nils-herrmann/clean_scirepeval huggingface dataset:
Geometrical image denoising using quadtree segmentation

We propose a quadtree segmentation based denoising algorithm, which attempts to capture the underlying geometrical structure hidden in real images corrupted by random noise. The algorithm is based on the quadtree coding scheme proposed in our earlier work and on the key insight that the lossy compression of a noisy signal can provide the filtered/denoised signal. The key idea is to treat the denoising problem as the compression problem at low rates. The intuition is that, at low rates, the coding scheme captures the smooth features only, which basically belong to the original signal. We present simulation results for the proposed scheme and compare these results with the performance of wavelet based schemes. Our simulations show that the proposed denoising scheme is competitive with wavelet based schemes and achieves improved visual quality due to better representation for edges.
A deterministic analysis of oversampled A/D conversion and Sigma Delta modulation

As an alternative to the usual statistical analysis, the authors present a purely deterministic analysis of oversampled A/D (analog-to-digital) conversion and sigma-delta modulation, which requires no assumption on the quantizer error. This leads to the notion of a consistent estimate for the decoding, which is a necessary condition for optimality. Algorithms using convex projections are discussed and the reduction of decoding error from SiO(R/sup -(2n+1)/)to SiO(SiR/sup -(2n+2)/) (where R is the oversampling ratio and n is the order of the encoder) is demonstrated experimentally for multiloop and multistage sigma-delta modulators.<<ETX>>
Joint mesh and texture compression using marginal analysis

In many computer graphic applications, a texture is mapped onto a meshed surface. Under complexity constraints, both texture and mesh need to be approximated. We present a framework for the joint simplification of texture and mesh with respect to an error measure in screen space. In order to achieve optimal operating points, we choose two efficient simplification algorithms: optimal multiresolution mesh simplification based on a quadtree structure and multiresolution view-dependent texture compression based on wavelets. Together, these two algorithms are used for joint simplification using an efficient heuristic based on marginal analysis. As an application example, we study the mapping of aerial orthophotographs onto a terrain model of the Swiss Alps, and show that the resolution of the terrain and the aerial photograph is efficiently traded-off.
The commutativity of up/downsampling in two dimensions

The authors state and prove a theorem solving the problem of commutativity in two dimensions. It is shown under which conditions upsampling and downsampling can be interchanged in two dimensions. This is the generalization to arbitrary two-dimensional lattices of the result that one-dimensional upsampling and downsampling commute if and only if their sampling rates are coprime. Some illustrative examples are given. The result holds for arbitrary sampling lattices.
Consistency in quantized matching pursuit

This paper explores the effects of coefficient quantization in applying the matching pursuit algorithm to source coding of vectors in R/sup N/. By considering the issue of consistency, we find that even though matching pursuit is designed to produce a linear combination to estimate a given source vector, optimal reconstruction in the presence of coefficient quantization requires a nonlinear algorithm. Such an algorithm was implemented and was experimentally confirmed to have superior reconstruction properties in comparison to the standard linear reconstruction. The improvement depends on the source, dictionary and operating point; in some cases the MSE was lessened by as much as a factor of five.
Lattice networks: capacity limits, optimal routing, and queueing behavior

Lattice networks are widely used in regular settings like grid computing, distributed control, satellite constellations, and sensor networks. Thus, limits on capacity, optimal routing policies, and performance with finite buffers are key issues and are addressed in this paper. In particular, we study the routing algorithms that achieve the maximum rate per node for infinite and finite buffers in the nodes and different communication models, namely uniform communications, central data gathering and border data gathering. In the case of nodes with infinite buffers, we determine the capacity of the network and we characterize the set of optimal routing algorithms that achieve capacity. In the case of nodes with finite buffers, we approximate the queue network problem and obtain the distribution on the queue size at the nodes. This distribution allows us to study the effect of routing on the queue distribution and derive the algorithms that achieve the maximum rate
On a Stochastic Version of the Plenacoustic Function

In this paper, we model a spatially varying channel where a source is moving along a random trajectory with respect to a fixed receiver. The aim is to compute the power spectral density corresponding to the channel impulse response as a function of temporal and spatial frequencies. The trajectory of the source follows an auto regressive model where the poles of the system control the smoothness of the path. Theoretical results are presented for the AR2 case and generalized to any ARn systems. Simulations results are shown and compared to the presented theory. The stochastic plenacoustic function analyzed in this paper also models time-varying channels governed by the wave equation, like they appear in acoustic echo cancellation or ultra wide band communication channels
Bit allocation for dependent quantization with applications to MPEG video coders

The problem of efficient bit allocation in a dependent coding environment is addressed. Techniques of optimal bit allocation for independently coded signal blocks are extended to the more general dependent coding scenarios. Of particular interest is the topical MPEG (Motion Picture Experts Group) video coder. It is shown how a certain monotonicity property of dependent operational rate-distortion (R-D) curves, verified through MPEG simulations, can be exploited in formulating fast ways to obtain optimal and near-optimal solutions (in the R-D sense) for the MPEG bit allocation problem.<<ETX>>
Efficient algorithms for embedded rendering of terrain models

Digital terrains are generally large files and need to be simplified to be rendered efficiently. We propose to build an adaptive embedded triangulation based on a binary tree structure to generate multiple levels of details. We present a O(nlogn) decimation algorithm and a O(nlogn) refinement algorithm, where n is the number of elevation points. We compare them in a rate-distortion (RD) framework. The algorithms are based on an improved version of the optimal tree pruning algorithm G-BFOS allowing one to deal with constrained tree structures and non-monotonic tree functionals.
To code or not to code

The theory and practice of digital communication during the past 50 years has been strongly influenced by Shannon's separation theorem. While it is conceptually and practically appealing to separate source from channel coding, either step requires infinite delay in general for optimal performance. On the other extreme is uncoded transmission, which has no delay but is suboptimal in general. In this paper, necessary and sufficient conditions for the optimality of uncoded transmission are shown. These conditions allow the construction of arbitrary examples of optimal uncoded transmission (beyond the well-known Gaussian example).
Wavelet thresholding for multiple noisy image copies

This correspondence addresses the recovery of an image from its multiple noisy copies. The standard method is to compute the weighted average of these copies. Since the wavelet thresholding technique has been shown to effectively denoise a single noisy copy, we consider in this paper combining the two operations of averaging and thresholding. Because thresholding is a nonlinear technique, averaging then thresholding or thresholding then averaging produce different estimators. By modeling the signal wavelet coefficients as Laplacian distributed and the noise as Gaussian, our investigation finds the optimal ordering to depend on the number of available copies and on the signal-to-noise ratio. We then propose thresholds that are nearly optimal under the assumed model for each ordering. With the optimal and near-optimal thresholds, the two methods yield similar performance, and both show considerable improvement over merely averaging.
Multiresolution broadcast for digital HDTV using joint source-channel coding

In the context of digital terrestrial broadcast of high-definition television (HDTV), the use of multiresolution joint source-channel coding is shown to provide an attractive alternative to traditional single resolution (SR) digital techniques. While SR schemes suffer from a sharp threshold effect in the fringes of the broadcast area, it is shown that a matched multiresolution approach to both source and channel coding can provide a stepwise graceful degradation. Furthermore, this multiresolution approach improves the behavior, in terms of coverage and robustness of the transmission scheme, over systems that are not specifically designed to broadcast situations. The authors examine the alternatives available for multiresolution transmission, through embedded modulation, possibly trellis-coded to increase coverage range. From a systems point of view, they also discuss the tradeoffs involved in the choice of coverage areas for the low- and high-resolution signals.<<ETX>>
Nonseparable two- and three-dimensional wavelets

We present two- and three-dimensional nonseparable wavelets. They are obtained from discrete-time bases by iterating filter banks. We consider three sampling lattices: quincunx, separable by two in two dimensions, and FCO. The design methods are based either on cascade structures or on the McClellan transformation in the quincunx case. We give a few design examples. In particular, the first example of an orthonormal 2-D wavelet basis with symmetries is constructed.
Layered transmission of signals over power-constrained wireless channels

Transmission of layered information over a power-constrained channel is considered. We address the question of the power allocation policy where the goal is to minimize the overall end-to-end distortion. A layer containing significant information is more heavily protected against channel error through allocating more power to that layer. We define an appropriate coding gain and show that the Karhunen-Loeve transform (KLT) is the best unitary transform in the sense of maximizing this gain. Furthermore, we look that the embedded transmission of information and compare this method to orthogonal methods such as multicarrier transmission. We also consider end-to-end distortion optimization for broadcast channels.
Detecting planar surface using a light-field camera with application to distinguishing real scenes from printed photos

We propose a novel approach for detecting printed photos from natural scenes using a light-field camera. Our approach exploits the extra information captured by a light-field camera and the multiple views of scene in order to infer a compact feature vector from the variance in the distribution of the depth of the scene. We then use this feature for robust detection of printed photos. Our algorithm can be used in person-based authentication applications to avoid intruding the system using a facial photo. Our experiments show that the energy of the gradients of points in the epipolar domain is highly discriminative and can be used to distinguish printed photos from original scenes.
Spatially adaptive wavelet thresholding with context modeling for image denoising

The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the work has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to the non-stationarity of images. We model each coefficient as a random variable with the generalized Gaussian prior with unknown parameters. Context modeling is used to estimate the parameters for each coefficient, which are then used to adapt the thresholding strategy. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than optimal uniform thresholding.
A framework for optimization of a multiresolution remote image retrieval system

Studies the tradeoffs involved in choosing the bit allocation in a multiresolution remote image retrieval system. Such a system uses a multiresolution image coding scheme so that a user accessing the database will first see a coarse version of the images and will be able to accept or discard a given image faster, without needing to receive all the image data. The authors formalize the problem of choosing the bit allocation (e.g., in the two resolution case, how many bits should be given to the coarse image and the additional information, respectively?) so that the overall delay in the query is minimized. They provide analytical methods to find the optimal solution under different configurations and show how a good choice of the bit allocation results in a significant reduction of the overall delay in the query (by up to a factor of two in some cases).<<ETX>>
Soft caching: web cache management techniques for images

The vast majority of current Internet traffic is generated by web browsing applications. Proxy caching, which allows some of the most popular web objects to be cached at intermediate nodes within the network, has been shown to provide substantial performance improvements. In this paper we argue that image-specific caching strategies are desirable and will result in improved performance over approaches treating all objects alike. We propose that Soft Caching, where an image can be cached at one of a set of levels of resolutions, can benefit the overall performance when combined with cache management strategies that estimate, for each object, both the bandwidth to the server where the object is stored and the appropriate resolution level demanded by the user. We formalize the cache management problem under these conditions and describe an experimental system to test these techniques.
On the Bandwidth of the Plenoptic Function

The plenoptic function (POF) provides a powerful conceptual tool for describing a number of problems in image/video processing, vision, and graphics. For example, image-based rendering is shown as sampling and interpolation of the POF. In such applications, it is important to characterize the bandwidth of the POF. We study a simple but representative model of the scene where band-limited signals (e.g., texture images) are “painted” on smooth surfaces (e.g., of objects or walls). We show that, in general, the POF is not band limited unless the surfaces are flat. We then derive simple rules to estimate the essential bandwidth of the POF for this model. Our analysis reveals that, in addition to the maximum and minimum depths and the maximum frequency of painted signals, the bandwidth of the POF also depends on the maximum surface slope. With a unifying formalism based on multidimensional signal processing, we can verify several key results in POF processing, such as induced filtering in space and depth-corrected interpolation, and quantify the necessary sampling rates.
Split-radix algorithms for length-p/sup m/ DFTs

Reasons are suggested why the split-radix algorithm is better than any single-radix algorithm on length-2/sup m/ DFTs (discrete Fourier transforms). The split-radix approach is generalized to length-p/sup m/ DFTs. It is shown that whenever a radix-p/sup 2/ outperforms a radix-p algorithm, a radix-p/p/sup 2/ algorithm will outperform both of them. As an example, a radix-3/9 algorithm is developed for length-3/sup m/ DFTs.<<ETX>>
Source localization and tracking in non-convex rooms

We consider the estimation of the acoustic source position in a known room from recordings by a microphone array. We propose an algorithm that does not require the room to be convex, nor a line-of-sight path between the microphone array and the source to be present. Times of arrival of early echoes are exploited through the image source model, thereby transforming the indoor localization problem to a problem of localizing multiple sources in the free-field. The localized virtual sources are mirrored into the room using the image source method in the reverse direction. Further, we propose an optimization-based algorithm for improving the estimate of the source position. The algorithm minimizes a cost function derived from the geometry of the localization problem. We apply the designed optimization algorithm to track a moving source, and show through numerical simulations that it improves the tracking accuracy when compared with the naïve approach.
A multiresolution approach to motion estimation and interpolation with application to coding of digital HDTV

The notion of multiresolution signal analysis is used to develop motion estimation algorithms on a three-dimensional pyramid. The motion vectors are then used to interpolate a sequence at a higher resolution, and the difference is coded as an error signal to guarantee very-high-quality coding. This process is repeated to generate a hierarchy of sequences. Such a scheme is well suited for compatible coding of HDTV (high-definition television). The performance of the motion estimation algorithm and of the coding at 100 Mb/s is described.<<ETX>>
Balanced multiwavelets

This paper deals with multiwavelets which are a generalization of wavelets in the context of multirate filter banks and their applications to signal processing and especially compression. By their inherent structure, multiwavelets are fit for processing multi-channel signals. First, we recall some general results on multiwavelets and the convergence of the iterated matrix product. Then, we define under what conditions we can apply systems based on multiwavelets to one-dimensional signals in a simple way. That means we give some natural and simple conditions that should help in the design of new multiwavelets for signal processing. Finally, we provide some tools in order to construct multiwavelets with the required properties, the so-called 'balanced multiwavelets'.
A Discrete Fourier-Cosine Transform Chip

An 8-point Fourier-cosine transform chip designed for a data rate of 100 Mbits/s is described. The top-down design is presented step by step, including algorithm modification for VLSI suitability, architectural choices, testing overhead, internal precision assignments, mask generation, and finally, verification of the layout. A high-level language (C) design tool was developed concurrently with the layout. This tool allows mimicking exactly the different representations of the algorithm: software, mask, and chip. This provides an automatic cross-checking at all design stages. The VLSI environment created by this tool, as well as existing powerful CAD tools, made a fast design-time possible.
Multiresolution Broadcast for Digital HDTV Using Joint Source/Channel Coding

The use of multiresolution (MR) joint source-channel coding in the context of digital terrestrial broadcasting of high-definition television (HDTV) is shown to be an efficient alternative to single-resolution techniques, which suffer from a sharp threshold effect in the fringes of the broadcast area. It is shown how matched multiresolution source and channel coding can provide a stepwise graceful degradation and improve the behavior, in terms of coverage and robustness of the transmission scheme, over systems not specifically designed for broadcast situations. The alternative available for multiresolution transmission through embedded modulation and error correction codes are examined. It is also shown how multiresolution trellis-coded modulation (TCM) can be used to increase coverage range. Coding results and simulations of noisy transmission are presented, and tradeoffs are discussed.
Sampling and reconstruction of signals with finite rate of innovation in the presence of noise

Recently, it was shown that it is possible to develop exact sampling schemes for a large class of parametric nonbandlimited signals, namely certain signals of finite rate of innovation. A common feature of such signals is that they have a finite number of degrees of freedom per unit of time and can be reconstructed from a finite number of uniform samples. In order to prove sampling theorems, Vetterli et al. considered the case of deterministic, noiseless signals and developed algebraic methods that lead to perfect reconstruction. However, when noise is present, many of those schemes can become ill-conditioned. In this paper, we revisit the problem of sampling and reconstruction of signals with finite rate of innovation and propose improved, more robust methods that have better numerical conditioning in the presence of noise and yield more accurate reconstruction. We analyze, in detail, a signal made up of a stream of Diracs and develop algorithmic tools that will be used as a basis in all constructions. While some of the techniques have been already encountered in the spectral estimation framework, we further explore preconditioning methods that lead to improved resolution performance in the case when the signal contains closely spaced components. For classes of periodic signals, such as piecewise polynomials and nonuniform splines, we propose novel algebraic approaches that solve the sampling problem in the Laplace domain, after appropriate windowing. Building on the results for periodic signals, we extend our analysis to finite-length signals and develop schemes based on a Gaussian kernel, which avoid the problem of ill-conditioning by proper weighting of the data matrix. Our methods use structured linear systems and robust algorithmic solutions, which we show through simulation results.
Sampling and reconstructing diffusion fields in presence of aliasing

The reconstruction of a diffusion field, such as temperature, from samples collected by a sensor network is a classical inverse problem and it is known to be ill-conditioned. Previous work considered source models, such as sparse sources, to regularize the solution. Here, we consider uniform spatial sampling and reconstruction by classical interpolation techniques for those scenarios where the spatial sparsity of the sources is not realistic. We show that even if the spatial bandwidth of the field is infinite, we can exploit the natural low-pass filter given by the diffusion phenomenon to bound the aliasing error.
Improved Fourier and Hartley transform algorithms: Application to cyclic convolution of real data

This paper highlights the possible tradeoffs between arithmetic and structural complexity when computing cyclic convolution of real data in the transform domain. Both Fourier and Hartley-based schemes are first explained in their usual form and then improved, either from the structural point of view or in the number of operations involved. Namely, we first present an algorithm for the in-place computation of the discrete Fourier transform on real data: a decimation-in-time split-radix algorithm, more compact than the previously published one. Second, we present a new fast Hartley transform algorithm with a reduced number of operations. A more regular convolution scheme based on FFT's is also proposed. Finally, we show that Hartley transforms belong to a larger class of algorithms characterized by their "generalized" convolution property.