nils-herrmann/clean_scirepeval huggingface dataset:
Conditional Access to H.264/AVC Video with Drift Control

In this paper we address the problem of providing conditional access to video sequences, namely, to generate a low-quality video to be used as preview, which can be decoded at full quality if a decryption key is obtained. We propose and investigate the performance of two different techniques, based on smoothing and separate encoding in the compressed domain, and motion vector perturbation. We show that these techniques are able to provide conditional access to different quality levels of H.264/AVC video with very small rate overhead, and that their combination can provide different levels of security towards malicious attacks
A Novel Progressive Image Scanning and Reconstruction Scheme Based on Compressed Sensing and Linear Prediction

Compressed sensing (CS) is an innovative technique allowing to represent signals through a small number of their linear projections. In this paper we address the application of CS to the scenario of progressive acquisition of 2D visual signals in a line-by-line fashion. This is an important setting which encompasses diverse systems such as flatbed scanners and remote sensing imagers. The use of CS in such setting raises the problem of reconstructing a very high number of samples, as are contained in an image, from their linear projections. Conventional reconstruction algorithms, whose complexity is cubic in the number of samples, are computationally intractable. In this paper we develop an iterative reconstruction algorithm that reconstructs an image by iteratively estimating a row, and correlating adjacent rows by means of linear prediction. We develop suitable predictors and test the proposed algorithm in the context of flatbed scanners and remote sensing imaging systems. We show that this approach can significantly improve the results of separate reconstruction of each row, providing very good reconstruction quality with reasonable complexity.
Error-Resilient and Low-Complexity Onboard Lossless Compression of Hyperspectral Images by Means of Distributed Source Coding

In this paper, we propose a lossless compression algorithm for hyperspectral images inspired by the distributed-source-coding (DSC) principle. DSC refers to separate compression and joint decoding of correlated sources, which are taken as adjacent bands of a hyperspectral image. This concept is used to design a compression scheme that provides error resilience, very low complexity, and good compression performance. These features are obtained employing scalar coset codes to encode the current band at a rate that depends on its correlation with the previous band, without encoding the prediction error. Iterative decoding employs the decoded version of the previous band as side information and uses a cyclic redundancy code to verify correct reconstruction. We develop three algorithms based on this paradigm, which provide different tradeoffs between compression performance, error resilience, and complexity. Their performance is evaluated on raw and calibrated AVIRIS images and compared with several existing algorithms. Preliminary results of a field-programmable gate array implementation are also provided, which show that the proposed algorithms can sustain an extremely high throughput.
Minimally non-linear integer wavelets for image coding

In this paper we deal with the problem of finding high performance factorizations of wavelet filters to be employed within the lifting scheme framework to yield the integer wavelet transform (IWT). A method is proposed, based on the search for the factorization yielding the minimally non-linear iterated graphic function. Results are reported, referring to a set of popular wavelet filters, which show that the obtained implementations lead to IWTs achieving very satisfactory results for both lossy and lossless image compression.
Autoregressive process parameter estimation from compressed sensing measurements

In this paper we introduce a least squares estimator of the regression coefficients of an autoregressive process acquired by means of Compressed Sensing (CS). Unlike common CS problems in which we only know that the signal is sparse, using the proposed autoregressive model we can gain knowledge about the structure of the original signal without recovering it. This problem is addressed by introducing an ad-hoc sensing matrix able to preserve the structure of the regression. We numerically validate the performance of this matrix. Moreover, we present applications that naturally exploit this additional information we can directly obtain from the compressed data, and particularly power spectral density estimation from CS measurements.
Compressive signal processing with circulant sensing matrices

Compressive sensing achieves effective dimensionality reduction of signals, under a sparsity constraint, by means of a small number of random measurements acquired through a sensing matrix. In a signal processing system, the problem arises of processing the random projections directly, without first reconstructing the signal. In this paper, we show that circulant sensing matrices allow to perform a variety of classical signal processing tasks such as filtering, interpolation, registration, transforms, and so forth, directly in the compressed domain and in an exact fashion, i.e., without relying on estimators as proposed in the existing literature. The advantage of the techniques presented in this paper is to enable direct measurement-to-measurement transformations, without the need of costly recovery procedures.
Affine scale space for viewpoint invariant keypoint detection

The research of affine scale space is to create a more general approach to the affine invariant image scale representation by modifying the corresponding Gaussian filters in order to cope with the specific change of view point. It has the purpose to retain a linear relationship with the transiting of the view point. With this linear relationship, the affine scale space could be established as a more general approach for the affine invariant image retrieval, including affine feature detection and affine feature descriptor. The scope of this paper is to discuss the accessible to the affine scale space, its performance and a practical implementation to construct it in order to cope with the high complexity brought in by the scale space and the affine adaptation.
Optimization and implementation of the integer wavelet transform for image coding

This paper deals with the design and implementation of an image transform coding algorithm based on the integer wavelet transform (IWT). First of all, criteria are proposed for the selection of optimal factorizations of the wavelet filter polyphase matrix to be employed within the lifting scheme. The obtained results lead to the IWT implementations with very satisfactory lossless and lossy compression performance. Then, the effects of finite precision representation of the lifting coefficients on the compression performance are analyzed, showing that, in most cases, a very small number of bits can be employed for the mantissa keeping the performance degradation very limited. Stemming from these results, a VLSI architecture is proposed for the IWT implementation, capable of achieving very high frame rates with moderate gate complexity.
Gaussian Mixtures Based IRLS for Sparse Recovery With Quadratic Convergence

In this paper, we propose a new class of iteratively re-weighted least squares (IRLS) for sparse recovery problems. The proposed methods are inspired by constrained maximum-likelihood estimation under a Gaussian scale mixture (GSM) distribution assumption. In the noise-free setting, we provide sufficient conditions ensuring the convergence of the sequences generated by these algorithms to the set of fixed points of the maps that rule their dynamics and derive conditions verifiable a posteriori for the convergence to a sparse solution. We further prove that these algorithms are quadratically fast in a neighborhood of a sparse solution. We show through numerical experiments that the proposed methods outperform classical IRLS for lτ-minimization with τ ∈ (0,1] in terms of speed and of sparsity-undersampling tradeoff and are robust even in presence of noise. The simplicity and the theoretical guarantees provided in this paper make this class of algorithms an attractive solution for sparse recovery problems.
Embedding unequal error protection into turbo codes

A technique to embody unequal error protection into turbo codes is presented. Through proper optimization of the turbo codes interleaver, different levels of error protection can be accommodated within the same encoded block. The effectiveness of the proposed approach is demonstrated in the case of transmission of JPEG2000 coded images across an AWGN channel. The unequal error protection offered by the turbo code is able to match the progressive nature of the encoded image well, thus improving the quality of the decoded images at the receiver.
Compressive classification based on autoregressive features

Compressed Sensing (CS) allows to efficiently acquire and compress a signal within a single operation. However, reconstructing the original signal is typically expensive. Hence, being able to perform signal processing operations in the compressed domain is extremely important. In this paper we propose a new technique to perform classification tasks in the compressed domain. In order to perform compressive classification we employ feature vectors which can estimated directly in the compressed domain. The feature vectors we propose are the autoregressive (AR) parameters since they are well suited to approximate signals in a more general fashion than specifically suited feature vectors. We validate the proposed technique with two kind of natural signals: texture and speech showing the effectiveness of the proposed technique which can achieve excellent classification performance.
Guaranteeing quality of service for image transmission by means of hybrid loss protection

In the context of joint source and channel coding, unequal loss protection is often used to make image data more robust to possible packet losses. The allocation of source and code symbols is customarily done so as to maximize the expected PSNR at the receiver. We propose a new objective function, attempting to maximize PSNR given a constraint on the system probability of failure, so that PSNR is constrained to be above a given threshold with a given probability. This leads to the definition of a hybrid loss protection scheme, and the related allocation algorithm, which is able to satisfy this constraint. Experimental results are reported, related to the transmission of JPEG2000-compressed images over the Internet. It is shown that the proposed hybrid approach outperforms existing algorithms in terms of PSNR, while requiring less computational resources.
DSP performance comparison between lifting and filter banks for image coding

The lifting scheme is a very well-known computationally efficient alternative to the filter bank scheme for evaluating the discrete wavelet transform of signals and images. However, the actual computational saving is still a matter of debate. On one hand, theoretical results in the literature report an asymptotic upper-bound of two for very long wavelet filters. On the other hand, it is worth wondering to what extent the architecture of the processor used can actually bias this gain. In this paper we tackle this problem from an implementation perspective, and profile the execution time of the two algorithms on a digital signal processor. Both the real-valued and the integer versions of the wavelet transform are considered. The quantitative results are used to gain some insight on the way the processor architecture affects the algorithms.
Joint near-lossless watermarking and compression for the authentication of remote sensing images

In this paper we present a new watermarking algorithm for joint near-lossless compression and authentication of remote sensing images. The adopted compression algorithm is the standard JPEG-LS algorithm. Our methodology has been designed by integrating into the standard JPEG-LS compression algorithm, by means of a stripe approach, a known authentication technique derived from Fridrich. This procedure points out two advantages: firstly, the produced bit-stream is perfectly compliant with the JPEG-LS standard, secondly, when the image has been decoded, it is always authenticated because information has been embedded in the reconstructed values. Near-lossless coding does not harm authentication procedure and robustness against different attacks is preserved
Distributed scheduling for P2P IPTV with low-delay network coding

This paper presents a distributed push-based packet scheduling algorithm for Peer-to-Peer (P2P) IPTV streaming with Network Coding (NC) over unorganized random network topologies. The benefits of random-push NC for P2P system have been investigated previously, packet scheduling in P2P systems using NC is still largely uninvestigated. The proposed scheduling algorithm aims to opt for a policy that maximizes the number of peers that completely retrieve the media content. We compare the performance of the proposed scheduling algorithm with a typical random scheduler as reference. Our results show increased performance in terms of media quality and bandwidth consumption.
Predictive graph construction for image compression

In this work, we propose a new method of graph construction for graph-based image compression. In particular, because of the overhead incurred by graph transmission to the receiver, we focus our attention to develop an efficient method to construct and to code the graph representation of the image. The proposed method employs innovative edge metrics, quantization and prediction techniques, leading to a compact yet high-quality graph, corresponding to a very efficient transform that performs very well on natural as well as piece-wise smooth images. We have tested our method on different images and, compared to the standard DCT, it provides an average quality gain of 1.6 dB.
Sliding-Window Raptor Codes for Efficient Scalable Wireless Video Broadcasting With Unequal Loss Protection

Digital fountain codes have emerged as a low-complexity alternative to Reed-Solomon codes for erasure correction. The applications of these codes are relevant especially in the field of wireless video, where low encoding and decoding complexity is crucial. In this paper, we introduce a new class of digital fountain codes based on a sliding-window approach applied to Raptor codes. These codes have several properties useful for video applications, and provide better performance than classical digital fountains. Then, we propose an application of sliding-window Raptor codes to wireless video broadcasting using scalable video coding. The rates of the base and enhancement layers, as well as the number of coded packets generated for each layer, are optimized so as to yield the best possible expected quality at the receiver side, and providing unequal loss protection to the different layers according to their importance. The proposed system has been validated in a UMTS broadcast scenario, showing that it improves the end-to-end quality, and is robust towards fluctuations in the packet loss rate.
Distributed Recovery of Jointly Sparse Signals Under Communication Constraints

The problem of the distributed recovery of jointly sparse signals has attracted much attention recently. Let us assume that the nodes of a network observe different sparse signals with common support; starting from linear, compressed measurements, and exploiting network communication, each node aims at reconstructing the support and the non-zero values of its observed signal. In the literature, distributed greedy algorithms have been proposed to tackle this problem, among which the most reliable ones require a large amount of transmitted data, which barely adapts to realistic network communication constraints. In this paper, we address the problem through a reweighted ℓ1 soft thresholding technique, in which the threshold is iteratively tuned based on the current estimate of the support. The proposed method adapts to constrained networks, as it requires only local communication among neighbors, and the transmitted messages are indices from a finite set. We analytically prove the convergence of the proposed algorithm and we show that it outperforms the state-of-the-art greedy methods in terms of balance between recovery accuracy and communication load.
Distributed covariance estimation for compressive signal processing

In this paper we present a novel technique for the distributed estimation of the covariance matrix of an additive colored noise process affecting Compressed Sensing (CS) measurements. The main application is in wireless sensor networks, where nodes sense signals in CS format in order to save energy in the computation and transmission stages. The proposed technique enables a variety of compressive signal processing operations to be performed at each node directly on the linear measurements, such as detection, exploiting the knowledge of the noise statistics, thereby achieving improved performance. The parametric approach we introduce promises to yield good results while keeping the communication cost low. Hence, we validate our technique by evaluating the error on the estimated covariance matrix, and by including it in a compressive detection task.
Spatially scalable compressed image sensing with hybrid transform and inter-layer prediction model

Compressive imaging is an emerging application of compressed sensing, devoted to acquisition, encoding and reconstruction of images using random projections as measurements. In this paper we propose a novel method to provide a scalable encoding of an image acquired by means of compressed sensing techniques. Two bit-streams are generated to provide two distinct quality levels: a low-resolution base layer and full-resolution enhancement layer. In the proposed method we exploit a fast preview of the image at the encoder in order to perform inter-layer prediction and encode the prediction residuals only. The proposed method successfully provides resolution and quality scalability with modest complexity and it provides gains in the quality of the reconstructed images with respect to separate encoding of the quality layers. Remarkably, we also show that the scheme can also provide significant gains with respect to a direct, non-scalable system, thus accomplishing two features at once: scalability and improved reconstruction performance.
Low-power distributed sparse recovery testbed on wireless sensor networks

Recently, distributed algorithms have been proposed for the recovery of sparse signals in networked systems, e.g. wireless sensor networks. Such algorithms allow large networks to operate autonomously without the need of a fusion center, and are very appealing for smart sensing problems employing low-power devices. They exploit local communications, where each node of the network updates its estimates of the sensed signal also based on the correlated information received from neighboring nodes. In the literature, theoretical results and numerical simulations have been presented to prove convergence of such methods to accurate estimates. Their implementation, however, raises some concerns in terms of power consumption due to iterative inter-node communications, data storage, computation capabilities, global synchronization, and faulty communications. On the other hand, despite these potential issues, practical implementations on real sensor networks have not been demonstrated yet. In this paper we fill this gap and describe a successful implementation of a class of randomized, distributed algorithms on a real low-power wireless sensor network test bed with very scarce computational capabilities. We consider a distributed compressed sensing problem and we show how to cope with the issues mentioned above. Our tests on synthetic and real signals show that distributed compressed sensing can successfully operate in a real-world environment.
Predictive coding of SAR phase history data

In this article we evaluate the use of predictive coding for compression of SAR phase history data. We first show that the data are mainly correlated along range lines. Then, we exploit this result to define a new DPCM-based compression algorithm named RDPCM-BAQ. The performance of this algorithm is compared with that of BAQ on SIR-C/X-SAR data, showing a significant improvement in signal-to-noise ratio of up to 2 dB with respect to BAQ.
Quality Assessment for Hyperspectral Imagery: Comparison Between Lossy and Near-Lossless Compression

In the field of remote sensing image compression it is often argued that traditional MSE-based fidelity metrics might not effectively describe the quality of remote sensing lossy or near-lossless compressed images. In this paper we introduce a performance evaluation framework based on both reconstruction fidelity and impact on image exploitation. Besides MSE, the framework also considers hard classification and mixed pixel classification, as well as anomaly detection. We apply this framework to evaluate and compare the quality of state-of-the-art lossy and near-lossless compression techniques applied to hyperspectral AVIRIS scenes.
Unequal processing gain for JPEG2000 image transmission in a CDMA environment

This paper investigates the error resilience capability of JPEG2000, for image transmission in a DS-CDMA environment. The processing gain is exploited to achieve a two class bitstream segmentation, able to yield a given Quality of Service (QoS) level also in the presence of severe noise conditions. Both the error resilience tools included in the standard and the possibility of improving the QoS through unequal error protection are exploited. The results show that unequal processing gain is a useful and viable tool for reliable transmission of images in a wireless environment.
Image reconstruction from partial Fourier measurements via curl constrained sparse gradient estimation

In this paper, we propose new gradient-based methods for image reconstruction from partial Fourier measurements, which are commonly used in magnetic resonance imaging (MRI) or synthetic aperture radar. Compared to classical gradient recovery methods, a key improvement is obtained by formulating the gradient recovery problem as a compressed sensing problem with the additional constraint that the curl of the gradient field must be zero. Moreover, we formulate the image recovery problem as an inverse problem on graphs. Iteratively reweighted ℓ1 recovery methods are proposed to recover these relative differences and the structure of the similarity graph. Finally, the image is recovered from the compressed Fourier measurements using least squares estimation. Numerical experiments demonstrate that the proposed approach outperforms the state-of-the-art image recovery methods.
Distributed media-aware scheduling for P2P streaming with Network Coding

We present a distributed packet scheduling scheme for pushbased Peer-to-Peer (P2P) video streaming with Network Coding (NC) over unstructured random overlays. While previous research has shown the potentials of random-push NC for P2P, little attention has been given to the problem of scheduling the packet transmissions at the network nodes. The proposed scheduling scheme exploits the knowledge of the status of the network links and nodes to maximize the number of nodes that are able to recover the media content prior to its playout deadline. Our experiments show a large performance gain with respect to random-push scheduler in terms of better media quality.