nils-herrmann/clean_scirepeval huggingface dataset:
Globally optimal bit allocation

Given M quantizers of variable rates, scaler or/and vector, the globally optimal allocation of B bits to the M quantizers can be computed in O(MB/sup 2/) time with integer constraint, or O(MB2/sup B/) time without. The author also considers the nested optimization problem of optimal bit allocation with respect to optimal quantizers. Various algorithmic techniques are proposed to solve this new problem in pseudo-polynomial time.<<ETX>>
On Multi-Stage Sequential Coding of Correlated Sources

We study the problem of multi-stage sequential coding (MSSC), which is an extension of sequential coding of correlated sources. Consider two correlated random variables X and Y to be coded in two stages. The first stage is sequential coding as referred to in the existing literature. At the second stage, the Y encoder refines the information of Y without any knowledge of X, and X encoder refines the information of X with the knowledge of Y, while all previous outputs are known at the decoder. As the sequential coding problem provides a theoretical abstraction of video coding, the MSSC model is a theoretical abstraction of scalable video coding, which is an important application of network communications. We give an achievable region for the MSSC system. The given achievable region is tight when Y is required to be reconstructed perfectly in the usual Shannon sense at the second stage. We also study the minimum total rate MSSC problem, and derive the minimum total rate for Gaussian sources. This result disproves the possibility that the minimum total rate of one stage sequential coding can be achieved at both stages even for correlated Gaussian sources. Thus we offer a theoretical explanation for the performance loss of scalable video coding widely noted by practitioners
Optimal index assignment for multiple description lattice vector quantization

Optimal index assignment of multiple description lattice vector quantizer (MDLVQ) can be posed as a large-scale linear assignment problem. But is this expensive algorithmic approach necessary? This paper presents a simple index assignment algorithm for high-resolution MDLVQ of K /spl ges/ 2 balanced descriptions in any dimensions. Despite its simplicity, the new algorithm is optimal for a large family of lattices encountered in theory and practice, in terms of minimizing the expected distortion for any side description loss rate and any side entropy rate. This work offers exact combinatoric constructions of optimal index assignments, rather than arguing for the optimality asymptotically. Consequently, the optimality holds for all values of sublattice index N (i.e., over all trade-offs between the central and side distortions), rather than for very large N only. Furthermore, the time complexity of the new algorithm is O(N) as opposed to O(N/sup 6/) for a current linear assignment-based method. New and improved closed form expressions of the expected distortion as the function of N and K are also presented. Thus the optimal values of N and K can be computed.
Recovery of lost VQ indexes in packet transmission

We consider the problem of robust transmission of VQ-coded image/video via noisy packet networks. In the event of packet loss, some VQ index bits will be absent at the receiver side. But the very knowledge of lost packets identities the spatial locations of affected VQ blocks, which are powerful information for the decoder to estimate the missing VQ index bits. This is possible because of the statistical redundancy between spatially adjacent VQ blocks. In this paper we present a MAP (maximum a posterior) estimation technique to recover missing VQ index bits due to packet loss. The novelty of this work is to couple high-order Markov modeling with MAP while avoiding the problem of context dilution.
Scalable lossy to lossless video coding via adaptive 3D wavelet transform and context modeling

We investigated scalable compression of image sequences using 3D wavelet transform and adaptive arithmetic coding driven by 3D context modeling. The interplays between motion compensation, 3D wavelet transform, and entropy coding were studied empirically. The best compression, in both lossy and lossless cases, was achieved by a non-dyadic 3D wavelet decomposition and high-order Markov modeling of transform coefficients, but without motion compensation. The experimental results are very encouraging. On the mother-and-daughter and salesman QCIF sequences, the proposed method outperforms H.263 by up to 3 dB. If integer wavelets are used, our method also improves the lossless performance of other 3D integer wavelet methods by an appreciable margin.
Wavelet image coding using trellis coded space-frequency quantization

The progress in wavelet image coding have brought the field into its maturity. Major developments in the process are rate-distortion (R-D) based wavelet packet transformation, zerotree quantization, subband classification and trellis-coded quantization, and sophisticated context modeling in entropy coding. Drawing from past experience and recent in sights, we propose a new wavelet image coding technique with trellis coded space-frequency quantization (TCSFQ). TCSFQ aims to explore space-frequency characterizations of wavelet image representations via R-D optimized zerotree pruning, trellis-coded quantization, and context modeling in entropy coding. Experiments indicate that the TCSFQ coder achieves twice as much compression as the baseline JPEG coder does at the same peak signal to noise ratio (PSNR), making it better than all other coders described in the literature.
PCA-Based Spatially Adaptive Denoising of CFA Images for Single-Sensor Digital Cameras

Single-sensor digital color cameras use a process called color demosaicking to produce full color images from the data captured by a color filter array (CFA). The quality of demosaicked images is degraded due to the sensor noise introduced during the image acquisition process. The conventional solution to combating CFA sensor noise is demosaicking first, followed by a separate denoising processing. This strategy will generate many noise-caused color artifacts in the demosaicking process, which are hard to remove in the denoising process. Few denoising schemes that work directly on the CFA images have been presented because of the difficulties arisen from the red, green and blue interlaced mosaic pattern, yet a well designed ldquodenoising first and demosaicking laterrdquo scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. This paper presents a principle component analysis (PCA) based spatially-adaptive denoising algorithm, which works directly on the CFA data using a supporting window to analyze the local image statistics. By exploiting the spatial and spectral correlations existed in the CFA image, the proposed method can effectively suppress noise while preserving color edges and details. Experiments using both simulated and real CFA images indicate that the proposed scheme outperforms many existing approaches, including those sophisticated demosaicking and denoising schemes, in terms of both objective measurement and visual evaluation.
Optimal variable rate multiplexing of scalable code streams

The problem of multiplexing of several rate-distortion scalable multimedia code streams to be transmitted via a variable-rate channel is investigated. The aim is to minimize the expected distortion at the receiver, weighted by the probability distribution of the truncation point, P(l). Firstly, a very simple algorithm is presented which solves the problem in the case when the distribution of the truncation point is uniform or exponential. Secondly, the problem of constructing the optimal code stream is treated with the side information included. In order to reduce the amount of side information, a trade off between the optimal order of the /spl alpha/-atom strings described and a predefined order is proposed. Finally, a dynamic programming algorithm is presented to determine the optimal number of refinement stages. The time complexity of this algorithm is quadratic in the total number of /spl alpha/-atom strings.
Rate-Distortion Optimized Network Communication

Network information multicast has been considered extensively, either as a routing problem or more recently in the context of network coding. Most of the Internet bandwidth, however, is consumed by multimedia contents that are amenable to lossy reconstruction. In this paper, we investigate the following fundamental question: How does one communicate a media content from nodes (servers) that observe/supply the content to a set of sink nodes (clients) to realize the best possible reconstruction of the content in a rate-distortion sense? While this problem remains essentially open, this paper takes the first step by exploring the intricate entanglement of source coding and network communication, within an optimization framework. In particular, we investigate the joint optimization of network communication strategies (e.g., routing or network coding) and common source coding schemes (e.g., progressive coding or more general multiple description coding). We formulate several such problems for which we are able to develop efficient polynomial time solutions. In particular, we consider layered multicast of progressively encoded source code streams using network coding and optimal routing of balanced multiple description codes. Finally, the improvement in the overall quality of source reconstruction by using the proposed schemes is verified through simulations.
Index assignment optimization for joint source-channel MAP decoding

Channel-optimized quantizer index assignment and maximum a posteriori (MAP) decoding have been extensively studied for error-resilient communications. An interesting and largely untreated problem is how to optimize the index assignment with respect to joint source-channel MAP decoding. In this paper we formulate the above problem as one of quadratic assignment, and discuss its solutions from very general to some special cases. For highly correlated Gaussian Markov sources and Hamming distortion, we can construct the optimal index assignment analytically. For general cases, simulated annealing algorithm is adopted to search for the optimal index assignment. Experimental results are presented to demonstrate the performance improvement of the index assignments optimized for MAP decoding over those designed for hard-decision decoding (e.g. Gray code). The reduction of symbol error rate and mean squared error can be as large as 40% and 50% respectively for highly correlated Gaussian Markov sources.
Multispectral Image Restoration via Inter- and Intra-Block Sparse Estimation Based on Physically-Induced Joint Spatiospectral Structures

Existing low-level vision algorithms (e.g., those for superresolution, denoising, and deblurring) were primarily motivated and optimized for precision in spatial domain. However, high precision in spectral domain is of importance for many applications in scientific and technical fields, such as spectral analysis, recognition, and classification. In quest for both high spectral and spatial fidelity, we introduce previously unexplored, physically induced, and joint spatiospectral sparsities to improve existing methods for multispectral image restoration. The bidirectional image formation model is used to reveal that the discontinuities of a multispectral image tend to align spatially across different spectral bands; in other words, the 2D Laplacians of different bands are not only sparse each, but they also agree with one the other in significance positions. Such strongly structured sparsities give rise to a new inter- and intra-block sparse estimation approach. The estimation is performed on 3D spatiospectral sample blocks, rather than on separate 2D patches, per spectral band or per luminance and chrominance component as in current practice. Moreover, intra-block and inter-block sparsity priors are combined via an intra-block  $\ell _{1,2}$ -norm minimization term and an inter-block low-rank term, strengthening the regularization of the underlying inverse problem. The new approach is tested and evaluated on two concrete applications. The superresolving and denoising multispectral images; its validity and advantages over the current state-of-the-art are established by empirical results.
Coded Acquisition of High Frame Rate Video

High frame rate video (HFV) is an important investigational tool in sciences, engineering, and military. In ultrahigh speed imaging, the obtainable temporal, spatial, and spectral resolutions are limited by the sustainable throughput of in-camera mass memory, lower bound of exposure time, and illumination conditions. To break these bottlenecks, we propose a new coded video acquisition framework that employs K ≥ 2 cameras, each of which makes random measurements of the video signal in both temporal and spatial domains. For each of the K cameras, this multicamera strategy greatly relaxes the stringent requirements in memory speed, shutter speed, and illumination strength. The recovery of HFV from these random measurements is posed and solved as a large-scale ℓ1 minimization problem by exploiting joint temporal and spatial sparsities of the 3D signal. Three coded video acquisition techniques of varied tradeoffs between performance and hardware complexity are developed: framewise coded acquisition; pixelwise coded acquisition; and columnwise-rowwise coded acquisition. The performances of these techniques are analyzed in relation to the sparsity of the underlying video signal. Simulations of these new HFV capture techniques are carried out and experimental results are reported.
Optimized prediction for geometry compression of triangle meshes

In this paper we propose a novel geometry compression technique for 3D triangle meshes. We focus on a commonly used technique for predicting vertex positions via a flipping operation using the parallelogram rule. We show that the efficiency of the flipping operation is dependent on the order in which triangles are traversed and vertices are predicted accordingly. We formulate the problem of optimally (traversing triangles and) predicting the vertices via flippings as a combinatorial optimization problem of constructing a constrained minimum spanning tree. We give heuristic solutions for this problem and show that we can achieve prediction efficiency within 17.4% on average as compared to the unconstrained minimum spanning tree which is an unachievable lower bound. We also show significant improvements over previous techniques in the literature that strive to find good traversals that also attempt to minimize prediction errors obtained by a sequence of flipping operations, albeit using a different approach.
Joint color decrosstalk and demosaicking for CFA cameras

The problem of crosstalk between different color bands was seemingly overlooked by existing color demosaicking algorithms. In this paper we propose a new joint demosaicking and decrosstalk technique that corrects channel crosstalks by adaptive least-squares inverse filtering. The new technique integrates the operations of deconvolution for crosstalk removal and interpolation for color demosaicking, and it introduces a general framework in which any spatially varying crosstalks and varying spatial-spectral correlations can be modeled and factored into the color reproduction. Simulation results show that the proposed technique is highly effective and capable to obtain both high color fidelity and sharp, clean spatial details.
Lagrangian Optimization of Two-Description Scalar Quantizers

In this paper, we study the problem of optimal design of balanced two-description fixed-rate scalar quantizer (2DSQ) under the constraint of convex codecells. Using a graph-based approach to model the problem, we show that the minimum expected distortion of the 2DSQ is a convex function of the number of codecells in the side quantizers. This property allows the problem to be solved by Lagrangian minimization for which the optimal Lagrangian multiplier exists. Given a trial multiplier, we exploit a monotonicity of the objective function, and develop a simple and fast dynamic programming technique to solve the parameterized problem. To further improve the algorithm efficiency, we propose an RD-guided search strategy to find the optimal Lagrangian multiplier. In our experiments on distributions of interest for signal compression applications the proposed algorithm improves the speed of the fastest algorithm so far, by a factor of O(K/logK), where K is the number of codecells in each side quantizer. We also assess the impact on the optimality of the convex codecell constraint. Using a published performance analysis of 2DSQ at high rates, we show that asymptotically this constraint does not preclude optimality for L 2 distortion measure, when channels have a higher than 0.12 loss rate.
On cross correlation based-discrete time delay estimation

The cross correlation function (CCF) is a powerful tool in time delay estimation and parabola functions are widely used as parametric models of it. However, no study has been done on the accuracy of the parabola approximation of CCF. In this paper, we analyze the CCF of multi-sensors and derive the analytic forms of CCF for the stationary processes of the exponential auto-correlation function with respect to two important types of sensor kernels. We demonstrate that the Gaussian function is a better and more robust approximation of CCF than the parabola in these cases. This new approach leads to higher precision in time delay estimation using the CCF peak locating strategy.
Rainbow Network Flow of Multiple Description Codes

This paper is an enquiry into the interaction between multiple description coding (MDC) and network routing. We are mainly concerned with rate-distortion optimized network flow of a multiple description (MD) source from multiple servers to multiple sinks. We aim at maximizing a collective metric of the quality of source reconstruction at all sinks, by optimally routing the MD source streams from the server nodes to the sinks. This problem turns out to be very different from conventional maximum network flow. The objective function involves not only the flow volume but also the diversity of the flow contents (i.e., distinction of descriptions), hence, the term rainbow network flow (RNF). For a general network topology, a general fidelity function, and an arbitrary distribution of MDC descriptions on the servers, we prove the RNF problem to be Max-SNP-hard. However, the problem becomes tractable in many practical scenarios, such as when MDC is balanced with descriptions of the same length and importance, when all source nodes have the complete set of MDC descriptions, and when the network topology is a tree or has only one sink. Polynomial-time RNF algorithms are developed for these cases.
CALIC-a context based adaptive lossless image codec

We propose a context-based, adaptive, lossless image codec (CALIC). CALIC obtains higher lossless compression of continuous-tone images than other techniques reported in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. CALIC puts heavy emphasis on image data modeling. A unique feature of CALIC is the use of a large number of modeling contexts to condition a non-linear predictor and make it adaptive to varying source statistics. The non-linear predictor adapts via an error feedback mechanism. In this adaptation process, CALIC only estimates the expectation of prediction errors conditioned on a large number of contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the sparse context problem. The low time and space complexities of CALIC are attributed to efficient techniques for forming and quantizing modeling contexts.
Layered Multicast With Inter-Layer Network Coding for Multimedia Streaming

Multirate multicast is a powerful methodology of multimedia communication in heterogenous networks. A variant of multirate multicast motivated by scalable multimedia streaming is layered multicast, where the transmitted signal is presented in successive data layers. With recent advances of network coding theory, many layered multicast schemes using network coding have been proposed to improve the performance of traditional routing-based layered multicast. They divide the network into different layers and construct a unirate multicast network code for each layer. However, these schemes do not perform network coding between data layers, and consequently cannot realize the full potential of network coding. In this paper, we propose a novel approach to layered multicast that allows network coding of data in different layers. This relaxation lends the proposed scheme greater flexibility in optimizing the data flow than previous layered solutions, and thus achieves higher throughput.
On Optimal Index Assignment for MAP Decoding of Markov Sequences

Index assignment and maximum a posteriori (MAP) decoding are two well-known techniques for error-resilient multimedia communications. If the two techniques are used in tandem, how they interact with each other will greatly affect the system performance. An important problem in this regard is, which has seemingly evaded attention, the design of index assignment to achieve the best possible performance of joint source-channel MAP decoding, given the source and channel statistics and given a distortion metric. In a first attempt on this design challenge, we pose the index assignment of a scalar quantizer for MAP decoding of Markov sequences coded by this quantizer as a quadratic programming problem. For Gaussian Markov sequences we derive a locally optimal index assignment by exploring some properties of the objective function. Experimental results show that the proposed scheme can find optimal or near-optimal solutions. The optimized index assignment can achieve much lower average symbol error rate than conventional schemes
On the location of plug-in relay devices for indoor power line communication environment

Indoor power line communication (PLC) systems experience high attenuation and frequency-selective channel condition, in addition to different types of colored noises. Its performance is heavily influenced by the length and topology of the in-house power cable wiring. By exploiting the broadcast property of the indoor power grid to each outlet, this paper presents a simple but practical relay-based cooperative communication scheme for indoor PLC networks. By plugging a relay device into a third outlet between the transmitter and receiver, reliable signal-to-noise ratio (SNR) can be cooperatively achieved by setting proper system parameters. The condition for the optimal location of the plug-in relay device has been identified. Numerical simulation results show that with the cost of extra relay device, more reliable performance can be obtained with respect to the conventional direct transmission scheme.
On the minimum sum rate of Gaussian multiterminal source coding: New proofs

We show that the minimum sum rate of the Gaussian multiterminal source coding problems can be derived in a unified manner by exploiting the semidefinite partial order of the distortion covariance matrices associated with the MMSE estimation and the so-called reduced optimal linear estimation. In contrast to the existing proofs, the new method does not rely on Shannon's entropy power inequality. Furthermore, this new method leads to a direct proof of the minimum sum rate of the Gaussian two-terminal source coding problem without coupling it to a Gaussian CEO problem.