nils-herrmann/clean_scirepeval huggingface dataset:
Hidden Markov models for tracking neuronal structure contours in electron micrograph stacks

This paper is focused on the problem of tracking cell contours across an electron micrograph stack, so as to discern the 3D neuronal structures, with particular application to analysis of retinal images. While the problem bears similarity to traditional object tracking in video sequences, it poses additional significant challenges due to the coarse z-axis resolution which causes large contour deformations across frames, and involves major topological changes including contour splits and merges. The method proposed herein applies a deformable trellis, on which a hidden Markov model is defined, to track contour deformation. The first phase produces an estimated new contour and computes its probability given the model. The second phase detects low-confidence contour segments and tests the hypothesis that a topological change has occurred, by introducing corresponding hypothetical arcs and re-optimizing the contour. The most probable solution, including the topological hypothesis, is identified. Experimental results show, both quantitatively and qualitatively, that the proposed approach can effectively and efficiently track cell contours while accounting for splitting, merging, large contour displacements and deformations.
On zero-error coding of correlated sources

The problem of separate zero-error coding of correlated sources is considered. Inner and outer single-letter bounds are established for the achievable rate region, and conditions for their coincidence are investigated. It is shown that successive encoding combined with time sharing is not always an optimal coding strategy. Conditions for its optimality are derived. The inner bound to the achievable rate region follows as a special case of the single-letter characterization of a generalized zero-error multiterminal rate-distortion problem. The applications of this characterization to a problem of remote computing are also explored. Other results include (i) a product-space characterization of the achievable rates, (ii) bounds for finite block length, and (iii) asymptotic fixed-length rates.
Transmission energy allocation with low peak-to-average ratio

Transmission energy allocation (TEA) to bits according to their sensitivity is known to significantly enhance robustness to channel errors. These advantages are gained at the cost of high peak-to-average ratio (PAR) of the signal energy employed to transmit different bits. We show that, in the case of 4-QAM, appropriate grouping of bits allows achieving all the gains of optimal TEA while maintaining PAR at a small fraction of a decibel. Alternatively, we show how to achieve close to optimal TEA under the constraint of perfect (0 dB) PAR, thus extending the application of TEA to constant envelope modulation schemes. Performance is illustrated with an example of Gauss-Markov sources compressed by vector quantization.
On optimum communication cost for joint compression and dispersive information routing

In this paper, we consider the problem of minimum cost joint compression and routing for networks with multiple-sinks and correlated sources. We introduce a routing paradigm, called dispersive information routing, wherein the intermediate nodes are allowed to forward a subset of the received bits on subsequent paths. This paradigm opens up a rich class of research problems which focus on the interplay between encoding and routing in a network. What makes it particularly interesting is the challenge in encoding sources such that, exactly the required information is routed to each sink, to reconstruct the sources they are interested in. We demonstrate using simple examples that our approach offers better asymptotic performance than conventional routing techniques. We also introduce a variant of the well known random binning technique, called ‘power binning’, to encode and decode sources that are dispersively transmitted, and which asymptotically achieves the minimum communication cost within this routing paradigm.
Rate-distortion optimized slicing, packetization and coding for error resilient video transmission

This paper presents an algorithm to optimize the tradeoff between rate and expected end-to-end distortion of a video sequence transmitted over a packet network. The approach optimizes the source coding parameters, slicing, network QoS class selection and/or error control coding parameters, and accounts for the effects of compression, packetization, error propagation, and concealment at the decoder. It builds on, and substantially extends the applicability of, the recursive optimal per-pixel estimate (ROPE) technique for end-to-end distortion estimation. A trellis-based algorithm is introduced in order to overcome macroblock interdependencies in the estimation procedure, and allow adaptive slicing. Moreover, we propose a complementary packetization scheme to efficiently arrange the slices into packets for FEC protection while minimizing rate loss due to padding. Simulations demonstrate consistent gains over currently used techniques.
Entropy-constrained tree-structured vector quantizer design

Current methods for the design of pruned or unbalanced tree-structured vector quantizers such as the generalized Breiman-Friedman-Olshen-Stone (GBFOS) algorithm proposed in 1980 are effective, but suffer from several shortcomings. We identify and clarify issues of suboptimality including greedy growing, the suboptimal encoding rule, and the need for time sharing between quantizers to achieve arbitrary rates. We then present the leaf-optimal tree design (LOTD) method which, with a modest increase in design complexity, alters and reoptimizes tree structures obtained from conventional procedures. There are two main advantages over existing methods. First, the optimal entropy-constrained nearest-neighbor rule is used for encoding at the leaves; second, explicit quantizer solutions are obtained at all rates without recourse to time sharing. We show that performance improvement is theoretically guaranteed. Simulation results for image coding demonstrate that close to 1 dB reduction of distortion for a given rate can be achieved by this technique relative to the GBFOS method.
Robust video coding for packet networks with feedback

Robustness to packet loss is a critical requirement for video communication over packet switched networks. Intra-coding is an important tool to mitigate the effects of packet loss by limiting error propagation. This work proposes an algorithm for intra/inter coding mode selection, while utilizing the feedback channel that carries acknowledgement information about received packets. The overall distortion in frame reconstruction at the decoder due to quantization, error concealment (after packet loss) and error propagation is estimated at pixel-level precision, and is dynamically refined based on the feedback information. The estimate is then integrated into a rate-distortion (RD) framework for optimal selection of coding mode for each macroblock. Simulation results demonstrate that precise distortion estimation enables the coder to achieve substantial and consistent gain in PSNR over known state-of-the-art feedback-based mode selection modes.
Efficient bit-rate scalability for weighted squared error optimization in audio coding

We propose two quantization techniques for improving the bit-rate scalability of compression systems that optimize a weighted squared error (WSE) distortion metric. We show that quantization of the base-layer reconstruction error using entropy-coded scalar quantizers is suboptimal for the WSE metric. By considering the compandor representation of the quantizer, we demonstrate that asymptotic (high resolution) optimal scalability in the operational rate-distortion sense is achievable by quantizing the reconstruction error in the compandor's companded domain. We then fundamentally extend this work to the low-rate case by the use of enhancement-layer quantization which is conditional on the base-layer information. In the practically important case that the source is well modeled as a Laplacian process, we show that such conditional coding is implementable by only two distinct switchable quantizers. Conditional coding leads to substantial improvement over the companded scalable quantization scheme introduced in the first part, which itself significantly outperforms standard techniques. Simulation results are presented for synthetic memoryless Laplacian sources with mu-law companding, and for real-world audio signals in conjunction with MPEG AAC. Using the objective noise-mask ratio (NMR) metric, the proposed approaches were found to result in bit-rate savings of a factor of 2 to 3 when implemented within the scalable MPEG AAC. Moreover, the four-layer scalable coder consisting of 16-kb/s layers achieves performance close to that of the 64-kb/s nonscalable coder on the standard test database of 44.1-kHz audio
An Estimation-Theoretic Framework for Spatially Scalable Video Coding

This paper focuses on prediction optimality in spatially scalable video coding. It draws inspiration from an estimation-theoretic prediction framework for quality (SNR) scalability earlier developed by our group, which achieved optimality by fully accounting for relevant information from the current base layer (e.g., quantization intervals) and the enhancement layer, to efficiently calculate the conditional expectation that forms the optimal predictor. It was central to that approach that all layers reconstruct approximations to the same original transform coefficient. In spatial scalability, however, the layers encode different resolution versions of the signal. To approach optimality in enhancement layer prediction, this paper departs from existing spatially scalable codecs that employ pixel domain resampling to perform interlayer prediction. Instead, it incorporates a transform domain resampling technique that ensures that the base layer quantization intervals are accessible and usable at the enhancement layer despite their differing signal resolutions, which in conjunction with prior enhancement layer information, enable optimal prediction. A delayed prediction approach that complements this framework for spatial scalable video coding is then provided to further exploit future base layer frames for additional enhancement layer coding performance gains. Finally, a low-complexity variant of the proposed estimation-theoretic prediction approach is also devised, which approximates the conditional expectation by switching between three predictors depending on a simple condition involving information from both layers, and which retains significant performance gains. Simulations provide experimental evidence that the proposed approaches substantially outperform the standard scalable video codec and other leading competitors.
Design of on-chip error correction systems for multilevel NOR and NAND flash memories

The design of on-chip error correction systems for multilevel code-storage NOR flash and data-storage NAND flash memories is concerned. The concept of trellis coded modulation (TCM) has been used to design on-chip error correction system for NOR flash. This is motivated by the non-trivial modulation process in multilevel memory storage and the effectiveness of TCM in integrating coding with modulation to provide better performance at relatively short block length. The effectiveness of TCM-based systems, in terms of error-correcting performance, coding redundancy, silicon cost and operational latency, has been successfully demonstrated. Meanwhile, the potential of using strong Bose-Chaudhiri-Hocquenghem (BCH) codes to improve multilevel data-storage NAND flash memory capacity is investigated. Current multilevel flash memories store 2 bits in each cell. Further storage capacity may be achieved by increasing the number of storage levels per cell, which nevertheless will correspondingly degrade the raw storage reliability. It is demonstrated that strong BCH codes can effectively enable the use of a larger number of storage levels per cell and hence improve the effective NAND flash memory storage capacity up to 59.1% without degradation of cell programming time. Furthermore, a scheme to leverage strong BCH codes to improve memory defect tolerance at the cost of increased NAND flash cell programming time is proposed.
Unequally protected multi-stage vector quantization for time-varying channels

We present a source-channel coding system consisting of a multi-stage vector quantizer in conjunction with unequal protection against channel errors. The receiver estimates the channel conditions and decodes as many stages of the quantized signal as can be reliably decoded. The proposed overall system is optimized for robustness to time-varying noisy channels. Two versions of the system are designed and evaluated, which involve: (i) hard, and (ii) weighted decoding of the stage indices. Simulation results are given for Gauss-Markov sources transmitted over broadcast and fading channels. Consistent and substantial improvement is achieved over the standard multi-stage vector quantizer with equal error protection, and the gains are in the range of 3 to 5 dB.
BEOL yield predictions for SIA roadmap

Interconnect-related effects are a major constraint on the ability to achieve the goals for integration and performance projected by the SIA National Technology Roadmap for Semiconductors. As CMOS technology moves from 0.35 /spl mu/m to 0.1 /spl mu/m minimum feature sizes, minimum interconnect pitches are projected to shrink from 1.0 /spl mu/m to 0.27 /spl mu/m. Several factors may make BEOL yield a dominant concern in submicron manufacturing. Since both microprocessor (logic) transistor densities and chip areas are projected to grow from 4 M/cm/sup 2/ and 250 mm/sup 2/ to 50 M/cm/sup 2/ and 520 mm/sup 2/, the difficulty of interconnecting devices becomes much greater. Solving this wireability problem requires significant increases in the number of wiring levels on a chip. Shrinking interconnect pitches, an increased number of wiring levels, and increased chip area all combine to reduce BEOL yield. To achieve 90% product yield the SIA roadmap proposes substantial reductions in defect density. This paper predicts defect density requirements and wiring strategies which combine high performance with high yield as minimum feature sizes shrink from 0.35 /spl mu/m to 0.1 /spl mu/m. Predictions are made using a CAD tool, RIPE, the Rensselaer Interconnect Performance Estimator, which predicts the effects of interconnect technology, system design, and wiring strategy on wireability, performance, power, and BEOL yield. We predict about 99% BEOL yield using the SIA proposals for defect density. Significantly higher defect densities should give acceptable yields.
Source coding in the presence of exploration-exploitation tradeoff

Exploration versus exploitation in a sensor field with a mobile agent is examined in the context of source coding. The encoder is the low complexity data gathering agent. The decoder is a high complexity fusion center. The encoder first sends a coarse description of the random field, then transmits a refined description of a region of interest, i.e., a subset of the correlated sources in the first stage and so on. The main source coding challenge is that the receiver wants to refine a subset of the correlated sources that is unknown to the encoder a priori. The conventional approach of scalable coding via conditional codebook encoding (CCE) requires a codebook that is exponential in size with respect to the number of sources and also the number of refinement stages. This paper studies an alternative approach, using random binning (RB), in lieu of CCE. The universality of RB plays a key role, as the encoder does not know a priori which sources the decoder wants to refine. It is shown that RB does not introduce any loss and can effectively replace CCE while providing significant storage reduction in terms of the number of codewords stored. Achievable rate regions are derived for the single and the multi-terminal encoding settings.
Optimal estimation for error concealment in scalable video coding

This work is concerned with scalable video coding and, specifically, with error concealment for the mitigation of damage caused by packet loss at the enhancement layer. We propose a frequency-domain error concealment scheme that makes full use of the information available at the decoder from both the current base layer, and prior enhancement layer frames. It employs a statistical model for the evolution of transform coefficients from frame to frame and implements the optimal estimate of the reconstructed coefficient. Moreover, the ideas underlying the proposed approach allow detection and correction of blocks that suffer from significant error propagation from past losses. Substantial PSNR gains are observed in the experiments.
Estimation-Theoretic Approach to Delayed Decoding of Predictively Encoded Video Sequences

Current video coders employ predictive coding with motion compensation to exploit temporal redundancies in the signal. In particular, blocks along a motion trajectory are modeled as an auto-regressive (AR) process, and it is generally assumed that the prediction errors are temporally independent and approximate the innovations of this process. Thus, zero-delay encoding and decoding is considered efficient. This paper is premised on the largely ignored fact that these prediction errors are, in fact, temporally dependent due to quantization effects in the prediction loop. It presents an estimation-theoretic delayed decoding scheme, which exploits information from future frames to improve the reconstruction quality of the current frame. In contrast to the standard decoder that reproduces every block instantaneously once the corresponding quantization indices of residues are available, the proposed delayed decoder efficiently combines all accessible (including any future) information in an appropriately derived probability density function, to obtain the optimal delayed reconstruction per transform coefficient. Experiments demonstrate significant gains over the standard decoder. Requisite information about the source AR model is estimated in a spatio-temporally adaptive manner from a bit-stream conforming to the H.264/AVC standard, i.e., no side information needs to be sent to the decoder in order to employ the proposed approach, thereby compatibility with the standard syntax and existing encoders is retained.
Fast adaptive Mahalanobis distance-based search and retrieval in image databases

Motivated by the need to efficiently leverage user relevance feedback in content-based retrieval from image databases, we propose a fast, clustering-based indexing technique for exact nearest-neighbor search that adapts to the Mahalanobis distance with a varying weight matrix. We derive a basic property of point-to-hyperplane Mahalanobis distance, which enables efficient recalculation of such distances as the Mahalanobis weight matrix is varied. This property is exploited to recalculate bounds on query-cluster distances via projection on known separating hyperplanes (available from the underlying clustering procedure), to effectively eliminate noncompetitive clusters from the search and to retrieve clusters in increasing order of (the appropriate) distance from the query. We compare performance with an existing variant of VA-File indexing designed for relevance feedback, and observe considerable gains.
Joint source-channel vector quantization using deterministic annealing

A new approach to the combined source-channel vector quantizer design problem is presented. The method utilizes deterministic annealing to avoid local minima that trap conventional descent algorithms. The temperature is used to control the fuzziness of the encoder and decoder association probabilities. In the low temperature limit, the method reduces to a descent method that is an analog of the generalized Lloyd algorithm (GLA) for noisy channels. Thus, in this sense, it is a generalization of noisy channel GLA (NC-GLA). Simulations were performed in order to compare the approach with several versions of NC-GLA. The method outperformed the other methods under all test conditions. Moreover, the gains over other methods grow with the codebook size.<<ETX>>
Fusion Coding of Correlated Sources for Storage and Selective Retrieval

We focus on a new, potentially important application of source coding directed toward storage and retrieval, termed fusion coding of correlated sources. The task at hand is to efficiently store multiple correlated sources in a database so that, at any point of time in the future, data from a selective subset of sources specified by user can be efficiently retrieved. Only statistical information about future queries is available in advance. A typical application scenario would be in storage of correlated data generated by dense sensor networks, where information from specific regions is requested in the future. We propose a fusion coder (FC) for lossy storage and retrieval, wherein different queries are handled by allowing for selective (compressed) bit retrieval. We derive the properties of an optimal FC and present an iterative algorithm for its design. Since iterative design is initialization-dependent, we present initialization heuristics that help avoid poor local optima. An analysis of design complexity reveals complexity growth with query-set size. We first tackle this problem by exploiting optimality properties of FCs. We also consider quantization of the query-space with decision trees in order to adapt to new queries, unseen during FC design. Experiments conducted on real and synthetic data-sets demonstrate that the proposed FC is able to achieve significantly better tradeoffs than joint compression by vector quantization (VQ), with retrieval speedups reaching 3 × and distortion gains of up to 3.5 dB possible.
Additive successive refinement

Rate-distortion bounds for scalable coding, and conditions under which they coincide with nonscalable bounds, have been extensively studied. These bounds have been derived for the general tree-structured refinement scheme, where reproduction at each layer is an arbitrarily complex function of all encoding indexes up to that layer. However, in most practical applications (e.g., speech coding) "additive" refinement structures such as the multistage vector quantizer are preferred due to memory limitations. We derive an achievable region for the additive successive refinement problem, and show via a converse result that the rate-distortion bound of additive refinement is above that of tree-structured refinement. Necessary and sufficient conditions for the two bounds to coincide are derived. These results easily extend to abstract alphabet sources under the condition E{d(X,a)}</spl infin/ for some letter a. For the special cases of square-error and absolute-error distortion measures, and subcritical distortion (where the Shannon lower bound (SLB) is tight), we show that successive refinement without rate loss is possible not only in the tree-structured sense, but also in the additive-coding sense. We also provide examples which are successively refinable without rate loss for all distortion values, but the optimal refinement is not additive.