nils-herrmann/clean_scirepeval huggingface dataset:
Robust Distributed Source Coder Design by Deterministic Annealing

This paper considers the design of efficient quantizers for a robust distributed source coding system. The information is encoded at independent terminals and transmitted across separate channels, any of which may fail. The scenario subsumes a wide range of source and source-channel coding/quantization problems, including multiple descriptions and distributed source coding. Greedy descent methods depend heavily on initialization, and the presence of abundant (high density of) ¿poor¿ local optima on the cost surface strongly motivates the use of a global design algorithm. We propose a deterministic annealing approach for the design of all components of a generic robust distributed source coding system. Our approach avoids many poor local optima, is independent of initialization, and does not make any simplifying assumption on the underlying source distribution. Simulation results demonstrate a wide spread in the performance of greedy Lloyd-based algorithms, and considerable gains are achieved by using the proposed deterministic annealing approach.
Robust predictive vector quantizer design

The design of predictive quantizers generally suffers from difficulties due to the prediction loop, which have an impact on the convergence and the stability of the design procedure. We previously proposed an asymptotically closed-loop approach to quantizer design for predictive coding applications, which benefits from the stability of open-loop design while asymptotically optimizing the actual closed-loop system. In this paper, we present an enhancement to the approach where joint optimization of both predictor and quantizer is performed within the asymptotically closed-loop framework. The proposed design method is tested on synthetic sources (first-order Gauss and Laplacian-Markov sequences), and on natural sources, in particular, line spectral frequency parameters of speech signals.
Towards optimal scalability in predictive video coding

We propose a method for efficient SNR scalability in predictive video coding which overcomes the known fundamental difficulties due to the prediction loop. The method is generally applicable to any scalable predictive coder but a special emphasis is given in this paper to standard DCT-based video compression. The compression efficiency of the enhancement layer is substantially improved by casting the design of its prediction module within an estimation-theoretic framework. The resulting prediction of a transform coefficient in the enhancement layer is optimal given both the corresponding reconstructed coefficient at the base layer, and the enhancement layer reconstructed coefficient in the previous frame. Simulation shows consistent, substantial improvement in PSNR of the reconstructed enhancement layer frames. For two-layer systems, such optimal prediction can be closely approximated by a simple switched prediction scheme which is of negligible complexity. The proposed method may be easily combined with known temporal scalability methods to provide further improvement in compression performance over a wide range of bit rates.
Segmentation of cells in electron microscopy images through multimodal label transfer

Automated segmentation of electron microcope (EM) images is a challenging problem, but the presence of related images of a different modality can be a valuable resource. This paper describes a method to effectively utilize complementary information, if available, in EM segmentation. Images of both modalities are oversegmented into superpixels. A 2D hidden Markov model (HMM) is set up on the superpixel graph to determine the optimal superpixel mapping between images. This mapping is used to transfer labels and generate preliminary segmentations in the EM domain, whose boundaries are then refined, to eliminate imprecisions due to the su-perpixel grid, using a 1D HMM based contour refinement method. The performance of the proposed approach is demonstrated on a challenging dataset, and significant improvement is observed over related techniques.
On common information and the encoding of sources that are not successively refinable

This paper focuses on a new framework for scalable coding of information based on principles derived from common information of two dependent random variables. In the conventional successive refinement setting, the encoder generates two layers of information called the base layer and the enhancement layer. The first decoder, which receives only the base layer, produces a coarse reconstruction of the source, whereas the second decoder, which receives both the layers, uses the enhancement layer to refine the information further leading to a finer reconstruction. It is popularly known that asymptotic rate-distortion optimality at both the decoders is possible if and only if the source-distortion pair is successively refinable. However when the source is not successively refinable under the given distortion metric, it is impossible to achieve rate-distortion optimality at both the layers simultaneously. For this reason, most practical system designers resort to storing two individual representations of the source leading to significant overhead in transmission/storage costs. Inspired by the breadth of applications, in this paper, we propose a new framework for scalable coding wherein a subset of the bits sent to the first decoder is not sent to the second decoder. That is, the encoder generates one common bit stream which is routed to both the decoders, but unlike the conventional successive refinement setting, both the decoders receive an additional individual bitstream. By relating the proposed framework with the problem of common information of two dependent random variables, we derive a single letter characterization for the minimum sum rate achievable for the proposed setting when the two decoders are constrained to receive information at their respective rate-distortion functions. We show using a simple example that the proposed framework provides a strictly better asymptotic sum rate as opposed to the conventional scalable coding setup when the source-distortion pair is not successively refinable.
A Global Approach to Joint Quantizer Design for Distributed Coding of Correlated Sources

The focus of this work is on the design of efficient quantizers for correlated sources subject to complexity limitations on the encoding terminals. Existing iterative descent methods rely heavily on initialization, and the prevalence of numerous 'poor' local optima strongly motivates the use of a global design algorithm. We propose a multiple-prototype based deterministic annealing approach for the joint design of all components of a generic distributed source coding system. Our approach avoids many poor local optima, is independent of initialization and does not assume any prior information on the underlying source distribution. Simulation results show substantial gains over a Lloyd-like iterative algorithm
An optimal transmit-receive rate tradeoff in Gray-Wyner network and its relation to common information

This paper considers the problem of characterizing the optimal tradeoff between the total transmit versus receive rate in the Gray-Wyner network. This tradeoff plays a crucial role in many important practical applications including establishing fundamental limits in databases for correlated sources and in minimum cost routing for networks. We develop the insight into this tradeoff by defining two quantities C(X, Y; R ) and K{X,Y;R ), which quantify the shared rate as a function of the total transmit and receive rates respectively. Closely tied up with this tradeoff is the notion of common information of two dependent random variables. The two most influential definitions are due to Wyner  and Gács-Körner . Though it is well know that these definitions can be characterized as two extreme points in the Gray-Wyner region, no contour with operational significance is known which connects them. We will show that the tradeoff between transmit and receive rates leads to a contour of points on the boundary of Gray-Wyner region which passes through the operating points of Wyner and Gács-Körner. We use these properties to derive alternate characterizations for the two definitions of common information under a broader unified framework.
On random binning versus conditional codebook methods in multiple descriptions coding

There are two common types of encoding paradigms in multiple descriptions (MD) coding: i) an approach based on conditional codebook generation, which was originally initiated by El-Gamal and Cover for the 2 channel setting and later extended to more than 2 channels by Venkataramani, Kramer and Goyal (VKG), ii) and an approach based on Slepian and Wolf's random binning technique, proposed by Pradhan, Puri and Ramchandran (PPR) for L >; 2 descriptions. It is well known that the achievable region due to PPR subsumes the VKG region for the symmetric Gaussian MD problem. Motivated by several practical advantages of random binning based methods over the conditional codebook encoding, this paper focuses on the following important questions: Does a random binning based scheme achieve the performance of conditional codebook encoding, even for the 2 descriptions scenario? Are random binning based approaches beneficial for settings that are not fully symmetric? This paper answers both these questions in the affirmative. Specifically, we propose a 2 descriptions coding scheme, based on random binning, which subsumes the currently known largest region for this problem due to Zhang and Berger. Moreover, we propose its extensions to L >; 2 channels and derive the associated achievable regions. The proposed scheme enjoys the advantages of both encoding paradigms making it particularly useful when there is symmetry only within a subset of the descriptions.
Design assurance in a university setting

It is argued that VLSI design assurance in a university setting requires developing an environment in which a multiplicity of reliable designs can be produced by inexperienced designers at minimal costs. Two issues in design verification and testing are examined: the first relates to design for testability; the second is the practical verification of designs once they are implemented in silicon. It is concluded that as problems associated with design assurance are solved, the results should be of value to a broader community and especially to ASIC (application-specific integrated circuit) designers.<<ETX>>
Adaptive Cluster Distance Bounding for High-Dimensional Indexing

We consider approaches for similarity search in correlated, high-dimensional data sets, which are derived within a clustering framework. We note that indexing by “vector approximation” (VA-File), which was proposed as a technique to combat the “Curse of Dimensionality,” employs scalar quantization, and hence necessarily ignores dependencies across dimensions, which represents a source of suboptimality. Clustering, on the other hand, exploits interdimensional correlations and is thus a more compact representation of the data set. However, existing methods to prune irrelevant clusters are based on bounding hyperspheres and/or bounding rectangles, whose lack of tightness compromises their efficiency in exact nearest neighbor search. We propose a new cluster-adaptive distance bound based on separating hyperplane boundaries of Voronoi clusters to complement our cluster based index. This bound enables efficient spatial filtering, with a relatively small preprocessing storage overhead and is applicable to euclidean and Mahalanobis similarity measures. Experiments in exact nearest-neighbor set retrieval, conducted on real data sets, show that our indexing method is scalable with data set size and data dimensionality and outperforms several recently proposed indexes. Relative to the VA-File, over a wide range of quantization resolutions, it is able to reduce random IO accesses, given (roughly) the same amount of sequential IO operations, by factors reaching 100X and more.
Combinatorial Message Sharing for a refined multiple descriptions achievable region

This paper presents a new achievable rate-distortion region for the L-channel multiple descriptions problem. Currently, the most popular region for this problem is due to Venkataramani, Kramer and Goyal . Their encoding scheme is an extension of the Zhang-Berger scheme to the L-channel case and includes a combinatorial number of refinement codebooks, one for each subset of the descriptions. All the descriptions also share a single common codeword, which introduces redundancy, but assists in better coordination of the descriptions. This paper proposes a novel encoding technique involving ‘Combinatorial Message Sharing’, where every subset of the descriptions may share a distinct common message. This introduces a combinatorial number of shared codebooks along with the refinement codebooks of . These shared codebooks provide a more flexible framework to trade off redundancy across the messages for resilience to descriptions loss. We derive an achievable rate-distortion region for the proposed technique, and show that it subsumes the achievable region of .
On efficient quantizer design for robust distributed source coding

This paper considers the design of efficient quantizers for a distributed source coding system. The information is encoded at independent terminals and transmitted across separate channels, any of which may fail. The scenario subsumes a wide range of vector quantization problems. Greedy descent methods rely heavily on initialization, and the presence of numerous 'poor' local optima on the distortion cost surface strongly motivates the use of a global design algorithm. We propose a deterministic annealing approach for the design of all components of a generic distributed source coding system. Our approach avoids many poor local optima, is independent of initialization, and does not assume any prior information on the underlying source distribution. Simulation results show significant gains over an iterative greedy algorithm.
Lossy common information of two dependent random variables

The two most prevalent notions of common information are due to Wyner and Gács-Körner and both the notions can be stated as two different characteristic points in the lossless Gray-Wyner region. Although these quantities can be easily evaluated for random variables with infinite entropy (eg. continuous random variables), the operational significance underlying their definition is applicable only to the lossless framework. The primary objective of this paper is to generalize these two notions of common information to the lossy Gray-Wyner network, which extends the theoretical intuition underlying their definitions for general sources and distortion measures. We begin with the lossy generalization of Wyner's common information, defined as the minimum rate on the shared branch of the Gray-Wyner network at minimum sum rate when the two decoders reconstruct the sources subject to individual distortion constraints. We derive a complete single letter information theoretic characterization for this quantity and use it to compute the common information of symmetric bivariate Gaussian random variables. We then derive similar results to generalize Gács-Körner's definition to the lossy framework. These two characterizations allow us to carry the practical insight underlying the two notions of common information to general sources and distortion measures.
Towards large scale distributed coding

This paper considers the problem of distributed source coding for a large sensor network. A typical shortcoming of current approaches to true distributed coding is the exponential growth of the decoder codebook size with the number of sources in the network. This growth in complexity renders many traditional approaches impractical for even moderately sized sensor networks. Inspired by our recent results on fusion coding for selective retrieval, we propose a new distributed coding approach that scales to a large number of sources. Central to our approach is a “bit-subset selector” module whose role is to judiciously extract an appropriate subset of the received bits for decoding per individual source. This, together with joint design of all system components, enables direct optimization of the decoder complexity-distortion tradeoff, and thereby the desired scalability. Experiments on both real and synthetic data-sets show considerable gains over heuristic schemes.
On hierarchical type covering

This correspondence focuses on a significant distinction between two hierarchical type covering strategies, namely, weak and strong covering, and on the impact of this distinction on known results. In particular, it is demonstrated that the rate region for weak covering, whose natural use is in scalable source coding, is generally larger than the rate region for strong covering, which is primarily useful in hierarchical guessing. This correspondence also presents a corrected converse result for the hierarchical guessing problem.
On linear transforms in zero-delay Gaussian source channel coding

This paper is concerned with the optimal linear transforms in zero-delay source channel coding for Gaussian sources and channels. In preparation for the main results, we first consider the classical problem in the point-to-point setting, which had already been solved by Lee and Petersen, where we provide an alternative proof using majorization. We then analyze the performance of linear source-channel coding in the low signal-to-noise ratio (SNR) regime. We show that at asymptotically low SNR, or equivalently as the power tends to zero, the powerdistortion function of zero delay linear coding achieves optimum theoretically achievable performance. Finally, we consider zero-delay source-channel coding with decoder side information. Here, subject to structural constraints on the encoder, we find the optimal encoder-decoder pair in closed form. We analyze two structures: i) the encoder is constrained to be linear where we show that the optimal transform is product of matrices including as factor the conditional Karhunen Loéve transform (KLT) of the source given the side information. ii) the encoder consists of linear transformation followed by individual optimal nonlinear mapping of each transform coefficient. Using majorization principles, we show that the optimal transform does not depend on the nonlinearities introduced, as long as they are scale invariant, hence the optimal transform is also a product involving the conditional KLT of the source.
Mismatch Impact on Per-Pixel End-to-End Distortion Estimation and Coding Mode Selection

The recursive optimal per-pixel estimate (ROPE) is an effective end-to-end distortion estimation scheme. Most existing ROPE-based applications assume that: (i) the encoder knows exactly the actual packet loss rate and (ii) the decoder error concealment scheme; (iii) no deblocking in-loop filtering is employed. However, in practice, these assumptions may not all be valid. In this paper, we investigate the impact of mismatch between assumed and actual conditions on the performance of ROPE and corresponding rate-distortion optimized coding mode selection. Useful conclusions are drawn from extensive experimental results.
Quality in test education?

The short answer to the first question is that test engineers had better be seen by their managements as part of the solution rather than part of the problem. The long answer is that the drive to raise quality and lower cost in less time has definite implications for test engineering practice. Kaoru Ishikawa at the University of Tokyo has been a leader in adapting quality improvement techniques to the work place. He has suggested that quality management has evolved from final inspection to manufacturing control to design improvement Final inspection, testing, to determine whether a product should be selected or rejected does not, by itself, improve quality. However, a high reject rate does indicate poor quality and higher cost.
Block-size adaptive transform domain estimation of end-to-end distortion for error-resilient video coding

The accuracy of end-to-end distortion (EED) estimation is crucial to achieving effective error resilient video coding. An established solution, the recursive optimal per-pixel estimate (ROPE), does so by tracking the first and second moments of decoder-reconstructed pixels. An alternative estimation approach, the spectral coefficient-wise optimal recursive estimate (SCORE), tracks instead moments of decoder-reconstructed transform coefficients, which enables accounting for transform domain operations. However, the SCORE formulation relies on a fixed transform block size, which is incompatible with recent standards. This paper proposes a non-trivial generalization of the SCORE framework which, in particular, accounts for arbitrary block size combinations involving the current and reference block partitions. This seemingly intractable objective is achieved by a two-step approach: i) Given the fixed block size moments of a reference frame, estimate moments of transform coefficients for the codec-selected current block partition; ii) Convert the current results to transform coefficient moments corresponding to a regular fixed block size grid, to facilitate EED estimation for the next frame. Experimental results first demonstrate the accuracy of the proposed estimate in conjunction with transform domain temporal prediction. Then the estimate is leveraged to optimize the coding mode and yields considerable gains in rate-distortion performance.
On zero-error coding of correlated sources

The authors consider the following simple encoding scenario, which they term successive encoding: /spl phi//sub x/ performs lossless encoding of X, at rate H(X), and /spl phi//sub y/ is designed assuming that the decoder has recovered X. For coding with vanishing error probability, the results of Slepian and Wolf (1973) show that time-sharing of the two possible successive encoding scenarios (obtained by interchanging the roles of X and Y) is optimal, in that all achievable rate pairs can be achieved via this strategy. But time-sharing combined with successive encoding may not be an optimal strategy in the case of zero-error coding. The authors construct a simple example for this suboptimality phenomenon.
An Achievable Rate Region for Joint Compression and Dispersive Information Routing for Networks

This paper considers the problem of minimum cost communication of correlated sources over a network with multiple sinks, which consists of distributed source coding followed by routing. We introduce a new routing paradigm called dispersive information routing (DIR), wherein the intermediate nodes are allowed to split a packet and forward subsets of the received bits on each of the forward paths. This paradigm opens up a rich class of research problems, which focus on the interplay between encoding and routing in a network. Unlike conventional routing methods such as in , DIR ensures that each sink receives just the information needed to reconstruct the sources it is required to reproduce. We demonstrate using simple examples that the proposed approach offers better asymptotic performance than conventional routing techniques. We show that, under certain assumptions on the cost function, the problem of finding the minimum cost under DIR essentially reduces to characterizing an achievable rate region for a new multiterminal information theoretic setup. While it is possible to derive an achievable region for this setup using prior results from general multiterminal source coding [3], these techniques do not exploit the underlying problem structure and thereby lead to suboptimal regions. In this paper, we propose a new coding scheme, using principles from multiple descriptions encoding , and show that it strictly improves upon a corresponding variant of coding scheme in . We further show that the new coding scheme achieves the complete rate region for certain special cases of the general setup and thereby achieves the minimum communication cost under this routing paradigm.