nils-herrmann/clean_scirepeval huggingface dataset:
Mixed DSP/FPGA implementation of an error-resilient image transmission system based on JPEG2000

This paper describes a demonstrator of an error-resilient image communication system over wireless packet networks, based on the novel JPEG2000 standard. In particular, the decoder implementation is addressed, which is the most critical task in terms of complexity and power consumption, in view of use on a wireless portable terminal for cellular applications. The system implementation is based on a mixed DSP/FPGA architecture, which allows us to parallelize some computational tasks, thus leading to efficient system operation.
Symmetric Distributed Arithmetic Coding of Correlated Sources

We propose a new scheme for symmetric Slepian-Wolf coding of correlated binary sources. Unlike previous designs that employ capacity-achieving channel codes, the proposed scheme is based on arithmetic codes with error correction capability. We define a time-sharing version of a distributed arithmetic coder, and a soft joint decoder. Experimental results on two sources show that, for short block length, the proposed scheme outperforms the symmetric turbo code design in (Stankovic et al., 2006).
Distributed Scheduling for Low-Delay and Loss-Resilient Media Streaming With Network Coding

Network coding (NC) has been shown to be very effective for collaborative media streaming applications. A pivotal issue in media streaming with NC lies in the packet scheduling policy at the network nodes, which affects the perceived media quality. In this paper, we address the problem of finding the packet scheduling policy that maximizes the number of media segments recovered in the network. We cast this as a distributed minimization problem and propose heuristic solutions that make the proposed framework robust to infrequent or inaccurate feedback information. Moreover, the proposed framework accounts for the properties of layered and multiple description encoded media to provide graceful quality degradation in case of packet losses or lack of upload bandwidth. Experimental results on a local testbed as well as PlanetLab suggest that our scheduling framework achieves better media quality, lower playback delay, and lower bandwidth consumption than a random-push scheme.
Traffic Prioritization of H.264/SVC Video over 802.11e Ad Hoc Wireless Networks

The H.264/SVC video codec extends the H.264/AVC standard with scalability features. In this paper we introduce a traffic prioritization algorithm suitable for the transmission of both H.264/SVC and H.264/AVC video over 802.11e ad hoc wireless networks. The proposed algorithm exploits the traffic prioritization capabilities offered by 802.11e to provide better protection to the most perceptually important parts of a video while achieving efficient network resource usage. We evaluate the algorithm by simulating video transmissions in an ad hoc network scenario. Results show that the H.264/SVC codec particularly benefits from the proposed algorithm, which enables a graceful video quality degradation in congested network conditions, as well as PSNR gains up to 2 dB with respect to the H.264/AVC codec using the same amount of network resources.
Distributed source coding of hyperspectral images

A first attempt to exploit distributed source coding (DSC) principles for the lossless compression of hyperspectral images is presented. The DSC paradigm is exploited to design a very light coder which minimizes the exploitation of the correlation between the image bands. In this way we managed to move the computational complexity from the encoder to the decoder, thus matching the needs of classical acquisition system where compression is achieved on board of the aerial platform and decoding at the ground station. Though the encoder does not explicitly exploit inter-band correlation, the achieved bit rate is about 1 bit/pixel lower than classical 2D schemes such as JPEG-LS or CALID 2D, and only about 1 b/p higher than the best performing, and much more complex, 3D schemes.
Sparse image recovery using compressed sensing over finite alphabets

In this paper we present F2 OMP, a recovery algorithm for Compressed Sensing over finite fields. Classical recovery algorithms do not exploit the fact that a signal may belong to a finite alphabet, while we show that this information can lead to more efficient reconstruction algorithms. As an application, we use the proposed algorithm to recover sparse grayscale images, showing that performing CS operation over a finite field can outperform classical recovery algorithms from visual quality, memory occupation and complexity point of view.
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images

Predictive coding is attractive for compression on board of spacecraft due to its low computational complexity, modest memory requirements, and the ability to accurately control quality on a pixel-by-pixel basis. Traditionally, predictive compression focused on the lossless and near-lossless modes of operation, where the maximum error can be bounded but the rate of the compressed image is variable. Rate control is considered a challenging problem for predictive encoders due to the dependencies between quantization and prediction in the feedback loop and the lack of a signal representation that packs the signal's energy into few coefficients. In this paper, we show that it is possible to design a rate control scheme intended for onboard implementation. In particular, we propose a general framework to select quantizers in each spatial and spectral region of an image to achieve the desired target rate while minimizing distortion. The rate control algorithm allows achieving lossy near-lossless compression and any in-between type of compression, e.g., lossy compression with a near-lossless constraint. While this framework is independent of the specific predictor used, in order to show its performance, in this paper, we tailor it to the predictor adopted by the CCSDS-123 lossless compression standard, obtaining an extension that allows performing lossless, near-lossless, and lossy compression in a single package. We show that the rate controller has excellent performance in terms of accuracy in the output rate, rate-distortion characteristics, and is extremely competitive with respect to state-of-the-art transform coding.
SparseHash: Embedding Jaccard coefficient between supports of signals

Embeddings provide compact representations of signals to be used to perform inference in a wide variety of tasks. Random projections have been extensively used to preserve Euclidean distances or inner products of high dimensional signals into low dimensional representations. Different techniques based on hashing have been used in the past to embed set similarity metrics such as the Jaccard coefficient. In this paper we show that a class of random projections based on sparse matrices can be used to preserve the Jaccard coefficient between the supports of sparse signals. Our proposed construction can be therefore used in a variety of tasks in machine learning and multimedia signal processing where the overlap between signal supports is a relevant similarity metric. We also present an application in retrieval of similar text documents where SparseHash improves over MinHash.
A resilient and low-delay P2P streaming system based on network coding with random multicast trees

Network coding is known to provide increased throughput and reduced delay for communications over networks. In this paper we propose a peer-to-peer video streaming system that exploits network coding in order to achieve low start-up delay, high streaming rate, and high resiliency to peers' dynamics. In particular, we introduce the concept of random multicast trees as overlay topology. This topology offers all benefits of tree-based overlays, notably a short start-up delay, but is much more efficient at distributing data and recovering from ungraceful peers departures. We develop a push-based streaming system that leverages network coding to efficiently distribute the information in the overlay without using buffer maps. We show performance results of the proposed system and compare it with an optimized pull systems based on Coolstreaming, showing significant improvement.
Laplace mixtures models for efficient compressed sensing with side information

In this paper, we propose a new method for the recovery of a sparse signal from few linear measurements using a reference signal as side information. Modeling the signal coefficients with a double Laplace mixture model, and assuming that the distribution of the components of the prior information differs slightly from the unknown signal, the problem is formulated as a weighted ℓ1 minimization problem. We derive sufficient conditions for perfect recovery and we show that our method is able to reduce significantly the number of measurements required for reconstruction. Numerical experiments demonstrate that the proposed approach outperforms the best algorithms for compressed sensing with prior information and is robust in imperfect scenarios.
Unified Lossy and Near-Lossless Hyperspectral Image Compression Based on JPEG 2000

We propose a compression algorithm for hyperspectral images featuring both lossy and near-lossless compression. The algorithm is based on JPEG 2000 and provides better near-lossless compression performance than 3D-CALIC. We also show that its effect on the results of selected applications is negligible and, in some cases, better than JPEG 2000.
Graded Quantization for Multiple Description Coding of Compressive Measurements

Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics.
Robust and edge-preserving video error concealment by coarse-to-fine block replenishment

In this paper we propose a novel error concealment algorithm for video transmission over wireless networks potentially subject to packet erasures. In particular, we develop a technique for the replenishment of missing macroblocks, which aims at minimizing the impact of the lost data on the resulting video with respect to the human visual system. The proposed algorithm operates three reconstruction stages at different scales, by first recovering smooth large-scale patterns, then large-scale structures, and finally local edges in the lost macroblock. Experimental results show that the proposed algorithm achieves improved visual quality of the reconstructed frames with respect to other state-of-the-art techniques, as well as better PSNR results.
A Tutorial on Image Compression for Optical Space Imaging Systems

Public policies and private initiatives share the will to explore outer space and to monitor the Earth from space sensors. Recent years have seen an increased number of space missions, while the sensors on board aircrafts or spacecrafts have also significantly improved their acquisition capabilities. Given this huge volume of remote sensing data and the detailed characteristics of the acquired images, a data compression process is in order to allow as large a transmission rate as possible.
Hyperspectral Image Compression Employing a Model of Anomalous Pixels

We propose a new lossy compression algorithm for hyperspectral images, which is based on the spectral Karhunen-Loeve transform, followed by spatial JPEG 2000, which employs a model of anomalous pixels during the compression process.  provides better rate-distortion performance, as well as improved anomaly detection performance, with respect to the state of the art.
Scale-robust compressive camera fingerprint matching with random projections

Recently, we demonstrated that random projections can provide an extremely compact representation of a camera fingerprint without significantly affecting the matching performance. In this paper, we propose a new construction that makes random projections of camera fingerprints scale-robust. The proposed method maps the compressed fingerprint of a rescaled image to the compressed fingerprint of the original image, rescaled by the same factor. In this way, fingerprints obtained from rescaled images can be directly matched in the compressed domain, which is much more efficient than existing scale-robust approaches. Experimental results on the publicly available Dresden database show that the proposed technique is robust to a wide range of scale transformations. Moreover, robustness can be further improved by providing reference scales in the database, with a small additional storage cost.
Randomized Algorithms for Distributed Nonlinear Optimization Under Sparsity Constraints

Distributed optimization in multi-agent systems under sparsity constraints has recently received a lot of attention. In this paper, we consider the in-network minimization of a continuously differentiable nonlinear function which is a combination of local agent objective functions subject to sparsity constraints on the variables. A crucial issue of in-network optimization is the handling of the communications, which may be expensive. This calls for efficient algorithms, that are able to reduce the number of required communication links and transmitted messages. To this end, we focus on asynchronous and randomized distributed techniques. Based on consensus techniques and iterative hard thresholding methods, we propose three methods that attempt to minimize the given function, promoting sparsity of the solution: asynchronous hard thresholding (AHT), broadcast hard thresholding (BHT), and gossip hard thresholding (GHT). Although similar in many aspects, it is difficult to obtain a unified analysis for the proposed algorithms. Specifically, we theoretically prove the convergence and characterize the limit points of AHT in regular networks under some proper assumptions on the functions to be minimized. For BHT and GHT, instead, we characterize the fixed points of the maps that rule their dynamics in terms of stationary points of original problem. Finally, we illustrate the implementation of our techniques in compressed sensing and present several numerical results on performance and number of transmissions required for convergence.
Low-complexity video compression for wireless sensor networks

We study the problem of compression of videosurveillance sequences collected by a wireless sensor network. In particular, we propose a low-complexity coding framework based on change detection and JPEG-like compression of regions of interest, along with a suitable low-complexity change detection algorithm. We show that on typical videosurveillance sequences the performance of the proposed compression algorithm is similar to that of MPEG-2, at a much less computational cost. Energy profiling results on a TMS320VC5204 board validate the coder design for the proposed application.
Wavelet-based compression of SAR raw data

In this paper we compare two compression methods for SAR raw data, based on the discrete wavelet transform (DWT). In the former, the data are subject to blockwise normalization prior to being transformed by means of the DWT processor; then, an optimal rate allocation is performed for each subband. The latter employs the well known JPEG 2000 to perform the DWT and the subsequent quantization, rate allocation and coding steps; both the cases of normalized and non normalized data are considered. The performance of the algorithms have been tested on SIR-C/X-SAR data, and FBAQ is employed as the term of comparison for both quality and compression ratio. The obtained results show that the if the samples are normalized, the performance of the DWT-based algorithms is more predictable, and less subject to statistical fluctuations related to the characteristics of the input data.
Low-complexity predictive lossy compression of hyperspectral and ultraspectral images

Lossy compression of hyperspectral and ultraspectral images is traditionally performed using 3D transform coding. This approach yields good performance, but its complexity and memory requirements are unsuitable for onboard compression. In this paper we propose a low-complexity lossy compression scheme based on prediction, uniform-threshold quantization, and rate-distortion optimization. Its performance is competitive with that of state-of-the-art 3D transform coding schemes, but the complexity is immensely lower. The algorithm is able to limit the scope of errors, and is amenable to parallel implementation, making it suitable for onboard compression at high throughputs.
Large-Scale Image Retrieval Based on Compressed Camera Identification

Retrieving pictures from large collections according to a specific criterion is an increasingly relevant task. An important , but so far overlooked, such criterion is the retrieval of pictures acquired by a specific camera. Instead of relying on metadata , which can be absent or easily manipulated, a forensic tool is exploited, namely the photo response non-uniformity (PRNU) of the camera sensor. Recent works showed that random projections can be used to significantly compress the PRNU, enabling operation on very large scales, previously impossible due to the size of the PRNU and to the complexity of the matching operations. In this paper, we propose efficient techniques for management and retrieval of images employing the PRNU, and test them on a database of 1174 cameras and half a million pictures downloaded from the Internet.
Complexity-adaptive Random Network Coding for Peer-to-Peer video streaming

We present a novel architecture for complexity-adaptive Random Network Coding (RNC) and its application to Peer-to-Peer (P2P) video streaming. Network coding enables the design of simple and effective P2P video distribution systems, however it relies on computationally intensive packet coding operations that may exceed the computational capabilities of power constrained devices. It is hence desirable that the complexity of network coding can be adjusted at every node according to its computational capabilities, so that different classes of nodes can coexist in the network. To this end, we model the computational complexity of network coding as the sum of a packet decoding cost, which is centrally minimized at the encoder, and a packet recoding cost, which is locally controlled by each node. Efficient network coding is achieved exploiting the packet decoding process as a packet pre-recoding stage, hence increasing the chance that transmitted packets are innovative without increasing the recoding cost. Experiments in a P2P video streaming framework show that the proposed design enables the nodes of the network to operate at a wide range of computational complexity levels, while a higher number of low complexity nodes are able to join the network and experience high-quality video.
Distributed support detection of jointly sparse signals

In this paper, we address the problem of distributed support detection of multiple sparse signals with common support. Specifically, signals are acquired by the individual nodes of a network according to the so-called Joint Sparsity Model 2 (JSM-2). By leveraging on this model, we propose a distributed scheme for in-network signal recovery, i.e. not requiring data gathering and processing at a fusion center, based on distributed iterative thresholding and consensus strategies. For the proposed scheme, whose convergence properties we rigorously prove, no a priori knowledge on the non-zero number of entries in the signal vector is required.
Minimum-Impact-on-Classifier (MIC) watermarking for protection of remote sensing imagery

The application of digital watermarking to remote sensing images requires a careful quality assessment in order to understand how the data quality is affected by the watermark. We propose a watermarking approach that minimizes the watermark impact on image classification, based on the idea of modulating the insertion coefficient in each channel so as to preserve to a larger extent the channels which classification is most sensitive to; we also propose a simplified procedure for estimating cluster displacement due to watermarking, leading to a low-complexity insertion approach. Experimental results on Landsat 7 ETM+ and IKONOS images show that the proposed MIC approach is able to significantly reduce classification errors, and to keep them within the intrinsic classification error
VLSI Architecture for Low-Complexity Motion Estimation in H.264 Multiview Video Coding

This paper presents a VLSI architecture for a low complexity motion estimation algorithm, referred to as Slim264, for multiview video coding extension of H.264. Algorithmic modifications are introduced to obtain a fully parallel computational structure able to meet the throughput requirements of high resolution and high frame rate videos. High parallelism is achieved by predicting small blocks, i.e. 4x4 pixel blocks, in parallel and then adding them up in order to get Sum of Absolute Differences (SADs) of large block sizes. The predictor is able to support high resolution videos i.e. 1080p. The modified algorithm shows promising PSNR results with respect to full search algorithm. The predictor is synthesized with a clock frequency of 200 MHz, occupying an area of 0.49 mm2, on 90-nm Standard Cell ASIC technology.
On the fly estimation of the sparsity degree in Compressed Sensing using sparse sensing matrices

In this paper, we propose a mathematical model to estimate the sparsity degree k of exactly k-sparse signals acquired through Compressed Sensing (CS). Our method does not need to recover the signal to estimate its sparsity, and is based on the use of sparse sensing matrices. We exploit this model to propose a CS acquisition system where the number of measurements is calculated on-the-fly depending on the estimated signal sparsity. Experimental results on block-based CS acquisition of black and white images show that the proposed adaptive technique outperforms classical CS acquisition methods where the number of measurements is set a priori.
On-board selection of relevant images: an application to linear feature recognition

We propose an on-board selection scheme for aerial and space images, based on linear feature detection in a feature hyperspace. The detection task is performed by means of the Radon transform (RT) and the wavelet transform; a fast algorithm for the RT computation is described, and counteractions against the discretization errors are proposed. A new, wavelet-based algorithm is introduced, which performs a fine analysis of the waveforms of the RT peaks, yielding a possibly error-free detection in images corrupted by a high level of noise. A technique, based on the feature hyperspace, is proposed, able to significantly exploit all the available pieces of information on these peaks. Results of the tests on synthetic and real images are reported, which show that this method achieves satisfactory results, making the detection task highly reliable in the presence of both noise and clutter.