nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive wavelet packet image coding using an estimation-quantization framework

We extend the statistical model-based estimation-quantization (EQ) wavelet image coding algorithm introduced by LoPresto, Ramchandran and Orchard  to include an adaptive transform component. For this, we resort to the rich, space-frequency diverse, and easy-to-search library of transforms provided by the family of wavelet packet (WP) bases and their adaptive extensions. We use rate-distortion criteria to find the best basis jointly with the statistical model-based best adaptive quantization and entropy coding strategy of LoPresto et al. based on an efficient and fast tree pruning algorithm. A key underlying attribute of our paradigm is that the spatially-varying generalized Gaussian mixture model for wavelet coefficients introduced by LoPresto et al. is also applicable to the more arbitrary framework of (adaptive) wavelet packet transform coefficients as well. Our WP-EQ framework produces excellent results on standard test images. The most attractive property of our paradigm is its "universality" and robustness: based on an overall performance criterion that considers diverse classes of input test images that have varying space-frequency characteristics, it is more powerful than most of the existing image coding algorithms, using reasonable complexity, and a generic, integrated, non-training based framework.
Compressed Domain Real-time Action Recognition

We present a compressed domain scheme that is able to recognize and localize actions in real-time. The recognition problem is posed as performing a video query on a test video sequence. Our method is based on computing motion similarity using compressed domain features which can be extracted with low complexity. We introduce a novel motion correlation measure that takes into account differences in motion magnitudes. Our method is appearance invariant, requires no prior segmentation, alignment or stabilization, and is able to localize actions in both space and time. We evaluated our method on a large action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our classification results compare favorably with existing methods at only a fraction of their computational cost
Flexible time segmentations for time-varying wavelet packets

We examine the problem of how to choose a time-varying filter bank representation for a signal, which is optimal for a rate-distortion cost function. This involves deciding which filter tree to use, (frequency segmentation) and deciding when to prune or add branches to the tree, (time segmentation), For optimality the time and frequency segmentations must be done jointly and not sequentially. Herley et al.  1993) described an algorithm to find the best binary time-frequency split of a signal. In this work, we remove the constraint of binary time segmentation and study the adaptive wavelet packets expansion of a signal with arbitrary segmentation. A fast dynamic programming based algorithm is proposed to solve the optimal segmentation problem. Experimental results on different classes of sources are provided.<<ETX>>
Wavelet packet image coding using space-frequency quantization

We extend our previous work on space-frequency quantization (SFQ) for image coding from wavelet transforms to the more general wavelet packet transforms. The resulting wavelet packet coder offers a universal transform coding framework within the constraints of filterbank structures by allowing joint transform and quantizer design without assuming a priori statistics of the input image. In other words, the new coder adaptively chooses the representation to suit the image and the quantization to suit the representation. Experimental results show that, for some image classes, our new coder gives excellent coding performance.
An optimized distributed video-on-demand streaming system: Theory and design

We present a general framework for a distributed VoD content distribution problem by formulating an optimization problem yielding a highly distributed implementation that is highly scalable and resilient to changes in demand. Our solution takes into account several individual node resource constraints including disk space, network link bandwidth, and node-I/O degree bound. First, we present a natural formulation that is NP-hard. Next, we design a simple fractional storage architecture based on codes to “fluidify” the content, thereby yielding a convex content placement problem. Third, we use a recently developed Markov approximation technique to solve the NP-hard problem of topology selection under node degree bound, and propose a simple distributed solution. We prove analytically that our algorithm achieves close-to-optimal performance. We establish via simulations that the system is robust to changes in user demand or in network condition and churn.
Rate-efficient visual correspondences using random projections

We consider the problem of establishing visual correspondences in a distributed and rate-efficient fashion by broadcasting compact descriptors. Establishing visual correspondences is a critical task before other vision tasks can be performed in a wireless camera network. We propose the use of coarsely quantized random projections of descriptors to build binary hashes, and use the Hamming distance between binary hashes as the matching criterion. In this work, we derive the analytic relationship of Hamming distance between the binary hashes to Euclidean distance between the original descriptors. We present experimental verification of our result, and show that for the task of finding visual correspondences, sending binary hashes is more rate-efficient than prior approaches.
Dithered A/D Conversion of Smooth Non-Bandlimited Signals

The classical method for sampling a smooth non-bandlimited signal requires a lowpass anti-aliasing filter. In applications like distributed sampling where sampling and quantization operations precede filtering, aliasing-error is inevitable. Motivated by such applications, the sampling of smooth and bounded non-bandlimited signals whose spectra have a finite absolute first moment, without the use of an analog anti-alias lowpass filter, is studied in a centralized setup. Upper bounds for the distortion-rate function are derived by first upper-bounding the distortion with a linear combination of errors due to aliasing and quantization and then balancing their contributions by selecting an appropriate reconstruction bandwidth. For a class of dithered sampling methods, it is shown that a lower quantizer-precision can be traded for a higher sampling-rate without affecting the realizable high-rate asymptotic distortion-rate characteristics. These results are applied to signals with exponentially and polynomially decaying spectral characteristics and truncated bandlimited signals to uncover the realizable distortion-rate characteristics for these signal classes.
Inverse halftoning using wavelets

This work introduces a new approach to inverse halftoning using nonorthogonal wavelets. The distinct features of this wavelet-based approach are: 1) edge information in the highpass wavelet images of a halftone image is extracted and used to assist inverse halftoning, 2) cross-scale correlations in the multiscale wavelet decomposition are used for removing background halftoning noise while preserving important edges in the wavelet lowpass image, and 3) experiments show that our simple wavelet-based approach outperforms the best results obtained from inverse halftoning methods published in the literature, which are iterative in nature.
Overlap-based genome assembly from variable-length reads

Recently developed high-throughput sequencing platforms can generate very long reads, making the perfect assembly of whole genomes information-theoretically possible . One of the challenges in achieving this goal in practice, however, is that traditional assembly algorithms based on the de Bruijn graph framework cannot handle the high error rates of long-read technologies. On the other hand, overlap-based approaches such as string graphs  are very robust to errors, but cannot achieve the theoretical lower bounds. In particular, these methods handle the variable-length reads provided by long-read technologies in a suboptimal manner. In this work, we introduce a new assembly algorithm with two desirable features in the context of long-read sequencing: (1) it is an overlap-based method, thus being more resilient to read errors than de Bruijn graph approaches; and (2) it achieves the information-theoretic bounds even in the variable-length read setting.
Continuous error detection (CED) for reliable communication

Block cyclic redundancy check (CRC) codes represent a popular and powerful class of error detection techniques used almost exclusively in modern data communication systems. Though efficient, CRCs can detect errors only after an entire block of data has been received and processed. In this work, we exploit the "continuous" nature of error detection that results from using arithmetic codes for error detection, which provides a novel tradeoff between the amount of added redundancy and the amount of time needed to detect an error once it occurs. We demonstrate how this continuous error detection framework improves the overall performance of communication systems, and show how considerable performance gains can be attained. We focus on several important scenarios: 1) automatic repeat request (ARQ) based transmission; 2) forward error correction (FEC frameworks based on (serially) concatenated coding systems involving an inner error-correction code and an outer error-detection code; and 3) reduced state sequence estimation (RSSE) for channels with memory. We demonstrate that the proposed CED framework improves the throughput of ARQ systems by up to 15% and reduces the computational/storage complexity of FEC and RSSE by a factor of two in the comparisons that we make against state-of-the-art systems.
Efficient algorithms for the data exchange problem under fairness constraints

In this paper we study the data exchange problem where a set of users is interested in gaining access to a common file, but where each has only partial knowledge about it as side-information. Given that the collective information of all the users is sufficient to allow recovery of the entire file, the goal is to minimize the sum of convex functions over the number of bits each user has to transmit in order to achieve the goal. A special case of this objective is the uniform cost, which essentially leads to a “fair” communication load distribution among the users. We consider the following side-information settings: (i) side-information in the form of uncoded packets of the file, where the users' side-information consists of subsets of the file packets; and (ii) side-information in the form of linearly correlated packets, where the users have access to linear combinations of the file packets. We provide a polynomial-time deterministic algorithm that finds an optimal communication scheme w.r.t. the transmission cost. To further lower the complexity, we also give a simple randomized algorithm inspired by our deterministic algorithm.
Space-frequency quantization for wavelet image coding

A new class of image coding algorithms coupling standard scalar quantization of frequency coefficients with tree-structured quantization (related to spatial structures) has attracted wide attention because its good performance appears to confirm the promised efficiencies of hierarchical representation. This paper addresses the problem of how spatial quantization modes and standard scalar quantization can be applied in a jointly optimal fashion in an image coder. We consider zerotree quantization (zeroing out tree-structured sets of wavelet coefficients) and the simplest form of scalar quantization (a single common uniform scalar quantizer applied to all nonzeroed coefficients), and we formalize the problem of optimizing their joint application. We develop an image coding algorithm for solving the resulting optimization problem. Despite the basic form of the two quantizers considered, the resulting algorithm demonstrates coding performance that is competitive, often outperforming the very best coding algorithms in the literature.
Rate-distortion efficient video transmission from multiple servers

We address the problem of streaming video efficiently from mUl­ tiple servers in a distributed manner. Invoking a scalable video encoding format, we describe a network-friendly , rate-distortion efficient distributed streaming algorithm based on a robust dis­ tributed encoding paradigm. The algorithm is efficiently matched dynamically to both the video content and the network channel conditions. Due to t he d iversity effect of multiple servers, our dis­ tributed algorithm is immune to single points of failure, provides natural load-balancing of the multiple servers and a graceful degra­ dation in quality with an Increase in multi-server load or a decrease in network throughput. We present the results of our Internet ex­ periments that validate our hypotheses.
Information-Theoretic Limits on Sparse Signal Recovery: Dense versus Sparse Measurement Matrices

We study the information-theoretic limits of exactly recovering the support set of a sparse signal, using noisy projections defined by various classes of measurement matrices. Our analysis is high-dimensional in nature, in which the number of observations n, the ambient signal dimension p, and the signal sparsity k are all allowed to tend to infinity in a general manner. This paper makes two novel contributions. First, we provide sharper necessary conditions for exact support recovery using general (including non-Gaussian) dense measurement matrices. Combined with previously known sufficient conditions, this result yields sharp characterizations of when the optimal decoder can recover a signal for various scalings of the signal sparsity k and sample size n, including the important special case of linear sparsity (k = ¿(p)) using a linear scaling of observations (n = ¿(p)). Our second contribution is to prove necessary conditions on the number of observations n required for asymptotically reliable recovery using a class of ¿-sparsified measurement matrices, where the measurement sparsity parameter ¿(n, p, k) ¿ (0,1] corresponds to the fraction of nonzero entries per row. Our analysis allows general scaling of the quadruplet (n, p, k, ¿) , and reveals three different regimes, corresponding to whether measurement sparsity has no asymptotic effect, a minor effect, or a dramatic effect on the information-theoretic limits of the subset recovery problem.
Image coding based on a morphological representation of wavelet data

In this paper, an experimental study of the statistical properties of wavelet coefficients of image data is presented, as well as the design of two different morphology-based image coding algorithms that make use of these statistics. A salient feature of the proposed methods is that, by a simple change of quantizers, the same basic algorithm yields high performance embedded or fixed rate coders. Another important feature is that the shape information of morphological sets used in this coder is encoded implicitly by the values of wavelet coefficients, thus avoiding the use of explicit and rate expensive shape descriptors. These proposed algorithms, while achieving nearly the same objective performance of state-of-the-art zerotree based methods, are able to produce reconstructions of a somewhat superior perceptual quality, due to a property of joint compression and noise reduction they exhibit.
Channel coding with strictly casual colored side-information at transmitter

In this paper we study channels where a side-information sequence is available strictly causally at the transmitter, i.e., the channel input at time k may depend on the side-information sequence up to and including time k - 1. This is in contrast to Shannon's channel coding with causal side-information at the transmitter where the channel input at time k may depend on the side-information sequence up to and including time k. We consider side-information sequences with memory and study the Gaussian and modulo-additive channels.
A comparative study of DCT- and wavelet-based image coding

We undertake a study of the performance difference of the discrete cosine transform (DCT) and the wavelet transform for both image and video coding, while comparing other aspects of the coding system on an equal footing based on the state-of-the-art coding techniques. The studies reveal that, for still images, the wavelet transform outperforms the DCT typically by the order of about 1 dB in peak signal-to-noise ratio. For video coding, the advantage of wavelet schemes is less obvious. We believe that the image and video compression algorithm should be addressed from the overall system viewpoint: quantization, entropy coding, and the complex interplay among elements of the coding system are more important than spending all the efforts on optimizing the transform.
n-channel symmetric multiple descriptions - part I: (n, k) source-channel erasure codes

In this two-part paper, we present a new achievable rate region for the general n-channel symmetric multiple descriptions problem. In part I, inspired by the concept of maximum-distance separable (MDS) erasure channel codes, we consider a special case of this rate region, where the source is encoded into n descriptions each with rate R. These descriptions are transmitted over n bandwidth constrained and errorless channels. During transmission, a subset of these channels can break down, thus erasing the corresponding descriptions. The decoder is interested in recovering the source with the reception of at least k descriptions. Thus, the encoder is allowed to sample only one realization of this breakdown process during the entire transmission. For Gaussian sources, we have the following interesting result: when any k descriptions arrive, the achievable distortion exactly matches the optimal distortion-rate performance corresponding to a source rate of kR bits; with the reception of any m > k descriptions, the source reconstruction quality is strictly better, the improvement being nearly linear in the number of descriptions received.
A reliable decentralized Peer-to-Peer Video-on-Demand system using helpers

We propose a decentralized Peer-to-Peer (P2P) Video-on-Demand (VoD) system. The traditional data center architecture is eliminated and is replaced by a large set of distributed, dynamic and individually unreliable helpers. The system leverages the strength of numbers to effect reliable cooperative content distribution, removing the drawbacks of conventional data center architectures including complexity of maintenance, high power consumption and lack of scalability. In the proposed VoD system, users and helper “servelets” cooperate in a P2P manner to deliver the video stream. Helpers are preloaded with only a small fraction of parity coded video data packets, and form into swarms each serving partial video content. The total number of helpers is optimized to guarantee high quality of service. In cases of helper churn, the helper network is also able to regenerate itself by users and helpers working cooperatively to repair the lost data, which yields a highly reliable system. Analysis and simulation results corroborate the feasibility and effectiveness of the proposed architecture.
Reverse-engineering BitTorrent: A Markov approximation perspective

In this paper we understand BitTorrent protocol from a Markov approximation perspective. We show that together with the underlying rate control algorithm, the rarest first algorithm and choking algorithm in BitTorrent protocol implicitly solve a cooperative combinatorial network utility maximization problem in a distributed manner. This understanding allows us to access properties of BitTorrent from a fresh perspective, including performance optimality, convergence and impacts of design parameters. Our numerical evaluations validate the analytical results.
SAFFRON: A fast, efficient, and robust framework for group testing based on sparse-graph codes

The group testing problem is to identify a population of K defective items from a set of n items by pooling groups of items. The result of a test for a group of items is positive if any of the items in the group is defective and negative otherwise. The goal is to judiciously group subsets of items such that defective items can be reliably recovered using the minimum number of tests, while also having a low-complexity decoder. We describe SAFFRON (sparse-graph codes framework for group testing), a non-adaptive group testing scheme that recovers at least a (1 - ε)-fraction (for any arbitrarily small ε > 0) of K defective items with high probability with m = 6C(ε)K log2 n tests, where C(ε) is a precisely characterized constant that depends only on o. For instance, it can provably recover at least (1 - 10-6)K defective items with m ≃ 68K log2 n tests. The computational complexity of the decoding algorithm is O(K log n), which is order-optimal. Further, we describe a systematic methodology to robustify SAFFRON such that it can reliably recover the set of K defective items even in the presence of erroneous or noisy test results. We also propose Singleton-Only-SAFFRON, a variant of SAFFRON, that recovers all the K defective items with m = 2e(1+α)K log K log2 n tests with probability 1 - O(1/Kα), where α > 0 is a constant. Our key intellectual contribution involves the pioneering use of powerful density-evolution methods of modern coding theory (e.g. sparse-graph codes) for efficient group testing design and performance analysis.
On rate-constrained distributed estimation in unreliable sensor networks

We study the problem of estimating a physical process at a central processing unit (CPU) based on noisy measurements collected from a distributed, bandwidth-constrained, unreliable, network of sensors, modeled as an erasure network of unreliable "bit-pipes" between each sensor and the CPU. The CPU is guaranteed to receive data from a minimum fraction of the sensors and is tasked with optimally estimating the physical process under a specified distortion criterion. We study the noncollaborative (i.e., fully distributed) sensor network regime, and derive an information-theoretic achievable rate-distortion region for this network based on distributed source-coding insights. Specializing these results to the Gaussian setting and the mean-squared-error (MSE) distortion criterion reveals interesting robust-optimality properties of the solution. We also study the regime of clusters of collaborative sensors, where we address the important question: given a communication rate constraint between the sensor clusters and the CPU, should these clusters transmit their "raw data" or some low-dimensional "local estimates"? For a broad set of distortion criteria and sensor correlation statistics, we derive conditions under which rate-distortion-optimal compression of correlated cluster-observations separates into the tasks of dimension-reducing local estimation followed by optimal distributed compression of the local estimates.
DRESS codes for the storage cloud: Simple randomized constructions

We introduce an efficient family of exact regenerating codes for data storage in large-scale distributed systems. We refer to these new codes as Distributed Replication-based Exact Simple Storage (DRESS) codes. A key property of DRESS codes is their very efficient distributed and uncoded repair and growth processes that have minimum bandwidth, reads and computational overheads. This property is essential for large-scale systems with high reliability and availability requirements. DRESS codes will first encode the file using a Maximum Distance Separable (MDS) code, then place multiple replicas of the coded packets on different nodes in the system. We propose a simple and flexible randomized scheme for placing those replicas based on the balls-and-bins model. Our construction showcases the power of the probabilistic approach in constructing regenerating codes that can be efficiently repaired and grown.