nils-herrmann/clean_scirepeval huggingface dataset:
Coding of spatio-temporal audio spectra using tree-structured directional filterbanks

We address the problem of integrating directional analysis of sound into the filterbank of a spatial audio coder, with the purpose of processing and coding with some degree of independence the plane waves traveling in different directions. A plane wave represents an elementary waveform in the spatio-temporal analysis of the sound field, the same way a complex exponential is an elementary waveform in the time domain analysis of signals. Since a two-dimensional separable filterbank is not flexible enough for this purpose, we propose a non-separable approach based on the quincunx filterbank with diamond-shaped filters, cascaded with a base transform filterbank. This solution provides an invertible and critically sampled decomposition of the spatiotemporal spectra into subbands representing the different directions of wave propagation.
On network correlated data gathering

We consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a Slepian-Wolf model where optimal coding is complex and transmission optimization is simple, and a joint entropy coding model with explicit communication where coding is simple and transmission optimization is difficult. This problem requires a joint optimization of the rate allocation at the nodes and of the transmission structure. For the Slepian-Wolf setting, we derive a closed form solution and an efficient distributed approximation algorithm with a good performance. For the explicit communication case, we prove that building an optimal data gathering tree is NP-complete and we propose various distributed approximation algorithms.
Atomic decompositions of audio signals

Signal modeling techniques ranging from basis expansions to parametric approaches have been applied to audio signal processing. Motivated by the fundamental limitations of basis expansions for representing arbitrary signal features and providing means for signal modifications, we consider decompositions in terms of functions that are both signal-adaptive and parametric in nature. Granular synthesis and sinusoidal modeling can be viewed in this light; we interpret these approaches as signal-adaptive expansions in terms of time-frequency atoms that are highly correlated to the fundamental signal structures. This leads naturally to a discussion of the matching pursuit algorithm for deriving decompositions using over complete dictionaries of time-frequency atoms; specifically, we compare expansions using Gabor atoms and damped sinusoids. Such decompositions identify important signal features and provide parametric representations that are useful for signal coding and analysis-modification-synthesis.
Basefield transforms with the convolution property

We present a general framework for constructing transforms in the field of the input which have a convolution-like property. The construction is carried out over the reals, but is shown to be valid over more general fields. We show that these basefield transforms can be viewed as "projections" of the discrete Fourier transform (DFT). Furthermore, by imposing an additional condition on the projections, one may obtain self-inverse versions of the basefield transforms. Applying the theory to the real and complex fields, we show that the projection of the complex DFT results in the discrete combinational Fourier transform (DCFT) and that the imposition of the self-inverse condition on the DCFT yields the discrete Hartley transform (DHT). Additionally, we show that the method of projection may be used to derive efficient basefield transform algorithms by projecting standard FFT algorithms from the extension field to the basefield. Using such an approach, we show that many of the existing real Hartley algorithms are projections of well-known FFT algorithms.
Matching pursuit for compression and application to motion compensated video coding

In many hybrid video compression algorithms there is a clear distinction between motion estimation and compensation (MEMC) on the one hand, and transform coding of the residue on the other hand. By translating MEMC into the framework of matching pursuit (MP), we will show that unification of both steps is possible. Moreover, adapting the MP approach to the small size dictionaries involved in MEMC, it is shown that unification may in principle lead to a better performance of hybrid coding schemes. We close with a discussion on the practical obstacles of the technique, and how to avoid them.<<ETX>>
Trade-off's in the computation of mono- and multi-dimensional DCT's

An overview of some alternative algorithms for one- and two-dimensional DCTs (discrete cosine transforms) is given. Operation counts are derived for typical examples useful in image processing. It is shown that it is possible to generalize the 2-D schemes to 3-D DCTs as well. The result is that a 3-D DCT can be obtained from a 3-D DFT (discrete Fourier transform) of the same size on reals at the cost of permutations and O(3/2N/sup 3/) multiplications. The scheme involves rotations on eight output points at a time. Improvements through scaling are discussed, and implementation issues (both in hardware and software) are addressed.<<ETX>>
Quantized frame expansions as source-channel codes for erasure channels

Quantized frame expansions are proposed as a method for generalized multiple description coding, where each quantized coefficient is a description. Whereas previous investigations have revealed the robustness of frame expansions to additive noise and quantization, this represents a new application of frame expansions. The performance of a system based on quantized frame expansions is compared to that of a system with a conventional block channel code. The new system performs well when the number of lost descriptions (erasures on an erasure channel) is hard to predict.
Optimal bit allocation with side information

For a given set of quantizers and a data vector, the optimal bit allocation in a rate/distortion sense is the sequence of quantizers which minimizes the overall distortion for a given bit budget. In an operational framework, this sequence is dependent on the data realization rather than on its probabilistic model and the cost of describing the sequence itself becomes therefore part of the bit budget. We present an allocation algorithm based on dynamic programming which determines the optimal bit allocation taking into account the side information of describing the structure of the allocation itself; practical simplifications of the algorithm are also presented with respect to coding of continuous data sources.
The Plenacoustic Function and Its Sampling

The spatialization of the sound field in a room is studied, in particular the evolution of room impulse responses as a function of their spatial positions. It was observed that the multidimensional spectrum of the solution of the wave equation has an almost bandlimited character. Therefore, sampling and interpolation can easily be applied using signals on an array. The decay of the spectrum is studied on both temporal and spatial frequency axes. The influence of the decay on the performance of the interpolation is analyzed. Based on the support of the spectrum, the number and the spacing between the microphones is determined for the reconstruction of the sound pressure field up to a certain temporal frequency and with a certain reconstruction quality. The optimal sampling pattern for the microphone positions is given for the linear, planar and three-dimensional case. Existing techniques usually make use of room models to recreate the sound field present at some point in the space. The presented technique simply starts from the measurements of the sound pressure field in a finite number of positions and with this information the sound pressure field can be recreated at any spatial position. Finally, simulations and experimental results are presented and compared with the theory
Time-varying filter banks and multiwavelets

A wavelet construction by Geronimo, Hardin and Massopust uses more than one wavelet and scaling function. Strang and Strela gave a filter bank interpretation of that result, as well as a condition for moment properties of the resulting wavelets. The present authors are concerned with the regularity of the resulting iterated filter bank scheme, that is, a matrix extension of the classic result by Daubechies (1988) on iterated filters. They show in particular: (i) the relation between time-varying filter banks and multiwavelets, (ii) the construction of multiwavelets as limits of iterated time-varying filter banks, (iii) a necessary condition for the convergence of the iterated matrix product and (iv) an exploration of examples of multiwavelets as iterations of time-varying filter banks.<<ETX>>
Space-Frequency Quantization for Image Compression With Directionlets

The standard separable 2-D wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to efficiently capture 1-D discontinuities, like edges or contours. These features, being elongated and characterized by geometrical regularity along different directions, intersect and generate many large magnitude wavelet coefficients. Since contours are very important elements in the visual perception of images, to provide a good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional features. In our previous work, we proposed a construction of critically sampled perfect reconstruction transforms with directional vanishing moments imposed in the corresponding basis functions along different directions, called directionlets. In this paper, we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method outperforms the standard SFQ in a rate-distortion sense, both in terms of mean-square error and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm.
Rate-distortion problem for physics based distributed sensing

We consider the rate-distortion problem for sensing the continuous space-time physical temperature in a circular ring on which a heat source is applied over space and time, and which is also allowed to cool by radiation or convection to its surrounding medium. The heat source is modelled as a continuous space-time stochastic process which is bandlimited over space and time. The temperature field is the result of a circular convolution over space and a continuous-time causal filtering over time of the heat source with the Green's function corresponding to the heat equation, which is space and time invariant. The temperature field is sampled at uniform spatial locations by a set of sensors and it has to be reconstructed at a base station. The goal is to minimize the mean-square-error per second, for a given number of nats per second, assuming ideal communication channels between sensors and base station. We find a) the centralized R/sup c/ (D) function of the temperature field, where all the space-time samples can be observed and encoded jointly. Then, we obtain b) the R/sup s-i/ (D) function, where each sensor, independently, encodes its samples optimally over time and c) the R/sup st-i/ (D) function, where each sensor is constrained to encode also independently over time. We also study two distributed prediction-based approaches: a) with perfect feedback from the base station, where temporal prediction is performed at the base station and each sensor performs differential encoding, and b) without feedback, where each sensor locally performs temporal prediction.
Design of multidimensional non-separable regular filter banks and wavelets

The design of multidimensional nonseparable wavelets based on iterated filter banks is investigated. To obtain regularity of the wavelet, a maximum number of zeros is put at aliasing frequencies in the lowpass filter. Two approaches are pursued. A direct method designs nonseparable perfect reconstruction filter banks based on cascade structures and with prescribed zeros both analytically (small cases) and numerically (larger cases). A second, indirect method maps biorthogonal one-dimensional banks with high regularity into multidimensional banks using the McClellan transformation. A number of properties relevant to perfect reconstruction and zero locations are shown in this case. Design examples are given in all cases, and the testing of regularity is discussed, together with a fast algorithm to compute iterated filters.<<ETX>>
Wavelets, approximation, and compression

Over the last decade or so, wavelets have had a growing impact on signal processing theory and practice, both because of the unifying role and their successes in applications. Filter banks, which lie at the heart of wavelet-based algorithms, have become standard signal processing operators, used routinely in applications ranging from compression to modems. The contributions of wavelets have often been in the subtle interplay between discrete-time and continuous-time signal processing. The purpose of this article is to look at wavelet advances from a signal processing perspective. In particular, approximation results are reviewed, and the implication on compression algorithms is discussed. New constructions and open problems are also addressed.
Acoustic Tomography Method for Measuring Temperature and Wind Velocity

We consider the problem of reconstructing superimposed temperature and wind flow fields from acoustic measurements. A new technique based solely on acoustic waves propagation is presented. In contrast to the usual straight ray assumption, a bent ray model is considered in order to achieve higher accuracy. Under this assumption, we propose an iterative reconstruction algorithm that allows to entirely recover the considered fields. It alternates between estimating the fields of interest and the corresponding acoustic ray trajectories. Simulation results confirm the effectiveness and fast convergence of our scheme
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 literature has focused on developing the best uniform threshold or best basis selection. However, not much has been done to make the threshold values adaptive to the spatially changing statistics of images. Such adaptivity can improve the wavelet thresholding performance because it allows additional local information of the image (such as the identification of smooth or edge regions) to be incorporated into the algorithm. 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 changing image characteristics. Each wavelet coefficient is modeled as a random variable of a generalized Gaussian distribution with an unknown parameter. Context modeling is used to estimate the parameter for each coefficient, which is then used to adapt the thresholding strategy. This spatially adaptive thresholding is extended to the overcomplete wavelet expansion, which yields better results than the orthogonal transform. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than the best uniform thresholding with the original image assumed known.
Interactive DSP education using Java

We argue that Java is a natural language to develop interactive teaching material that can be shared and distributed widely. Unlike any other programming language or platform we know, Java development is justified because of its almost universal acceptance. We develop a block diagram (BD) based approach that allows one to develop interactive and downloadable signal processing laboratories. As an example, we show how specific experiments for a DSP class, as well as for an advanced course on wavelets have been developed. The article first explains why the Java language has been chosen, and then describes what has been realized today. Finally, we show how the BD representation can be efficiently used for the development of a wavelet theory course. It is shown that only a few simple blocks are sufficient for creating many didactic programs. This can be seen as an a posteriori justification of the BD model.
Share risk and energy: Sampling and communication strategies for multi-camera wireless monitoring networks

In the context of environmental monitoring, outdoor wireless cameras are vulnerable to natural hazards. To benefit from the inexpensive imaging sensors, we introduce a multi-camera monitoring system to share the physical risk. With multiple cameras focusing at a common scenery of interest, we propose an interleaved sampling strategy to minimize per-camera consumption by distributing sampling tasks among cameras. To overcome the uncertainties in the sensor network, we propose a robust adaptive synchronization scheme to build optimal sampling configuration by exploiting the broadcast nature of wireless communication. The theory as well as simulation results verify the fast convergence and robustness of the algorithm. Under the interleaved sampling configuration, we propose three video coding methods to compress correlated video streams from disjoint cameras, namely, distributed/independent/joint coding schemes. The energy profiling on a two-camera system shows that independent and joint coding perform substantially better. The comparison between two-camera and single-camera system shows 30%-50% per-camera consumption reduction. On top of these, we point out that MIMO technology can be potentially utilized to push the communication consumption even lower.
Topology optimization for energy-efficient communications in consensus wireless networks

Over the past years there has been an increasing interest in developing distributed computation methods over wireless networks. A new communication paradigm has emerged where distributed algorithms such as consensus have played a key role in the development of such networks. A special case are wireless sensor networks (WSN) which have found application in a large variety of problems such as environmental monitoring, surveillance, or localization, to cite a few. One major design issue in WSNs is energy efficiency. Nodes are typically battery-powered devices and thus, it is critical to make a proper use of the scarce energy resources. This fact motivates the search for optimal conditions that favor the communication environment. It is well known that the rate at which the information is spread across the network depends on the topology of the network and that finding the optimal topology is a hard combinatorial problem. However, using convex optimization tools, we propose a method that tries to find the optimal topology in a consensus wireless network that uses broadcast messages. Our results show that exploiting the broadcast nature of the wireless channel leads to more energy efficient configurations than using dedicated unicast messages and that our algorithm performs very close to the optimal solution.
Computationally efficient QMF filter banks

This paper introduces a computationally efficient technique for splitting a signal into N equally spaced sub-bands subsampled by 1/N and for near perfectly reconstructing the original signal from the sub-band signals. This technique is based on a multirate approach where some operations are nested to decrease the computation load. Simulation results show that the performances are comparable to that of conventional quadrature mirror filters, but with a very significant reduction in computational complexity.
Syntax-constrained encoder optimization using adaptive quantization thresholding for JPEG/MPEG coders

The authors show a rate-distortion optimal quantization technique to threshold the DCT coefficients in the industry image and video coding standards JPEG and MPEG respectively. Their scheme achieves a decent thresholding gain in terms of both objective SNR (about 1 dB) as well as perceived quality and uses a fast dynamic programming recursive structure which exploits certain monotonicity characteristics of the JPEG and MPEG codebooks to drastically reduce the complexity. The primary advantage of their encoding algorithm is that it is completely compatible with the baseline JPEG and MPEG decoders.<<ETX>>
A layered DCT coder for Internet video

Several compression schemes for Internet video utilize block-based conditional replenishment (CR) where block updates are coded independently of the past. In the current Internet video tools, blocks are compressed with a single-layer representation. We propose a new approach that compresses image blocks using a layered representation. Our layered-DCT (LDCT) compression algorithm, derived from progressive JPEG, has been combined with CR and optimized for efficient software implementation to provide an improved solution for Internet packet video. Although LDCT is constrained to a layered representation, its compression performance is as good or better than the single layer Intra-H.261 and baseline JPEG coding schemes.
Frame domain signal processing: Framework and applications

Besides basis expansions, frames representations play a key role in signal processing. We thus consider the problem of frame domain signal processing, which is more complex and challenging than transform domain processing. Examples of such processing abound, from overlap-add/save convolution, to frequency domain LMS, and frame magnitude reconstruction. We develop a unified view of all these situations by using a common Hilbert space view of the problem, and consider algorithms in this common framework. In addition to a synthetic view of multiple signal processing methods in frames, we derive several original results. This include a direct solution to spectral modification (which usually uses an iterative algorithm) and a unicity condition for reconstruction from frame coefficient magnitudes.
Bit allocation for dependent quantization with applications to multiresolution and MPEG video coders

We address the problem of efficient bit allocation in a dependent coding environment. While optimal bit allocation for independently coded signal blocks has been studied in the literature, we extend these techniques to the more general temporally and spatially dependent coding scenarios. Of particular interest are the topical MPEG video coder and multiresolution coders. Our approach uses an operational rate-distortion (R-D) framework for arbitrary quantizer sets. We show how a certain monotonicity property of the dependent R-D curves can be exploited in formulating fast ways to obtain optimal and near-optimal solutions. We illustrate the application of this property in specifying intelligent pruning conditions to eliminate suboptimal operating points for the MPEG allocation problem, for which we also point out fast nearly-optimal heuristics. Additionally, we formulate an efficient allocation strategy for multiresolution coders, using the spatial pyramid coder as an example. We then extend this analysis to a spatio-temporal 3-D pyramidal coding scheme. We tackle the compatibility problem of optimizing full-resolution quality while simultaneously catering to subresolution bit rate or quality constraints. We show how to obtain fast solutions that provide nearly optimal (typically within 0.3 dB) full resolution quality while providing much better performance for the subresolution layer (typically 2-3 dB better than the full-resolution optimal solution).