nils-herrmann/clean_scirepeval huggingface dataset:
Cutset width and spacing for Reduced Cutset Coding of Markov random fields

This paper analyzes the rate, redundancy and complexity of a generalized version of Reduced Cutset Coding (RCC)  in which cutset lines can consist of more than one image row. We show that increasing the thickness of the lines reduces coding rate for the cutset, increasing the spacing of the lines increases the coding rate of the non-cutset pixels, though the coding rate of the latter is always strictly less than that of the former. We show that the redundancy of RCC can be decomposed into two terms, a correlation redundancy due to coding the components of the cutset independently, and a distribution redundancy due to coding the cutset as a reduced Ising model . We show that the correlation redundancy decreases with increasing spacing of the lines, and that the parameter of the reduced Ising model approaches the true parameter monotonically in the thickness of the lines. We present a learning phase in which the true parameter of the model is learned as well as the parameter of the reduced model. Numerical simulations show that only small improvements in rate-complexity performance are possible by increasing line widths, though rates within 3 to 4 percent of the entropy rate can be achieved with only 20 to 50 operations per pixel through combinations of line thickness and spacing.
Quadtree-guided wavelet image coding

A hybrid of wavelet and quadtree image coding is introduced. It uses a quadtree predictive coding technique, having good rate/fidelity performance and very low complexity, to encode the baseband wavelet coefficients. The resulting quadtree decomposition of the baseband is then used to guide the encoding of higher frequency subbands, by indicating which of their coefficients require encoding and which do not. The result is a coding method with performance comparable to those of the best known wavelet coders, but with less complexity.
Performance-energy tradeoffs in cutset wireless sensor networks

This work explores performance vs. communication energy tradeoffs in wireless sensor networks that use the recently proposed cutset deployment strategy in which sensors are placed densely along a grid of intersecting lines. For a given number of sensors, intersensor spacing is less for a cutset network than for a conventional lattice deployment, so that cutset networks require less communication energy, albeit with some potential loss in network performance. Previous work analyzed the energy-performance tradeoffs for square-grid cutset networks in the context of specific decentralized algorithms for source localization based on received signal strength (RSS). The current work also considers the RSS based source localization problem. However, it takes a more fundamental approach to analyzing the tradeoff by considering a centralized task, minimum energy communication paths, Maximum Likelihood estimation algorithms and Cramér-Rao bounds. Moreover, it analyzes triangular and honeycomb cutset deployments, in addition to square-grid ones. The results indicate that cutset networks offer sizable decreases in energy with only modest losses of performance.
Structural Texture Similarity Metrics for Image Analysis and Retrieval

We develop new metrics for texture similarity that accounts for human visual perception and the stochastic nature of textures. The metrics rely entirely on local image statistics and allow substantial point-by-point deviations between textures that according to human judgment are essentially identical. The proposed metrics extend the ideas of structural similarity and are guided by research in texture analysis-synthesis. They are implemented using a steerable filter decomposition and incorporate a concise set of subband statistics, computed globally or in sliding windows. We conduct systematic tests to investigate metric performance in the context of “known-item search,” the retrieval of textures that are “identical” to the query texture. This eliminates the need for cumbersome subjective tests, thus enabling comparisons with human performance on a large database. Our experimental results indicate that the proposed metrics outperform peak signal-to-noise ratio (PSNR), structural similarity metric (SSIM) and its variations, as well as state-of-the-art texture classification metrics, using standard statistical measures.
Improved side matching for matched-texture coding

Matched-texture coding (MTC) exploits the redundancy of textured regions in natural images in order to achieve low-encoding-rate structurally lossless compression. A key element of MTC identifying large image blocks that can be replaced with previously encoded blocks that have similar structure. The side matching (SM) approach attempts to do this by matching the upper and left boundary (side) of a target block with the corresponding boundary of the candidate block, and then, among the best side matches, selecting the one that best matches the target block. We explore the effectiveness of, and interplay between, three SM criteria in order to increase the number and quality of matches and to reduce the computational complexity. The criteria are mean-squared-error, log variance ratio, and partial implementations of STSIM-2, a recently proposed structural texture similarity metric. We propose a hierarchical algorithm for side matching, with three layers that utilize the three metrics, that improves performance and reduces the computation complexity. To set thresholds for the first and second layers of the hierarchical algorithm, we rely on Bayesian hypothesis testing. To estimate the necessary local probability densities, we introduce an adaptive estimation technique that depends on the side matching search region. Experimental results demonstrate an improvement of quality for a given encoding rate over previous realizations of MTC.
Data Synchronization With Timing: The Variable-Rate Case

This paper extends the theory of data synchronization with timing for fixed-rate codes, previously developed by the authors, to the variable-rate case. Given a source code, a class of sync-timing codes called variable-rate cascaded (VRC) codes is considered that ldquowrap aroundrdquo the source code in such a way as to enable the decoder to not only resynchronize rapidly when the encoded bits are corrupted by insertion, deletion, or substitution errors, but also produce estimates of the time indices of the data symbols encoded by the source code. The estimates of the time indices are modulo-T reductions of the actual time indices, for some integer T called the timing span of the code. These sync-timing codes are analyzed on the basis of the maximum timing span achievable for a given coding rate R and permissible resynchronization delay D . It is shown that the timing span of VRC codes is upper-bounded by 2D(1-R) + o(D), and that this upper bound is achievable asymptotically in D. This exponential rate of growth of timing span with delay is the same as that found previously for certain fixed-rate sync-timing codes, e.g., (fixed-rate) cascaded codes.
Fine-coarse vector quantization

A fast method for searching an unstructured vector quantization (VQ) codebook is introduced and analyzed. The method, fine-coarse vector quantization (FCVQ), operates in two stages: a 'fine' structured VQ followed by a table lookup 'coarse' unstructured VQ. Its rate, distortion, arithmetic complexity, and storage are investigated using analytical and experimental means. Optimality condition and an optimizing algorithm are presented. The results of experiments with both uniform scalar quantization and tree-structured VQ (TSVQ) as the first stage are reported. Comparisons are made with other fast approaches to vector quantization, especially TSVQ. It is found that when rate, distortion, arithmetic complexity, and storage are all taken into account, FCVQ outperforms TSVQ in a number of cases. In comparison to full search quantization, FCVQ has much lower arithmetic complexity, at the expense of a slight increase in distortion and a substantial increase in storage. The increase in mean-squared error (over full search) decays as a negative power of the available storage.
Cell-conditioned multistage vector quantization

Two-stage vector quantization (2VQ) reduces the complexity of single-stage VQ at the cost of reduced performance. Cell-conditioned (CC) 2VQ is proposed to improve the performance of 2VQ, while retaining the advantage of reduced complexity. In CC2VQ, the error vector from the first stage is transformed prior to its quantization by second stage. The effect of transformation is to cause the size and orientation of different first-stage cells to be as similar as possible. It is shown theoretically that CC2VQ performs as well as single-stage VQ under asymptotic conditions. Some experimental results for the vector quantization of speech waveforms are presented to support these theoretical results.<<ETX>>
Delta codes for line drawings

A new class of chain codes for line drawings, named delta codes is introduced. Two specific types of fixed angle delta codes and ring delta codes are described. The area-of-error distortion and rate are calculated for two types of delta codes applied to straight and slowly curving lines, and it is shown that the delta outperforms other chain codes with the same set of link types. For rapidly curving lines, however, the delta codes introduce curvature overload distortion analogous to the slope overload distortion of traditional delta modulation for waveforms.
The role of Nitadori’s sequence in scalar quantization of general exponential sources

In 1965, Nitadori (1965) derived an infinite sequence such that for any N, the ith term of the sequence is the size of the ith half cell (counting from the right) of the N-level minimum mean-squared error quantizer for a unit variance exponential source. This was done by exploiting a key property that the source's probability density function and its tail function are equal, a fact resulting from its memoryless property. In this paper, an asymptotic version of this key fact is found to hold for a general exponential (GE) source parameterized by an exponential power p, and it is used to show that for such a source, the size of the ith half cell of an optimal N-level quantizer multiplied by the (p-l)st power of the ith threshold approaches the ith term of the Nitadori sequence as N grows to infinity. Thus, the Nitadori sequence asymptotically characterizes the cells of MMSE quantizers for GE sources, as well as exponential.
Lossy Compression of Bilevel Images Based on Markov Random Fields

A new method for lossy compression of bilevel images based on Markov random fields (MRFs) is proposed. It preserves key structural information about the image, and then reconstructs the smoothest image that is consistent with this information. The smoother the original image, the lower the required bit rate, and conversely, the lower the bit rate, the smoother the approximation provided by the decoded image. The main idea is that as long as the key structural information is preserved, then any smooth contours consistent with this information will provide an acceptable reconstructed image. The use of MRFs in the decoding stage is the key to efficient compression. Experimental results demonstrate that the new technique outperforms existing lossy compression techniques, and provides substantially lower rates than lossless techniques (JBIG) with little loss in perceived image quality.
Asymptotic MSE Distortion of Mismatched Uniform Scalar Quantization

Asymptotic formulas are derived for the mean-squared error (MSE) distortion of N-level uniform scalar quantizers designed to be MSE optimal for one density function, but applied to another, as N → ∞. These formulas, which are based on the Euler-Maclaurin formula, are then applied with generalized gamma, Bucklew-Gallagher, and Hui-Neuhoff density functions as the designed-for and applied-to densities. It is found that the mismatch between the designed-for and applied-to densities can disturb the delicate balance between granular and overload distortions in optimal quantization, with the result that, generally speaking, the granular or overload distortion dominates, respectively, depending on whether the applied-to density function has a lighter or heavier tail than the designed-for density. Specifically, in the case of generalized gamma densities, a variance mismatch makes overload distortion dominate for an applied-to source with a slightly larger variance, whereas a shape mismatch can tolerate a wider variance difference while retaining the dominance of the granular distortion. In addition, for the studied density functions, the Euler-Maclaurin approach is used to derive asymptotic formulas for the optimal quantizer step size in a simpler, more direct, way than previous approaches.
Subjective similarity evaluation for scenic bilevel images

In order to provide ground truth for subjectively comparing compression methods for scenic bilevel images, as well as for judging objective similarity metrics, this paper describes the subjective similarity rating of a collection of distorted scenic bilevel images. Unlike text, line drawings, and silhouettes, scenic bilevel images contain natural scenes, e.g., landscapes and portraits. Seven scenic images were each distorted in forty-four ways, including random bit flipping, dilation, erosion and lossy compression. To produce subjective similarity ratings, the distorted images were each viewed by 77 subjects. These are then used to compare the performance of four compression algorithms and to assess how well percentage error and SmSIM work as bilevel image similarity metrics. These subjective ratings can also provide ground truth for future tests of objective bilevel image similarity metrics.
Wove paper analysis through texture similarities

Wove paper, made on a papermaking screen or mold having a surface of smooth tightly woven wires, was the predominant paper type used for printing in the twentieth century. To aid in the study and classification of fine art prints on wove paper, the present work compares the results of five different image processing approaches for characterizing surface texture. Using a collection of popular wove papers, a reference dataset of raking light close-up images was assembled. Five research teams undertook their own processing strategies to detect affinities among the paper samples. Their success in identifying similarity groupings are reported.
Entropy bounds for a Markov random subfield

Given a Markov random field (MRF) X defined by potentials on a graph G = (V,E), and given a subset U ⊂ V of the sites on which X is defined, we prove, under a positive correlation constraint on the MRF, that the entropy of the subfield XU is upper bounded by the entropy of an MRF defined on the subgraph induced by U with potentials taken directly from those assigned to U in G. To prove this we use exponential family representations of MRFs. We first show that the entropy of an MRF is monotone decreasing in the exponential parameters. We then use the Maximum Entropy principle and a well-known result from information geometry to show that the marginal entropy of XU is upper bounded by the MRF on the induced subgraph with moments matching the marginal distribution. We then use the convexity of the log-partition function to show that to match the marginal moments on the induced subgraph, the exponential coordinates on the induced subgraph are component-wise greater than the corresponding parameter of the original exponential characterization. Our result follows from monotonicity.
Structural similarity metrics for texture analysis and retrieval

The development of objective texture similarity metrics for image analysis applications differs from that of traditional image quality metrics because substantial point-by-point deviations are possible for textures that according to human judgment are essentially identical. Thus, structural similarity metrics (SSIM) attempt to incorporate “structural” information in image comparisons. The recently proposed structural texture similarity metric (STSIM) relies entirely on local image statistics. We extend this idea further by including a broader set of local image statistics, basing the selection on metric performance as compared to subjective evaluations. We utilize both intra- and inter-subband correlations, and also incorporate information about the color composition of the textures into the similarity metrics. The performance of the proposed metrics is compared to PSNR, SSIM, and STSIM on the basis of subjective evaluations using a carefully selected set of 50 texture pairs.
Entropy of quantized data at high sampling rates

This paper considers the entropy of the highly correlated quantized samples resulting from sampling at high rate. Two results are shown. The first concerns sampling and identically scalar quantizing a stationary random process over a finite interval. It is shown that if the process crosses a quantization threshold with positive probability, then the joint entropy of the quantized samples tends to infinity as the sampling interval goes to zero. The second result provides an upper bound to the rate at which the joint entropy tends to infinity, in the case of infinite-level uniform threshold scalar quantizers and a stationary Gaussian random process whose mean lies at a midpoint of some quantization cell. Specifically, an asymptotic formula for the conditional entropy of one quantized sample conditioned on another quantized sample is derived
Channels with almost finite memory

A stationary channel can have two kinds of memory--input memory and output memory. Recently, Gray and Ornstein introduced a condition on the decay of input memory called \bar{d} -continuity and established coding theorems for d-continuous channels. In this paper a condition for the decay of output memory is introduced which is called conditional almost block independence (CABI). It is believed that these two properties describe the character of many channels of interest. The principal result states that the \bar{d} -continuous CABI channels are precisely those which can be approximated in a very strong sense by a simple kind of finite memory channel, called a primitive channel, in which the source of randomness is memoryless and completely separated from the memory.
Entropy of Highly Correlated Quantized Data

This paper considers the entropy of highly correlated quantized samples. Two results are shown. The first concerns sampling and identically scalar quantizing a stationary continuous-time random process over a finite interval. It is shown that if the process crosses a quantization threshold with positive probability, then the joint entropy of the quantized samples tends to infinity as the sampling rate goes to infinity. The second result provides an upper bound to the rate at which the joint entropy tends to infinity, in the case of an infinite-level uniform threshold scalar quantizer and a stationary Gaussian random process. Specifically, an asymptotic formula for the conditional entropy of one quantized sample conditioned on the previous quantized sample is derived. At high sampling rates, these results indicate a sharp contrast between the large encoding rate (in bits/sec) required by a lossy source code consisting of a fixed scalar quantizer and an ideal, sampling-rate-adapted lossless code, and the bounded encoding rate required by an ideal lossy source code operating at the same distortion.
Matched-texture coding for structurally lossless compression

We propose a new texture-based compression approach that relies on new texture similarity metrics and is able to exploit texture redundancies for significant compression gains without loss of visual quality, even though there may visible differences with the original image (structurally lossless). Existing techniques rely on point-by-point metrics that cannot account for the stochastic and repetitive nature of textures. The main idea is to encode selected blocks of textures - as well as smooth blocks and blocks containing boundaries between smooth and/or textured regions - by pointing to previously occurring (already encoded) blocks of similar textures, blocks that are not encoded in this way, are encoded by a baseline method, such as JPEG. Experimental results with natural images demonstrate the advantages of the proposed approach.
Perceptual coding of images for halftone display

We present a new technique for coding gray-scale images for facsimile transmission and printing on a laser printer. We use a gray-scale image encoder so that it is only at the receiver that the image is converted to a binary pattern and printed. The conventional approach is to transmit the image in halftoned form, using entropy coding (e.g. CCITT Group 3 or JBIG). The main advantages of the new approach are that we can get higher compression rates and that the receiver can tune the halftoning process to the particular printer. We use a perceptually based subband coding approach. It uses a perceptual masking model that was empirically derived for printed images using a specific printer and halftoning technique. In particular, we used a 300 dots/inch write-black laser printer and a standard halftoning scheme ("classical") for that resolution. For nearly transparent coding of gray-scale images, the proposed technique requires lower rates than the standard facsimile techniques.
Strong universal source coding subject to a rate-distortion constraint

The following problem in universal source coding is explored. Some members of a class of sources have various constraints on the maximum rate at which they may be encoded, and thc remainder have various constraints on the maximum distortion that may result from encoding. It is desired to find a universal code that will adapt its performance so that whatever source in the class is encoded, the resulting performance meets the constraint and is optimal in the rate-distortion function sense for that particular source. It is shown that such codes exist when the class is totally bounded and the constraints are uniformly continuous in an appropriate sense. A key result shows that the value of any uniformly continuous function on a totally bounded class can be uniformly well estimated from observations of the output of any source in
Optimizing motion-vector accuracy in block-based video coding

All motion-vectors are encoded with the same tired accuracy, typically 1/2-pixel accuracy, but the best motion-vector accuracies are not known. We present a theoretical framework to find the motion-vector accuracies that minimize the total encoding rate with this type of coder, for the classical case where all motion-vectors are encoded with the same accuracy, and for new cases where the accuracy is adapted on a frame-by-frame or block-by-block basis. To do this, we analytically model the effect of motion-vector accuracy and show that the energy in a block of the difference frame is approximately quadratic in the accuracy of the block's motion-vector. This energy-accuracy model is then used to obtain expressions for the total bit rate (motion rate plus difference frame rate) in terms of the blocks' motion accuracies and other key parameters. Minimizing these expressions leads to simple formulas that indicate how to choose the best motion-vector accuracies for this type of coder. These formulas also show that the motion accuracy must increase where more texture is present and decrease when there is much scene noise or when the level of compression is high. We implement several entropy and MPEG-like video coders based on our analysis and present experimental results on synthetic and real video sequences. These results suggest that our formulas are accurate and that significant bit rate savings can be achieved when our optimization procedures are used.