nils-herrmann/clean_scirepeval huggingface dataset:
Data synchronization with timing

This paper proposes and analyzes data synchronization techniques that not only resynchronize after encoded bits are corrupted by insertion, deletion, or substitution errors, but also produce estimates of the time indexes of the decoded data symbols, in order to determine their positions in the original source sequence. The techniques are based on block codes, and the estimates are of the time indexes modulo some integer T, called the timing span, which is desired to be large. Several types of block codes that encode binary data are analyzed on the basis of the maximum attainable timing span for a given coding rate R (or, equivalently, redundancy /spl rho/=1-R) and permissible resynchronization delay D. It is found that relatively simple codes can asymptotically attain the maximum timing span among such block codes, which grows exponentially with delay, with exponent D(1-R)+o(D). Thus, large timing span can be attained with little redundancy and only moderate values of delay.
Decision trees for vector quantizer codebook searching

Several incremental algorithms are presented for designing fixed length decision trees for searching the codebook of a vector quantizer (VQ). These trees are then used as the basis for a two-stage VQ, wherein the first stage is a high-rate, structured VQ with a fast quantization algorithm and the second stage is a low-rate optimal VQ. The mean square error and complexity of the resulting two-stage VQs are presented for i.i.d. Gaussian and speech sources, and are compared with both TSVQ and previous tree based two-stage VQ designs.<<ETX>>
Asymptotic distribution of the errors in scalar and vector quantizers

High-rate (or asymptotic) quantization theory has found formulas for the average squared length (more generally, the qth moment of the length) of the error produced by various scalar and vector quantizers with many quantization points. In contrast, this paper finds an asymptotic formula for the probability density of the length of the error and, in certain special cases, for the probability density of the multidimensional error vector, itself. The latter can be used to analyze the distortion of two-stage vector quantization. The former permits one to learn about the point density and cell shapes of a quantizer from a histogram of quantization error lengths. Histograms of the error lengths in simulations agree well with the derived formulas. Also presented are a number of properties of the error density, including the relationship between the error density, the point density, and the cell shapes, the fact that its qth moment equals Bennett's integral (a formula for the average distortion of a scalar or vector quantizer), and the fact that for stationary sources, the marginals of the multidimensional error density of an optimal vector quantizer with large dimension are approximately i.i.d. Gaussian.
Multidimensional Manhattan Sampling and Reconstruction

This paper introduces Manhattan sampling in two and higher dimensions, and proves sampling theorems for them. In 2-D, Manhattan sampling, which takes samples densely along a Manhattan grid of lines, can be viewed as sampling on the union of two rectangular lattices, one dense horizontally and the other vertically, with the coarse spacing of each being a multiple of the fine spacing of the other. The sampling theorem shows that the images bandlimited to the union of the Nyquist regions of the two rectangular lattices can be recovered from their Manhattan samples, and an efficient procedure for doing so is given. Such recovery is possible even though there is an overlap among the spectral replicas induced by Manhattan sampling. In three and higher dimensions, there are many possible configurations for Manhattan sampling, each consisting of the union of special rectangular lattices called bi-step lattices. This paper identifies them, proves a sampling theorem showing that the images bandlimited to the union of the Nyquist regions of the bi-step rectangular lattices are recoverable from Manhattan samples, presents an efficient onion-peeling procedure for doing so, and shows that the union of Nyquist regions is as large as any bandlimited region, such that all images supported by such can be stably reconstructed from samples taken at the rate of the Manhattan sampling. It also develops a special representation for the bi-step lattices with a number of useful properties. While most of this paper deals with continuous-space images, Manhattan sampling of discrete-space images is also considered, for infinite, as well as finite, support images.
Reduced storage tree-structured vector quantization

Methods are presented for reducing the table storage required when encoding and decoding with tree-structured vector quantization (TSVQ). The latter is a technique that requires many fewer arithmetic operations than unstructured vector quantization but at least as much storage. The new methods for reducing storage integrate a secondary quantizer into the design of TSVQ, so as to produce a tree structure that can be efficiently stored. Two of the techniques make use of the hierarchical nature of TSVQ. It is shown that, at the expense of a decrease in signal-to-quantization-noise ratio of 0.3 dB or less, encoder storage can be reduced by a factor of about ten and decoder storage can be reduced by a factor of about five. Comparisons are made with the method of codebook sharing.<<ETX>>
Objective similarity metrics for scenic bilevel images

This paper proposes new objective similarity metrics for scenic bilevel images, which are images containing natural scenes such as landscapes and portraits. Though percentage error is the most commonly used similarity metric for bilevel images, it is not always consistent with human perception. Based on hypotheses about human perception of bilevel images, this paper proposes new metrics that outperform percentage error in the sense of attaining significantly higher Pearson and Spearman-rank correlation coefficients with respect to subjective ratings. The new metrics include Adjusted Percentage Error, Bilevel Gradient Histogram and Connected Components Comparison. The subjective ratings come from similarity evaluations described in a companion paper. Combinations of these metrics are also proposed, which exploit their complementarity to attain even better performance.
A New Fixed-Rate Quantization Scheme Based on Arithmetic Coding

The construction of fixed-rate vector quantizers based on entropy-coded scalar quantizers has been suggested in . In , a particular case of such quantizers based on prefix-coded scalar quantizers was suggested and a very simple binary encoding scheme was presented as well as several reduced-complexity search methods. In this paper, we use arithmetic coding as the binary encoding scheme and show that good performance and low complexity are attained.
A nonlinear VQ-based predictive lossless image coder

A new lossless predictive image coder is introduced and tested. The predictions are made with a nonlinear, vector quantizer based, adaptive predictor. The prediction errors are losslessly compressed with an arithmetic coder that presumes they are Laplacian distributed with variances that are estimated during the prediction process, as in the approach of Howard and Vitter (1992).<<ETX>>
New results on coding of stationary nonergodic sources

Two results on the coding of stationary nonergodic sources are presented. The first is a source coding theorem stating that there exist variable-rate codes with performance arbitrarily close to the rate-distortion function of the stationary nonergodic source. The second is a converse information transmission theorem. It is shown that the distortion which results when the source is transmitted across a channel with capacity C is no less than the least distortion achievable by fixed-rate codes with rate C .
Image Coding By Perceptural Pursuit

We introduce an image coding algorithm based on the cortex transform, matching pursuit and perceptual pruning that selects and codes only the most perceptually relevant image components, as determined by a new image indistinguishability criterion that derives from a maximum likelihood paradigm. We demonstrate that this method selects image components that produce images with higher subjective quality than matching pursuit with the same number of components. We also show that our perceptual quantization rule makes a substantial gain over uniform scalar quantization. Finally, we present image coding results of high quality at low encoding rates.
Joint and tandem source-channel coding with delay constraints

Two common source-channel coding strategies, joint and tandem, are compared on the basis of distortion vs. delay by analyzing specific representatives of each when transmitting analog data samples across a binary symmetric channel. Channel-optimized transform coding is the joint source-channel coding strategy; transform coding with Reed-Solomon coding is the tandem strategy. For each strategy, formulas for the mean-squared error and delay are found and used to minimize distortion subject to a delay constraint, for data modeled as Gauss-Markov. The results of such optimizations suggest there is a threshold such that when the permissible delay is above this threshold, tandem coding is better, and when below the threshold, channel-optimized transform coding is better.
On the continuity of the stationary state distribution of DPCM

Continuity and singularity properties of the stationary state distribution of differential pulse code modulation (DPCM) are explored. Two-level DPCM (i.e. delta modulation) operating on a first-order autoregressive source is considered, and it is shown that, when the magnitude of the DPCM prediction coefficient is between zero and one-half, the stationary state distribution is singularly continuous; i.e. it is not discrete but concentrates on an uncountable set with a Lebesgue measure of zero. Consequently, it cannot be represented with a probability density function. For prediction coefficients with magnitude greater than or equal to one-half, the distribution is pure, i.e. either absolutely continuous and representable with a density function, or singular. This problem is compared to the well-known and still substantially unsolved problem of symmetric Bernoulli convolutions.
Perceptual similarity metrics for retrieval of natural textures

We investigate perceptual similarity metrics for the content-based retrieval of natural textures. The goal is to find perceptually similar textures that may have significant differences on a point-by-point basis. The evaluation of such metrics typically requires extensive and cumbersome subjective tests. The focus of this paper is on the recovery of textures that are “identical” to the query texture, in the sense that they are pieces of the same texture. This is important in content-based image retrieval (CBIR), where one may want to find images that contain a particular texture, as well as in some near-threshold coding applications. The advantage of evaluating metric performance in the context of retrieving identical textures is that the ground truth is known, and therefore no subjective tests are required. We can thus compare the performance of different metrics on large sets of textures, and derive meaningful statistical results.We evaluate the performance of a recently proposed structural texture similarity metric on grayscale textures, and compare it to that of PSNR, as well as space domain and complex wavelet structural similarity metrics. Experimental results with a database of 748 distinct texture images, indicate that the new metric outperforms the other metrics in the retrieval of identical textures, according to a number of standard statistical measures.
Cutset sampling and reconstruction of images

This paper presents a new approach to sampling images in which samples are taken on a cutset with respect to a graphical image model. The cutsets considered are Manhattan grids, for example every Nth row and column of the image. Cutset sampling is motivated mainly by applications with physical constraints, e.g. a ship taking water samples along its path, but also by the fact that dense sampling along lines might permit better reconstruction of edges than conventional sampling at the same density. The main challenge in cutset sampling lies in the reconstruction of the unsampled blocks. As a first investigation, this paper uses segmentation followed by linear estimation. First, the ACA method  is modified to segment the cutset, followed by a binary Markov random field (MRF) inspired segmentation of the unsampled blocks. Finally, block interiors are estimated from the pixels on their boundaries, as well as their segmentation, with methods that include a generalization of bilinear interpolation and linear MMSE methods based on Gaussian MRF models or separable autocorrelation models. The resulting reconstructions are comparable to those obtained with conventional sampling at higher sampling densities, but not generally as good as conventional sampling at lower rates.
Image interpolation from Manhattan cutset samples via orthogonal gradient method

Cutset sampling is a new approach to image sampling where 2D data is recorded densely along intersecting lines. A special case of cutset sampling is Manhattan sampling, where data is sampled densely along evenly-spaced rows and columns. This paper presents a new method for interpolating pixels from their Manhattan samples called the orthogonal gradient (OG) algorithm, which exploits the fact that pixels tend to be correlated along the direction orthogonal to the image gradient. Such an approach is enhanced by Manhattan sampling, where dense sampling along straight lines allows for better reconstruction of both sharp and soft image edges. In particular, the OG algorithm alternates between solving a constrained optimization problem, and changing the weights of the optimization problem according to the direction of the gradient of each new image estimate. The proposed method improves upon previous Manhattan interpolation algorithms, both qualitatively as well as in mean-squared error. Furthermore, unlike the previous algorithms, the OG algorithm can easily be extended to any sampling scheme.
Lossy Cutset Coding of Bilevel Images Based on Markov Random Fields

An effective, low complexity method for lossy compression of scenic bilevel images, called lossy cutset coding, is proposed based on a Markov random field model. It operates by losslessly encoding pixels in a square grid of lines, which is a cutset with respect to a Markov random field model, and preserves key structural information, such as borders between black and white regions. Relying on the Markov random field model, the decoder takes a MAP approach to reconstructing the interior of each grid block from the pixels on its boundary, thereby creating a piecewise smooth image that is consistent with the encoded grid pixels. The MAP rule, which reduces to finding the block interiors with fewest black-white transitions, is directly implementable for the most commonly occurring block boundaries, thereby avoiding the need for brute force or iterative solutions. Experimental results demonstrate that the new method is computationally simple, outperforms the current lossy compression technique most suited to scenic bilevel images, and provides substantially lower rates than lossless techniques, e.g., JBIG, with little loss in perceived image quality.
On the asymptotic distribution of the errors in vector quantization

In a recent paper, Lee and Neuhoff (see ibid., vol.42, no.2, p446-60, 1996) found an asymptotic formula for the distribution of the length of the errors produced by a vector quantizer with many quantization points. This distribution depends on the source probability density, the quantizer point density, and the quantizer shape profile. (The latter characterizes the shapes of the quantization cells as a function of position.) The purpose of this paper is to give a rigorous derivation of this formula by identifying precise conditions under which it is shown that if a sequence of vector quantizers with a given dimension and an increasing number of points has "specific" point densities and "specific" shape profiles converging to a "model" point density and a "model" shape profile, respectively, then the distribution of the length of the quantization error, suitably normalized, converges to the aforementioned formula, with the model point density and the model shape profile substituted.
Piecewise linear tree-structured models for lossless image compression

This paper presents a probabilistic model for use in lossless image compressors. For each pixel the model provides a conditional distribution, which in the most simple case is a discretized Gaussian. The mean and variance of this Gaussian are determined by using that pixel's neighbors to search a tree to find an autoregressive model, which is then applied to those same neighbors. Finally, arithmetic coding transmits the pixel. This paper also shows how to design this tree and find the distribution parameters associated with each leaf.
Asymptotic analysis of optimal fixed-rate uniform scalar quantization

Studies the asymptotic characteristics of uniform scalar quantizers that are optimal with respect to mean-squared error (MSE). When a symmetric source density with infinite support is sufficiently well behaved, the optimal step size /spl Delta//sub N/ for symmetric uniform scalar quantization decreases as 2/spl sigma/N/sup -1/V~/sup -1/(1/6N/sup 2/), where N is the number of quantization levels, /spl sigma//sup 2/ is the source variance and V~/sup -1/(/spl middot/) is the inverse of V~(y)=y/sup -1/ /spl int//sub y//sup /spl infin// P(/spl sigma//sup -1/X>x) dx. Equivalently, the optimal support length N/spl Delta//sub N/ increases as 2/spl sigma/V~/sup -1/(1/6N/sup 2/). Granular distortion is asymptotically well approximated by /spl Delta//sub N//sup 2//12, and the ratio of overload to granular distortion converges to a function of the limit /spl tau//spl equiv/lim/sub y/spl rarr//spl infin//y/sup -1/E[X|X>y], provided, as usually happens, that /spl tau/ exists. When it does, its value is related to the number of finite moments of the source density, an asymptotic formula for the overall distortion D/sub N/ is obtained, and /spl tau/=1 is both necessary and sufficient for the overall distortion to be asymptotically well approximated by /spl Delta//sub N//sup 2//12. Applying these results to the class of two-sided densities of the form b|x|/sup /spl beta//e(-/spl alpha/|x|/sup /spl alpha//), which includes Gaussian, Laplacian, Gamma, and generalized Gaussian, it is found that /spl tau/=1, that /spl Delta//sub N/ decreases as (ln N)/sup 1//spl alpha///N, that D/sub N/ is asymptotically well approximated by /spl Delta//sub N//sup 2//12 and decreases as (ln N)/sup 2//spl alpha///N/sup 2/, and that more accurate approximations to /spl Delta//sub N/ are possible. The results also apply to densities with one-sided infinite support, such as Rayleigh and Weibull, and to densities whose tails are asymptotically similar to those previously mentioned.
Low-Resolution Scalar Quantization for Gaussian Sources and Absolute Error

This correspondence considers low-resolution scalar quantization for a memoryless Gaussian source with respect to absolute error distortion. It shows that slope of the operational rate-distortion function of scalar quantization is infinite at the point Dmax where the rate becomes zero. Thus, unlike the situation for squared error distortion, or for Laplacian and exponential sources with squared or absolute error distortion, for a Gaussian source and absolute error, scalar quantization at low rates is far from the Shannon rate-distortion function, i.e., far from the performance of the best lossy coding technique
Low-resolution scalar quantization for Gaussian sources and squared error

This correspondence analyzes the low-resolution performance of entropy-constrained scalar quantization. It focuses mostly on Gaussian sources, for which it is shown that for both binary quantizers and infinite-level uniform threshold quantizers, as D approaches the source variance /spl sigma//sup 2/, the least entropy of such quantizers with mean-squared error D or less approaches zero with slope -log/sub 2/e/2/spl sigma//sup 2/. As the Shannon rate-distortion function approaches zero with the same slope, this shows that in the low-resolution region, scalar quantization with entropy coding is asymptotically as good as any coding technique.
Photographic paper classification via local radius index metric

The ability to recognize different types of photographic paper can be useful when attributing photographs to specific photographers or eras. This paper uses the recently developed texture feature, Local Radius Index (LRI), and the similarity metrics based on it, to classify photographic papers, from raking light photos of such. The results show that LRI leads to better classification of photographic papers than previous methods. They also suggest the ability to distinguish photographic papers by brand/style and reflectance.
Local radius index - a new texture similarity feature

We develop a new type of statistical texture image feature, called a Local Radius Index (LRI), which can be used to quantify texture similarity based on human perception. Image similarity metrics based on LRI can be applied to image compression, identical texture retrieval and other related applications. LRI extracts texture features by using simple pixel value comparisons in space domain. Better performance can be achieved when LRI is combined with complementary texture features, e.g., Local Binary Patterns (LBP) and the proposed Subband Contrast Distribution. Compared with Structural Texture Similarity Metrics (STSIM), the LRI-based metrics achieve better retrieval performance with much less computation. Applied to the recently developed structurally lossless image coder, Matched Texture Coding, LRI enables similar performance while significantly accelerating the encoding.
One-dimensional least-squares model-based halftoning

A least-squares model-based approach to digital halftoning is proposed. It exploits both a printer model and a model for visual perception. It obtains an optimal halftoned reproduction by minimizing the squared error between the response of the cascade of the printer and visual models to the binary image and the response of the visual model to the original gray-scale image. Least-squares model-based halftoning uses explicit eye models and relies on printer models that predict distortions and exploit them to increase, rather than decrease, both spatial and gray-scale resolution. The authors examine the one-dimensional case, in which each row or column of the image is halftoned independently. One-dimensional least-squares halftoning is implemented, in closed form, with the Viterbi algorithm. Experiments show that it produces better spatial and gray-scale resolution than conventional one-dimensional techniques and eliminates the problems associated with the modified (to account for printer distortions) error diffusion algorithm.<<ETX>>