nils-herrmann/clean_scirepeval huggingface dataset:
Bayesian tuning for support detection and sparse signal estimation via iterative shrinkage-thresholding

Iterative shrinkage-thresholding algorithms provide simple methods to recover sparse signals from compressed measurements. In this paper, we propose a new class of iterative shrinkage-thresholding algorithms which preserve the computational simplicity and improve iterative estimation by incorporating a soft support detection. Indeed, at each iteration, by learning the components that are likely to be nonzero from the current signal estimation using Bayesian techniques, the shrinkage-thresholding step is adaptively tuned and optimized. Unlike other adaptive methods, we are able to prove, under suitable conditions, the convergence of the proposed methods. Moreover, we show through numerical experiments that the proposed methods outperform classical shrinkage-thresholding in terms of rate of convergence and of sparsity-undersampling tradeoff.
Fast and robust EM-based IRLS algorithm for sparse signal recovery from noisy measurements

In this paper, we analyze a new class of iterative re-weighted least squares (IRLS) algorithms and their effectiveness in signal recovery from incomplete and inaccurate linear measurements. These methods can be interpreted as the constrained maximum likelihood estimation under a two-state Gaussian scale mixture assumption on the signal. We show that this class of algorithms, which performs exact recovery in noiseless scenarios under suitable assumptions, is robust even in presence of noise. Moreover these methods outperform classical IRLS for ℓτ -minimization with τ ∈ (0; 1] in terms of accuracy and rate of convergence.
Stable limit cycles in recurrent neural networks

The goal of this paper is to contribute to the understanding of the dynamics of recurrent neural networks. Specifically, we establish conditions for the existence of stable limit cycles, whose existence is equivalent to the echo state property. We provide sufficient conditions for the convergence to a trajectory that is uniquely determined by the driving input signal, independently of the initial states. Under these conditions, the hidden-to-hidden matrix may have norm larger than one. This result can help extending the memory of recurrent neural networks, since earlier work has shown that large matrix norms in the hidden layer imply longer memory duration. This would also increase the design options for recurrent neural networks.
Energy obfuscation for compressive encryption and processing

Compressed Sensing enables both computationally secure encryption and signal processing in the compressed domain. Even though these characteristics have always been considered in separate fashion, in this paper we propose a novel method that takes into account these features jointly. As a result we obtain provable secrecy guarantees and enable fast signal processing. In more detail, we show that it is possible to perform anomaly detection relying on the measurements information leakage. At the same time, we can prevent attackers trying to obtain confidential data by obfuscating the information leakage. We show the effectiveness of such method through theoretical bounds and numerical experiments.
Sliding-Window Digital Fountain Codes for Streaming of Multimedia Contents

Digital fountain codes are becoming increasingly important for multimedia communications over networks subject to packet erasures. These codes have significantly lower complexity than Reed-Solomon ones, exhibit high erasure correction performance, and are very well suited to generating multiple equally important descriptions of a source. In this paper we propose an innovative scheme for streaming multimedia contents by using digital fountain codes applied over sliding windows, along with a suitably modified belief-propagation decoder. The use of overlapped windows allows one to have a virtually extended block, which yields superior performance in terms of packet recovery. Simulation results using LT codes show that the proposed algorithm has better performance in terms of efficiency, reliability and memory with respect to fixed-window encoding.
Image compression practices and standards for geospatial information systems

Compression technology is becoming increasingly important in geospatial information systems. In this paper we address some of the most relevant compression issues for remote sensing applications, and highlight the potential benefits of the JPEG set of standards. In particular, we review the JPEG, JPEG 2000, and JPEG-LS compression standards, and the JPIP protocol for interactive image retrieval. Finally, we discuss the use of compressed-domain processing, along with the use of flexible file formats for efficient storage and access to metadata.
Decoder-driven adaptive distributed arithmetic coding

We propose a distributed source coding system for data collected by sensor networks. It uses a feedback channel between the sensors and the gateway node (i.e., the joint decoder) but, unlike previous systems, the encoding process is driven by the decoder. Compression is performed using distributed arithmetic coding, which is extended to adaptively estimate the source probabilities. Specifically, the decoder estimates marginal and conditional probabilities, and sends them back to the sensors to drive the distributed arithmetic coding process. This reduces the decoding delay, and potentially eliminates the need of rate-compatible Slepian-Wolf codes.
Low-complexity lossy compression of hyperspectral images via informed quantization

Lossy compression of hyperspectral and ultraspectral images is traditionally performed using 3D transform coding. This approach yields good performance, but the complexity and memory requirements make it unsuitable for onboard compression. In this paper we propose a low-complexity lossy compression scheme based on prediction, quantization and rate-distortion optimization. The scheme employs coset codes coupled with the newconcept of “informed quantization”, and requires no entropy coding. The performance of the resulting algorithm is competitive with that of state-of-the-art 3D transform coding schemes, but the complexity is immensely lower, making it suitable for onboard compression at high throughputs.
Comparison of Reed-Solomon and Raptor codes for the protection of video on-demand on the erasure channel

Streaming on-demand video services can be provided to an end user by transmitting video data as a sequence of Internet Protocol (IP) packets over the network. In order to maintain a sufficient video quality at the end user, video packets must be protected against erasures by means of a suitable form of error control. In this contribution we make a comparison of 2 classes of Forward Error Correction (FEC) codes: the Reed-Solomon (RS) codes and the Raptor codes. We present the decoding complexity analysis of these codes and compare their erasure decoding performance. For a given decoding complexity, the codewords of the Raptor code can be taken much longer than those of the RS code, because the decoding of the former code involves only XOR operations. For a performance target of less than one decoding error event in 4 hours, a video bitrate of 7.3 Mbit/s, a latency constraint of 10 seconds and a transmission overhead of 20%, equal erasure probabilities at the decoder input are allowed for the RS(254,212) code and for the Raptor code of size (8760,7300), whereas this RS code has a 12 times higher decoding complexity.
Superpixel-driven graph transform for image compression

Block-based compression tends to be inefficient when blocks contain arbitrary shaped discontinuities. Recently, graph-based approaches have been proposed to address this issue, but the cost of transmitting graph topology often overcome the gain of such techniques. In this work we propose a new Superpixel-driven Graph Transform (SDGT) that uses clusters of superpixels, which have the ability to adhere nicely to edges in the image, as coding blocks and computes inside these homogeneously colored regions a graph transform which is shape-adaptive. Doing so, only the borders of the regions and the transform coefficients need to be transmitted, in place of all the structure of the graph. The proposed method is finally compared to DCT and the experimental results show how it is able to outperform DCT both visually and in term of PSNR.
Analysis of One-Time Random Projections for Privacy Preserving Compressed Sensing

In this paper, the security of the compressed sensing (CS) framework as a form of data confidentiality is analyzed. Two important properties of one-time random linear measurements acquired using a Gaussian independent identically distributed matrix are outlined: 1) the measurements reveal only the energy of the sensed signal and 2) only the energy of the measurements leaks information about the signal. An important consequence of the above facts is that CS provides information theoretic secrecy in a particular setting. Namely, a simple strategy based on the normalization of the Gaussian measurements achieves, at least in theory, perfect secrecy, enabling the use of CS as an additional security layer in privacy preserving applications. In the generic setting in which CS does not provide information theoretic secrecy, two alternative security notions linked to the difficulty of estimating the energy of the signal and distinguishing equal-energy signals are introduced. Useful bounds on the mean square error of any possible estimator and the probability of error of any possible detector are provided and compared with the simulations. The results indicate that CS is in general not secure according to cryptographic standards, but may provide a useful built-in data obfuscation layer.
Lifting integer wavelets towards linearity

The integer wavelet transform (IWT) is dealt with. For for image compression performance, it is known that the IWT has a worse performance than the discrete wavelet transform (DWT). We analyse the IWT signal representation mechanism with respect to the DWT one, in order to explain this difference. Insight into this mechanism suggests a simple, computationally efficient yet very effective way to improve the IWT compression performance up to the DWT level. Simulation results are presented for both natural and synthetic images, these latter being more critical for the IWT; it is shown that the proposed method permits one to fill the gap between the IWT and the DWT performance.
Ensuring quality of service for image transmission: hybrid loss protection

We present hybrid loss protection as a new channel coding and packetization scheme for image transmission over nonprioritized lossy packet networks. The scheme employs an interleaver-based structure, and attempts to maximize the expected peak signal-to-noise ratio (PSNR) at the receiver given the constraint that the probability of failure, i.e., the probability that the PSNR of the decoded image is below a given threshold, is upper-bounded by a user-defined value. A new code-allocation algorithm is proposed, which employs Gilbert-Elliot modeling of the network statistics. Experimental results are provided in the case of transmission of images encoded by SPIHT and JPEG 2000 over a wireline, as well as a wireless UMTS-based Internet connection.
Lossy predictive coding of SAR raw data

In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for this signal. We show that, due to the statistical properties of the SAR signal, an along-range linear predictor with few taps is able to effectively capture most of the raw signal correlation. The proposed predictive coding algorithm, which performs quantization of the prediction error, optionally followed by entropy coding, exhibits a number of advantages, and notably an interesting performance/complexity trade-off, with respect to other techniques such as flexible block adaptive quantization (FBAQ) or methods based on transform-coding; fractional output bit-rates can also be achieved in the entropy-constrained mode. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems.
Energy consumption and image quality in wireless video-surveillance networks

Wireless video-surveillance networks are gaining increasing popularity due to the number of applications they make possible. We address the problem of designing a wireless video-surveillance network so as to optimize its performance. In particular, we investigate the possible trade-offs between energy consumption and image quality. We provide simulation results showing that video compression can be very beneficial in reducing data transmission costs, provided that the energy cost of video compression is low. Moreover, we discuss the impact of compression on image delay.
A pattern detection and compression algorithm based on the joint wavelet and Radon transform

The linear feature detection task can be efficiently accomplished by means of the joint Radon transform (RT) and continuous wavelet transform (CWT) analysis. With regard to the former, a fast algorithm for its computation is described in detail, and the compression and robustness capabilities of the transform are discussed. It is found that the RT is suitable for transmission and fast detection purposes. A completely new, highly efficient CWT based algorithm, to be applied in addition to the classical thresholding methods in the Radon domain, is then discussed. The algorithm is capable to detect linear features even in the presence of large amounts of noise.
All-integer Hough transform: performance evaluation

The Hough transform is a widely used tool for line detection mainly due to its robustness to noise; on the other hand, it is also known to be computationally expensive, thus often preventing real-time operation. In this paper we evaluate the performance of an all-integer version of the Hough transform, implemented without floating point operations. We show that the integer transform is 2 to 3.5 times faster than the standard one on most platforms, while its performance loss is negligible.
Multimedia Selective Encryption by Means of Randomized Arithmetic Coding

We propose a novel multimedia security framework based on a modification of the arithmetic coder, which is used by most international image and video coding standards as entropy coding stage. In particular, we introduce a randomized arithmetic coding paradigm, which achieves encryption by inserting some randomization in the arithmetic coding procedure; notably, and unlike previous works on encryption by arithmetic coding, this is done at no expense in terms of coding efficiency. The proposed technique can be applied to any multimedia coder employing arithmetic coding; in this paper we describe an implementation tailored to the JPEG 2000 standard. The proposed approach turns out to be robust towards attempts to estimating the image or discovering the key, and allows very flexible protection procedures at the code-block level, allowing to perform total and selective encryption, as well as conditional access
Spatio-temporal video error concealment with perceptually optimized mode selection

We propose a spatio-temporal error concealment algorithm for video transmission in an error-prone environment. The proposed technique employs motion vector estimation, edge-preserving interpolation, and texture analysis/synthesis. It has two main advantages with respect to existing methods, namely: (i) it aims at optimizing the visual quality of the restored video, and not only PSNR; and (ii) it employs an automatic mode selection algorithm in order to decide, on a macroblock basis, whether to use the spatial restoration, the temporal one, or a combination thereof. The algorithm has been applied to H.26L video, providing satisfactory performance over a large set of operating conditions.
Near-lossless digital watermarking for copyright protection of remote sensing images

We propose near-lossless digital watermarking for copyright protection of remote sensing images. In particular, we show that, by forcing a maximum absolute difference between the original and watermarked scene, the near-lossless paradigm makes it possible to decrease the effect of watermarking on remote sensing applications to be carried out on the images. As an example, the effect of near-lossless watermarking on image classification is analyzed.
Determination of a segment endpoint by means of the Radon transform

In this paper, a detection algorithm for linear features based on the Radon transform (RT) and on the continuous wavelet transform (CWT) is addressed. Particular emphasis is put on the discussion of the fast algorithm for the RT implementation; the errors introduced by RT discretization and data interpolation are discussed, and counteractions are suggested. Moreover, a method for the determination of the endpoints of the detected segments is proposed.
Graded quantization: Democracy for multiple descriptions in compressed sensing

The compressed sensing paradigm allows to efficiently represent sparse signals by means of their linear measurements. However, the problem of transmitting these measurements to a receiver over a channel potentially prone to packet losses has received little attention so far. In this paper, we propose novel methods to generate multiple descriptions from compressed sensing measurements to increase the robustness over unreliable channels. In particular, we exploit the democracy property of compressive measurements to generate descriptions in a simple manner by partitioning the measurement vector and properly allocating bit-rate, outperforming classical methods like the multiple description scalar quantizer. In addition, we propose a modified version of the Basis Pursuit Denoising recovery procedure that is specifically tailored to the proposed methods. Experimental results show significant performance gains with respect to existing methods.
On the security of random linear measurements

In this paper, we analyze the security of compressed sensing (CS) as a cryptosystem. We demonstrate that random linear measurements acquired using a Gaussian i.i.d. matrix reveal only the energy of the sensed signal, and that only the energy of the measurements leaks information about the signal. We provide useful bounds for assessing the information leakage about the energy, linking those bounds to the minimum mean square error achievable by practical estimators. Moreover, we propose a simple strategy based on the normalization of the measurements which achieves, at least in theory, perfect secrecy, enabling the use of CS-based encryption in practical cryptosystems.
Spatio-temporal video error concealment with perceptually optimized mode selection

We proposed a spatio-temporal error concealment algorithm for video transmission in an error-prone environment. The proposed technique employs motion vector estimation, edge-preserving interpolation, and texture analysis/synthesis. It has two main advantages with respect to existing methods, namely: i) it aims at optimizing the visual quality of the restored video, and not only PSNR; and ii) it employs an automatic mode selection algorithm in order to decide, on a macroblock basis, whether to use the spatial restoration, the temporal one, or a combination thereof. The algorithm has been applied to H26L video, providing satisfactory performance over a large set of operating conditions.
Compressive hyperspectral imaging using progressive total variation

Compressed Sensing (CS) is suitable for remote acquisition of hyperspectral images for earth observation, since it could exploit the strong spatial and spectral correlations, allowing to simplify the architecture of the onboard sensors. Solutions proposed so far tend to decouple spatial and spectral dimensions to reduce the complexity of the reconstruction, not taking into account that onboard sensors progressively acquire spectral rows rather than acquiring spectral channels. For this reason, we propose a novel progressive CS architecture based on separate sensing of spectral rows and joint reconstruction employing Total Variation. Experimental results run on raw AVIRIS and AIRS images confirm the validity of the proposed system.
An error concealment algorithm for streaming video

A known problem in video streaming is that loss of a packet usually results into loss of a whole video frame. In this paper we propose an error concealment algorithm specifically designed to handle this sort of losses. The technique exploits information in a few past frames (namely the motion vectors) in order to estimate the forward motion vectors of the last received frame. This information is used to project the last frame onto an estimate of the missing frame. The algorithm has been tested on MPEG-2 video, providing very satisfactory results, and outperforming by several dBs in PSNR the concealment technique based on repetition of the last received frame.
Image retrieval based on compressed camera sensor fingerprints

Image retrieval is the process of finding images from a large collection, satisfying a user-specified criterion. Content-based retrieval has been the traditional paradigm, in which one wishes to find images whose content is similar to a query. In this paper we explore a novel criterion for image search, based on forensic principles. We address the problem of retrieving all the photos in a collection that have been acquired by a specific device which is presented to the system as a query. This is an important forensic problem, whose solution could be very useful for detecting improper usage of pictures. We do not rely on metadata such as Exif headers because they can be unavailable, or easily manipulated, and in most cases cannot identify the specific device. We rely instead on a forensic tool called Photo Response Non-Uniformity (PRNU), which constitutes a reliable fingerprint of a camera sensor. We examine recent advances in compression of such fingerprints, which allow to address the previously unexplored image retrieval problem on large scales.
On the secrecy of compressive cryptosystems under finite-precision representation of sensing matrices

In recent years, the Compressed Sensing (CS) framework has been shown to be an effective private key cryptosystem. If infinite precision is available, then it has been shown that spherical secrecy can be achieved. However, despite its theoretically proven secrecy properties, the only practically feasible implementations involve the use of Bernoulli sensing matrices. In this work, we show that different distributions employing a much larger finite alphabet can be considered. More in detail, we consider the use of quantized Gaussian sensing matrices and experimentally show that, besides being suitable for practical implementation, they can achieve higher secrecy with respect to Bernoulli sensing matrices. Furthermore, we show that this approach can be used to tune the secrecy of the CS cryptosystems based on the available machine precision.
Progressive Compressed Sensing and Reconstruction of Multidimensional Signals Using Hybrid Transform/Prediction Sparsity Model

Compressed sensing (CS) is an innovative technique allowing to represent signals through a small number of their linear projections. Hence, CS can be thought of as a natural candidate for acquisition of multidimensional signals, as the amount of data acquired and processed by conventional sensors could create problems in terms of computational complexity. In this paper, we propose a framework for the acquisition and reconstruction of multidimensional correlated signals. The approach is general and can be applied to D dimensional signals, even if the algorithms we propose to practically implement such architectures apply to 2-D and 3-D signals. The proposed architectures employ iterative local signal reconstruction based on a hybrid transform/prediction correlation model, coupled with a proper initialization strategy.