder. We propose an adaptive frame prediction scheme for foveation scalable video coding (FSVC), which is a new video coding algorithm that combines a foveation-based human visual system (HVS) model with a wavelet-based rate scalable coding algorithm. The new frame prediction algorithm provides an adaptive mechanism to control the prediction errors while reduce error propagation.
18764674
Image information restoration based on long-range correlation
A new class of image information restoration algorithms, virtually different from traditional techniques, is proposed. In comparison with other approaches, our methods use not only the information in local areas, but also that in the remote regions of the image. The methods originate from the idea that there exists abundant long-range correlation within natural images and the human vision system, composed of our eyes and brains, can sufficiently utilize such types of information redundancy to implement the functions of image interpretation, representation, restoration, enhancement and error concealment. Our general approach can be summarized as five basic steps: fetching, searching, matching, competing and recovering. The experimental results on several practical applications show that our methods perform substantially better than many other state-of-the-art methods.
41489302
Integrated Optical Routing Topology for MicroPhotonic Switches
In this paper, an integrated optical beam router for MicroPhotonic switching is described. The router employs a piecewise linear optic that steers and couples optical collimated beams to optical fiber ports with minimum loss. Experimental results show that for a 3-port MicroPhotonic switch, fiber-to-fiber optical coupling loss less than 0.5 dB and insertion loss uniformity better than 0.25 dB can be achieved, and the tolerance to fluctuations in design parameters is practical.
2059324
Choquet integrals and natural extensions of lower probabilities
Abstract Both the Choquet integral with respect to monotone set functions and the natural extensions of lower probabilities are generalizations of the Lebesgue integral with respect to σ-additive measures. The relation between these generalizations is investigated. We show that the Choquet integral with respect to a belief measure is always greater than or equal to the corresponding natural extension. Also, we compare some concepts introduced in the theory of imprecise probabilities with concepts established in fuzzy measure theory, capacity theory, and Dempster-Shafer theory.
14687819
On finite precision implementation of low density parity check codes decoder
In this paper, we analyze the finite precision effects on the decoding performance of Gallager's low density parity check (LDPC) codes and develop optimal finite word lengths of variables as far as the tradeoffs between the performance and hardware complexity are concerned. We have found that 4 bits and 6 bits are adequate for representing the received data and extrinsic information, respectively. Simulation results indicate that the quantization scheme we have developed for the LDPC decoder is effective in approximating the infinite precision implementation.
1885677
Learning nonlinear multiregression networks based on evolutionary computation
This paper describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals. We develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP), we propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases. We have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as well as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients.
204983610
Pyramidic Clustering of Large-Scale Microarray Images
With ongoing research and development of imaging techniques such as those involved in brain MRIs, cDNA microarrays and satellite reconnaissance, the need for tools that can intelligently parse larger images is ever increasing. One group of such techniques often used is that of segmentation, an example of which is that of clustering algorithms. In order to deal with large data sets, current approaches require the data to be sampled or summarized before true analysis can take place. In this paper we propose a novel image analysis technique using pyramidic type grouping, namely copasetic clustering, which focuses on the problem of applying traditional clustering techniques to these large-scale image data sets with limited resources. A further benefit of the technique is the transparency of its intermediate clustering steps; when applied to spatial data sets this allows the capture and incorporation of contextual information to improve result accuracy. The algorithm achieves an ∼1--3 dB w-to-noise ratio when compared with the conventional techniques described.