nils-herrmann/clean_scirepeval huggingface dataset:
Joint classification and pairing of human chromosomes

We reexamine the problems of computer-aided classification and pairing of human chromosomes, and propose to jointly optimize the solutions of these two related problems. The combined problem is formulated into one of optimal three-dimensional assignment with an objective function of maximum likelihood. This formulation poses two technical challenges: 1) estimation of the posterior probability that two chromosomes form a pair and the pair belongs to a class and 2) good heuristic algorithms to solve the three-dimensional assignment problem which is NP-hard. We present various techniques to solve these problems. We also generalize our algorithms to cases where the cell data are incomplete as often encountered in practice.
Fast Generalized Motion Estimation and Superresolution

We propose a new superresolution algorithm based on a fast motion estimation technique. Two stages of this algorithm, namely, motion estimation and high-resolution reconstruction, rely on an area-based interpolation scheme that involves intersecting two pixel grids in arbitrary orientation, displacement, and scaling. We develop a fast approximate solution of the above problem, whose exact solution is prohibitively expensive. Also, gradient descent algorithm is used for fast convergence of the motion estimation algorithm. Experimental results demonstrate the good performance of the proposed superresolution algorithm as well its robustness against noise.
Model-based non-linear estimation for adaptive image restoration

We propose a new image restoration algorithm that is driven by an adaptive piecewise autoregressive model (PAR). The strength of the new algorithm is its ability to preserve spatial structures better than its predecessors. The high adaptability is achieved by locally fitting 2D image waveform to the PAR model in moving windows. The problem is posed as one of nonlinear least-square estimation of both PAR parameters and original pixels, constrained by the degradation function. Robust solutions of the underlying underdetermined inverse problem are obtained by an innovative use of multiple PAR models that circumvent the issue of model overfitting, and by applying a structured total least-square technique.
Queue Stability-Based Virtual Resource Allocation for Virtualized Wireless Networks With Self-Backhauls

Wireless network virtualization and self-backhauled small-cell networks are considered as two promising technologies to enable next generation wireless networks. In this paper, we propose an integrated framework for the wireless network virtualization problem under the frequency division duplexing self-backhaul mechanism. In this context, the dynamic virtual resource allocation issue is formulated as an optimization problem, aiming at the maximization of the average total utility of the virtualized network. In the mean time, the network queue stability, the minimum average data rate for each service provider and the capacity constraint of the backhaul link are expected to be satisfied. To solve this optimization problem, the Lyapunov optimization method is adopted to overcome the challenge from the stochastic properties exhibited in the original problem, and a real-time scheduling algorithm is then investigated based on the current queue status information and the channel state information. Specifically, a Lagrange dual decomposition and an improved particle swarm optimization with random mutation are employed to ensure the effectiveness of the proposed algorithm. Simulation results are presented to verify the proposed algorithm.
Improvement of Color Video Demosaicking in Temporal Domain

Color demosaicking is critical to the image quality of digital still and video cameras that use a single-sensor array. Limited by the mosaic sampling pattern of the color filter array (CFA), color artifacts may occur in a demosaicked image in areas of high-frequency and/or sharp color transition structures. However, a color digital video camera captures a sequence of mosaic images and the temporal dimension of the color signals provides a rich source of information about the scene via camera and object motions. This paper proposes an inter-frame demosaicking approach to take advantage of all three forms of pixel correlations: spatial, spectral, and temporal. By motion estimation and statistical data fusion between adjacent mosaic frames, the new approach can remove much of the color artifacts that survive intra-frame demosaicking and also improve tone reproduction accuracy. Empirical results show that the proposed inter-frame demosaicking approach consistently outperforms its intra-frame counterparts both in peak signal-to-noise measure and subjective visual quality
New Results on Multiple Descriptions in the Wyner-Ziv Setting

Some new results of multiple descriptions in the Wyner-Ziv setting (MDWZ) are presented. MDWZ uses two pieces of side information (SI) for two different side decoders. Both SI are available to the central decoder, but none to the encoder. The complete achievable region for a quadratic Gaussian MDWZ system is derived. This result is an extension of Ozarow's result on multiple descriptions of a Gaussian source without SI and Diggavi-Vaishampayan's result on common SI. It is shown that a refinement layer for the central decoder is indispensable if the central distortion is sufficiently small. The tight rate distortion region is also given if one of the side decoders is required to perfectly reconstruct a deterministic function of the source.
Random Walk Graph Laplacian-Based Smoothness Prior for Soft Decoding of JPEG Images

Given the prevalence of joint photographic experts group (JPEG) compressed images, optimizing image reconstruction from the compressed format remains an important problem. Instead of simply reconstructing a pixel block from the centers of indexed discrete cosine transform (DCT) coefficient quantization bins (hard decoding), soft decoding reconstructs a block by selecting appropriate coefficient values within the indexed bins with the help of signal priors. The challenge thus lies in how to define suitable priors and apply them effectively. In this paper, we combine three image priors-Laplacian prior for DCT coefficients, sparsity prior, and graph-signal smoothness prior for image patches-to construct an efficient JPEG soft decoding algorithm. Specifically, we first use the Laplacian prior to compute a minimum mean square error initial solution for each code block. Next, we show that while the sparsity prior can reduce block artifacts, limiting the size of the overcomplete dictionary (to lower computation) would lead to poor recovery of high DCT frequencies. To alleviate this problem, we design a new graph-signal smoothness prior (desired signal has mainly low graph frequencies) based on the left eigenvectors of the random walk graph Laplacian matrix (LERaG). Compared with the previous graph-signal smoothness priors, LERaG has desirable image filtering properties with low computation overhead. We demonstrate how LERaG can facilitate recovery of high DCT frequencies of a piecewise smooth signal via an interpretation of low graph frequency components as relaxed solutions to normalized cut in spectral clustering. Finally, we construct a soft decoding algorithm using the three signal priors with appropriate prior weights. Experimental results show that our proposal outperforms the state-of-the-art soft decoding algorithms in both objective and subjective evaluations noticeably.
Video denoising using 3-D Hybrid Wavelets and Directional filter banks

We propose a new family of nonredundant 3D directional transforms that are useful for video signals. In our construction, taking into account the correlation amongst frames of video, we first decorrelate the temporal data using a stage of 1D wavelet transform and then we employ the efficient 2D Hybrid Wavelets and Directional filter banks (HWD) transform family to the resulting spatial data where we achieve 3D HWD transform family. We construct translation-invariant version of the proposed family and show its efficiency in video denoising compared to other wavelet-based methods through several experiments.
Automatic tonal harmonization for multi-spectral mosaics

When producing a mosaic of multiple multi-spectral images one needs to harmonize the colours so that the tone transition is smooth from one image to the other. Given two images Ima and Imb, a transform T is sought to map Imb to an image that is harmonious in multi-spectral appearance to Ima. We give the above problem of tonal harmonization an analytical framework, in which both ideal and practical solutions of the problem are studied. Using a physically motivated image formation model, we prove that a perfect tonal harmonizing operator cannot in general be found, but that whenever such an operator exists it is linear. In the latter case, finding the optimal harmonizing transformation can be cast as a linear programme (LP), which is a type of problem that can be efficiently solved using known techniques. Finally, strong empirical evidence is provided for the efficacy of the proposed solution.
MAP decoding of variable length code with substitution, insertion and deletion

We propose a soft decision decoding algorithm for a variable-length encoded Markov source transmitted via a noisy channel that has all three types of errors - substitution, deletion, and insertion. The decoder aims to find a sequence, X, that maximizes the posterior probability, given a received sequence, Y. First, we assume the channel is a binary symmetric channel (BSC), and the input of the channel is a first order Markov source. We convert the MAP (maximum a posteriori) problem to one of finding a single-source longest path in a directed acyclic graph, which can be solved by dynamic programming. We also present a generalization of the BSC to include both insertion and deletion errors, and the development of a soft decision MAP decoding algorithm for this more challenging case, which is our main contribution.
ℓ 2 optimized predictive image coding with ℓ ∞ bound

In many scientific, medical and defense applications of image/video compression, an l∞ error bound is required. However, pure l∞-optimized image coding, colloquially known as near-lossless image coding, is prone to structured errors such as contours and speckles if the bit rate is not sufficiently high; moreover, previous l∞-based image coding methods suffer from poor rate control. In contrast, the l2 error metric aims for average fidelity and hence preserves the subtlety of smooth waveforms better than the l∞ error metric and it offers fine granularity in rate control; but pure l2-based image coding methods (e.g., JPEG 2000) cannot bound individual errors as the l∞-based methods can. This paper presents a new compression approach to retain the benefits and circumvent the pitfalls of the two error metrics.
Optimal packetization of embedded bitstreams

To achieve error resilience, digital communication systems typically partition a data file into blocks of samples in the time domain which represent small cohesive segments of the input source, be it image, video or audio. We consider the problem of packing a set of embedded bitstreams B/sub k/ into M packets of payload L. Optimal packetization is to select ML bits to fill in M packets while satisfying certain alignment constraints that are imposed by error resilience designs, and at the same time minimizing the distortion. Just as source and channel coding have conflicting objectives of removing and adding redundancy, error resilience via packetization will somewhat reduce the rate distortion performance of the compression code when the transmission is error free. Our goal is to minimize such losses of coding efficiency.
Wavelet coding of volumetric medical images with high throughput and operability.

In this paper we propose a new 3D wavelet-based scalable lossless coding scheme for compression of volumetric medical images. Aiming to improve the productivity of radiologists and the cost-effectiveness of the system, we strive to achieve high decoder throughput, random access to coded data volume, progressive transmission, and high compression ratio in a balanced design approach. These desirable functionalities are realized by a modified 3D wavelet transform tailored to volumetric medical images.
An efficient range search algorithm for visualizing extrema of volume data

A fast range search algorithm for visualizing extrema of d-dimensional volume data in real time as the user interactively moves the query range is presented. The algorithm is based on an efficient data structure, called index heap, which needs only O(N/log N) space and O(d2/sup d/N) preprocessing time to be set up, where N is the size of the d-dimensional data volume. The algorithm can answer an extremum query in O(4/sup d/) expected time, and its worst-case time complexity is O(2/sup d/ log N) per query. For dimensions two and three, the range search for extrema is effected in average O(1) time per query independently of the size of query range. Unlike previous range query algorithms in the computational geometry literature, the proposed algorithm is very simple and can be easily implemented.<<ETX>>
Index assignment design for three-description lattice vector quantization

In this paper, we propose a new index assignment scheme for the 3-description case, which aims to find a 3-tuple sublattice points to represent a fine lattice point. The design is made such that each edge of the triangle formed by the 3-tuple points needs to be as short as possible while the gravity center of the triangle is as close as possible to the fine lattice point. With a delicate sublattice partition, a well designed construction and mapping is developed to minimize the expected distortion
Multiple Description Image Coding with Local Random Measurements

In this paper, an effective multiple description image coding technique is developed to achieve competitive coding efficiency at low encoder complexity, while being standard compliant. The new technique is particularly suitable for visual communication over packet-switched networks and with resource-deficient wireless devices. To keep the encoder simple and standard compliant, multiple descriptions are produced by quincunx spatial multiplexing. Each side description is a polyphase down sampled version of the input image, but the conventional low-pass filter prior to downsampling is replaced by a local random binary convolution kernel. The pixels of each resulting side description are local random measurements and placed in the original spatial configuration. The advantages of local random measurements are two folds: 1) preservation of high-frequency image features that are otherwise discarded by low-pass filtering, 2) each side description remains a conventional image and can therefore be coded by any standardized codec to remove statistical redundancy of larger scales. The decoder performs joint upsampling of received description(s) and recovers the image from local random measurements in a framework of compressive sensing. Experimental results demonstrate that the proposed multiple description image codec is competitive in rate-distortion performance compared with existing methods, with a unique strength of recovering fine details and sharp edges at low bit rates.
Optimal bi-level quantization and its application to multilevel quantization

A special form of optimal quantization, optimal bilevel quantization, is studied. A fixed-point method is embedded in a search scheme to find all the locally optimal bilevel quantizers, resulting in an algorithm for computing the globally optimal bilevel quantizer. Some interesting relations between the optimal bilevel quantizer, and the mean and the median of the density function p(x) are explored. Efficient algorithms for computing optimal bilevel quantizers are proposed. The application of these results to optimal quantization in general is discussed.
Robust Color Demosaicking With Adaptation to Varying Spectral Correlations

Almost all existing color demosaicking algorithms for digital cameras are designed on the assumption of high correlation between red, green, blue (or some other primary color) bands. They exploit spectral correlations between the primary color bands to interpolate the missing color samples, but in areas of no or weak spectral correlations, these algorithms are prone to large interpolation errors. Such demosaicking errors are visually objectionable because they tend to correlate with object boundaries and edges. This paper proposes a remedy to the above problem that has long been overlooked in the literature. The main contribution of this work is a hybrid demosaicking approach that supplements an existing color demosaicking algorithm by combining its results with those of adaptive intraband interpolation. This is formulated as an optimal data fusion problem, and two solutions are proposed: one is based on linear minimum mean-square estimation and the other based on support vector regression. Experimental results demonstrate that the new hybrid approach is more robust and eliminates the worst type of color artifacts of existing color demosaicking methods.
Minimax multiresolution scalar quantization

We consider the problem of design and analysis of optimal L/sub /spl infin// (minmax) multiresolution scalar quantizers (MRSQ). The overall multiresolution L/sub /spl infin// distortion of an MRSQ is denned to be a weighted sum of L/sub /spl infin// distortions over all refinement levels of the MRSQ. The weight for a refinement level usually denotes the probability that the MRSQ will operate at that level (rate). An interesting relation of the problem to the design of optimal binary prefix codes under a code cell contiguity constraint is established: Lower bounds for the overall multiresolution L/sub /spl infin// distortion are derived based on this relation. Provably optimal as well as fast, near optimal algorithms are also developed for practically interesting scenarios. Furthermore, the performance penalty incurred by making a scalable quantizer embedded (progressively refinable) is analyzed. It is shown that constraining the quantizers to be embedded would on average increase the L/sub /spl infin// quantization error by at least 44%.
Image coding via bintree segmentation and texture VQ

Image compression is often approached from an angle of statistical image classification. For instance, VQ-based image coding methods compress image data by classifying image blocks into representative two-dimensional patterns (codewords) that statistically approximate the original data. Another image compression approach that naturally relates to image classification is segmentation-based image coding (SIC). In SIC, we classify pixels into segments of certain uniformity or similarity, and then encode the segmentation geometry and the attributes of the segments. Image segmentation in SIC has to meet some more stringent requirements than in other applications such as computer vision and pattern recognition. An efficient SIC coder has to strike a good balance between accurate semantics and succinct syntax of the segmentation. From a pure classification point of view, free form segmentation by relaxation, region-growing, or split-and-merge techniques offers an accurate boundary representation. But the resulting segmentation geometry is often too complex to have a compact description, defeating the purpose of image compression. Instead, we adopt a bintree-structured segmentation scheme. The bintree is a binary tree created by recursive rectilinear bipartition of an image.
Multiresolution polygonal approximation of digital curves

We propose an optimal split algorithm for multiresolution polygonal approximation of digital curves. Instead of using a sequence of heuristic split steps as in previous methods, we apply optimal approximation to obtain the next (higher) resolution levels using the previous (lower) resolution level as a starting point. We compare the proposed approach against a fast heuristic multiresolution algorithm based on a merge strategy under the L/sub 2/ error measure.
Inter-block consistent soft decoding of JPEG images with sparsity and graph-signal smoothness priors

Given the prevalence of JPEG compressed images on the Internet, image reconstruction from the compressed format remains an important and practical problem. Instead of simply reconstructing a pixel block from the centers of assigned DCT coefficient quantization bins (hard decoding), we propose to jointly reconstruct a neighborhood group of pixel patches using two image priors while satisfying the quantization bin constraints. First, we assume that a pixel patch can be approximated as a sparse linear combination of atoms from an offline-learned over-complete dictionary. Second, we assume that a patch, when interpreted as a graph-signal, is smooth with respect to an appropriately defined graph that captures the estimated structure of the target image. Finally, neighboring patches in the optimization have sufficient overlaps and are forced to be consistent, so that blocking artifacts typical in JPEG decoded images are avoided. To find the optimal group of patches, we formulate a constrained optimization problem and propose a fast alternating algorithm to find locally optimal solutions. Experimental results show that our proposed algorithm outperforms state-of-the-art soft decoding algorithms by up to 1.47dB in PSNR.
A Novel Color Image Encryption Scheme Using Rectangular Transform-Enhanced Chaotic Tent Maps

In recent years, the chaos-based cryptographic algorithms have attracted a lot of attention. Specially, chaotic tent map (CTM)-based schemes show some good performances in randomness properties and security level. However, several shortcomings still can be found from them. In this paper, based on the security analysis of the pure CTM-based scheme, we propose a novel image encryption algorithm by using the combination of the rectangular transform and the CTM principle. It encrypts the three channels of the plain image at the same time, and these channel encryptions associate with each other. In addition, by generating the key streams related to both the secret keys and the plain image, its key-sensitivity has been further improved. The security of the proposed scheme has been verified by security analysis and experimental evaluations, and our results show that many drawbacks of pure CTM-based schemes have been overcome.
Model-Guided Adaptive Recovery of Compressive Sensing

For the new signal acquisition methodology of compressive sensing (CS) a challenge is to find a space in which the signal is sparse and hence recoverable faithfully. Given the nonstationarity of many natural signals such as images, the sparse space is varying in time or spatial domain. As such, CS recovery should be conducted in locally adaptive, signal-dependent spaces to counter the fact that the CS measurements are global and irrespective of signal structures. On the contrary existing CS reconstruction methods use a fixed set of bases (e.g., wavelets, DCT, and gradient spaces) for the entirety of a signal. To rectify this problem we propose a new model-based framework to facilitate the use of adaptive bases in CS recovery. In a case study we integrate a piecewise stationary autoregressive model into the recovery process for CS-coded images, and are able to increase the reconstruction quality by $2 \thicksim 7$dB over existing methods. The new CS recovery framework can readily incorporate prior knowledge to boost reconstruction quality.
Steganalysis of LSB embedding in multimedia signals

We present a new, principled steganalytic approach to estimate the length of messages, if any, hidden in the least significant bits of digitized continuous signals such as images and audio. Our estimates of LSB-embedded messages are based on some intrinsic statistical properties of sample pairs drawn from the signal. We also give bounds on the estimation errors. Furthermore, possible attacks on our new steganalytic approach and our countermeasures are also discussed.