nils-herrmann/clean_scirepeval huggingface dataset:
Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC

We propose a new lossless and near-lossless compression algorithm for hyperspectral images based on context-based adaptive lossless image coding (CALIC). Specifically, we propose a novel multiband spectral predictor, along with optimized model parameters and optimization thresholds. The resulting algorithm is suitable for compression of data in band-interleaved-by-line format; its performance evaluation on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data shows that it outperforms 3-D-CALIC as well as other state-of-the-art compression algorithms.
Band Codes for Energy-Efficient Network Coding With Application to P2P Mobile Streaming

A key problem in network coding (NC) lies in the complexity and energy consumption associated with the packet decoding processes, which hinder its application in mobile environments. Controlling and hence limiting such factors has always been an important but elusive research goal, since the packet degree distribution, which is the main factor driving the complexity, is altered in a non-deterministic way by the random recombinations at the network nodes. In this paper we tackle this problem with a new approach and propose Band Codes (BC), a novel class of network codes specifically designed to preserve the packet degree distribution during packet encoding, recombination and decoding. BC are random codes over GF(2) that exhibit low decoding complexity, feature limited and controlled degree distribution by construction, and hence allow to effectively apply NC even in energy-constrained scenarios. In particular, in this paper we motivate and describe our new design and provide a thorough analysis of its performance. We provide numerical simulations of the BC performance in order to validate the analysis and assess the overhead of BC with respect to a conventional random NC scheme. Moreover, experiment in a real-world application, namely peer-to-peer mobile media streaming using a random-push protocol, show that BC reduce the decoding complexity by a factor of two with negligible increase of the encoding overhead, paving the way for the application of NC to power-constrained devices.
Signal sparsity estimation from compressive noisy projections via γ-sparsified random matrices

In this paper, we propose a method for estimating the sparsity of a signal from its noisy linear projections without recovering it. The method exploits the property that linear projections acquired using a sparse sensing matrix are distributed according to a mixture distribution whose parameters depend on the signal sparsity. Due to the complexity of the exact mixture model, we introduce an approximate two-component Gaussian mixture model whose parameters can be estimated via expectation-maximization techniques. We demonstrate that the above model is accurate in the large system limit for a proper choice of the sensing matrix sparsifying parameter. Moreover, experimental results demonstrate that the method is robust under different signal-to-noise ratios and outperforms existing sparsity estimation techniques.
Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2-D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this paper, we overview recent graph spectral techniques in GSP specifically for image/video processing. The topics covered include image compression, image restoration, image filtering, and image segmentation.
Compressive Estimation and Imaging Based on Autoregressive Models

Compressed sensing (CS) is a fast and efficient way to obtain compact signal representations. Oftentimes, one wishes to extract some information from the available compressed signal. Since CS signal recovery is typically expensive from a computational point of view, it is inconvenient to first recover the signal and then extract the information. A much more effective approach consists in estimating the information directly from the signal's linear measurements. In this paper, we propose a novel framework for compressive estimation of autoregressive (AR) process parameters based on ad-hoc sensing matrix construction. More in detail, we introduce a compressive least square estimator for AR(p) parameters and a specific AR(1) compressive Bayesian estimator. We exploit the proposed techniques to address two important practical problems. The first is compressive covariance estimation for Toeplitz structured covariance matrices where we tackle the problem with a novel parametric approach based on the estimated AR parameters. The second is a block-based compressive imaging system, where we introduce an algorithm that adaptively calculates the number of measurements to be acquired for each block from a set of initial measurements based on its degree of compressibility. We show that the proposed techniques outperform the state-of-the-art methods for these two problems.
Conditional Access to H.264/AVC Video by Means of Redundant Slices

In this paper a novel conditional access scheme for the distribution of H.264/AVC video is presented. The algorithm permits to cypher the full quality video, while guaranteeing free access to a reduced quality video that can be used as preview. The scheme is based on the novel coding options, introduced in the H.264 standard, and therefore is fully compliant with the standard syntax. The secured video stream exhibits a very small rate overhead and requires limited supplementary computational cost at coding time.
Parallel H.264/AVC Fast Rate-Distortion Optimized Motion Estimation by Using a Graphics Processing Unit and Dedicated Hardware

Heterogeneous systems on a single chip composed of a central processing unit, graphics processing unit (GPU), and field-programmable gate array (FPGA) are expected to emerge in the near future. In this context, the system on chip can be dynamically adapted to employ different architectures for execution of data-intensive applications. Motion estimation (ME) is one such task that can be accelerated using FPGA and GPU for high-performance H.264/Advanced Video Coding encoder implementation. This paper presents an inherent parallel low-complexity rate-distortion (RD) optimized fast ME algorithm well suited for parallel implementations, eliminating various data dependencies caused by a reliance on spatial predictions. In addition, this paper provides details of the GPU and FPGA implementations of the parallel algorithm by using OpenCL and Very High Speed Integrated Circuits (VHSIC) Hardware Descriptive Language (VHDL), respectively, and presents a practical performance comparison between the two implementations. The experimental results show that the proposed scheme achieves significant speedup on GPU and FPGA, and has comparable RD performance with respect to sequential fast ME algorithm.
Compressed Fingerprint Matching and Camera Identification via Random Projections

Sensor imperfections in the form of photoresponse nonuniformity (PRNU) patterns are a well-established fingerprinting technique to link pictures to the camera sensors that acquired them. The noise-like characteristics of the PRNU pattern make it a difficult object to compress, thus hindering many interesting applications that would require storage of a large number of fingerprints or transmission over a bandlimited channel for real-time camera matching. In this paper, we propose to use real-valued or binary random projections to effectively compress the fingerprints at a small cost in terms of matching accuracy. The performance of randomly projected fingerprints is analyzed from a theoretical standpoint and experimentally verified on databases of real photographs. Practical issues concerning the complexity of implementing random projections are also addressed using circulant matrices.
Steerable Discrete Cosine Transform

Block-based separable transforms tend to be inefficient when blocks contain arbitrarily shaped discontinuities. For this reason, transforms incorporating directional information are an appealing alternative. In this paper, we propose a new approach to this problem, designing a new transform that can be steered in any chosen direction and that is defined in a rigorous mathematical way. This new steerable DCT allows to rotate in a flexible way pairs of basis vectors, enabling precise matching of directionality in each image block, and thereby achieving improved coding efficiency. We tested the proposed transform on several images and the results show that it provides a significant performance gain compared to the DCT. Moreover, the mathematical framework on which the steerable DCT is based allows to generalize the transform to more complex steering patterns than a single pure rotation.
Enhancing whole-frame error concealment with an intra motion vector estimator in H.264/AVC

The novel H.264/AVC video coding specification provides a significant improvement in terms of coding efficiency compared to previous standards. In certain streaming scenarios, the problem of whole-frame loss concealment can arise when using H.264/AVC. Two different algorithms have been proposed to conceal a lost image, based on the optical flow concept. In a real streaming scenario, an encoder usually introduces some kind of intra refresh policy that can insert trouble concealment algorithms leaving gaps in the motion vector field used for reference. We discuss the advantages of introducing a motion vector estimator for such intra coded regions.
Reliable JPEG 2000 wireless imaging by means of error-correcting MQ coder

A new error resilience tool is proposed for robust JPEG 2000 imaging over noisy channels. In particular, a modified encoder, based on an MQ arithmetic coder with forbidden symbol, is introduced, along with a maximum likelihood error-correcting MQ decoder. The proposed technique features error detection, error concealment and error correction capability, thus adding new useful functionalities to JPEG 2000. Experimental results show that this technique largely outperforms the standard JPEG 2000 error resilience tools for error concealment and hard/soft channel decoding.
Comparison of rate allocation strategies for H.264 video transmission over wireless lossy correlated networks

In this paper we study the problem of transmitting coded video over a UMTS network. We first discuss the statistical characteristics of packet losses in case of RTP/UDP/IP wireless video communication. Then, we propose a new rate allocation algorithm for H.264 video, based on a Gilbert-Elliot model of the packet losses. We compare the proposed algorithm with several other allocation strategies, showing that it achieves satisfactory performance in terms of PSNR. Moreover, we outline the limits of the employed distortion model and outline possible solutions to overcome them.
Finite precision wavelets for image coding: lossy and lossless compression performance evaluation

This paper investigates the robustness of the wavelet transform, implemented by means of the lifting scheme (LS), with respect to numerical errors in the representation and calculation of transformed coefficients. The study promises to offer important contributions for the understanding of the LS capabilities when specific implementations are considered. This is a topic of growing interest as the new standard JPEG 2000, based on the wavelet transform, is being finalized. Taking into account the effect of finite precision representation can drive both software and hardware implementations with optimized trade off between complexity and performance; moreover the robustness to numerical errors could be an important feature, not usually considered, in order to select the best wavelet filters.
User Authentication via PRNU-Based Physical Unclonable Functions

Multifactor user authentication systems enhance security by augmenting passwords with the verification of additional pieces of information such as the possession of a particular device. This paper presents an innovative user authentication scheme that verifies the possession of one’s smartphone by uniquely identifying its camera. High-frequency components of the photo-response nonuniformity of the optical sensor are extracted from raw images and used as a weak physical unclonable function. A novel scheme for efficient transmission and server-side verification is also designed based on adaptive random projections and on an innovative fuzzy extractor using polar codes. The security of the system is thoroughly analyzed under different attack scenarios both theoretically and experimentally.
Selective encryption of JPEG 2000 images by means of randomized arithmetic coding

We describe 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 propose a randomized arithmetic coding paradigm, which achieves encryption by randomly swapping the intervals of the least and most probable symbols in arithmetic coding; moreover, we describe an implementation tailored to the JPEG 2000 standard. The proposed approach turns out to be robust towards attempts to discover the key, and allows very flexible procedures for insertion of redundancy at the codeblock level, allowing to perform total and selective encryption, conditional access, and encryption of regions of interest.
Steerable Discrete Fourier Transform

Directional transforms have recently raised a lot of interest thanks to their numerous applications in signal compression and analysis. In this letter, we introduce a generalization of the discrete Fourier transform (DFT), called steerable DFT (SDFT). Since the DFT is used in numerous fields, it may be of interest in a wide range of applications. Moreover, we also show that the SDFT is highly related to other well-known transforms, such as the Fourier sine and cosine transforms and the Hilbert transforms.
Highly-Parallel GPU Architecture for Lossy Hyperspectral Image Compression

Graphics Processing Units (GPU) are becoming a widespread tool for general-purpose scientific computing, and are attracting interest for future onboard satellite image processing payloads due to their ability to perform massively parallel computations. This paper describes the GPU implementation of an algorithm for onboard lossy hyperspectral image compression, and proposes an architecture that allows to accelerate the compression task by parallelizing it on the GPU. The selected algorithm was amenable to parallel computation owing to its block-based operation, and has been optimized here to facilitate GPU implementation incurring a negligible overhead with respect to the original single-threaded version. In particular, a parallelization strategy has been designed for both the compressor and the corresponding decompressor, which are implemented on a GPU using Nvidia's CUDA parallel architecture. Experimental results on several hyperspectral images with different spatial and spectral dimensions are presented, showing significant speed-ups with respect to a single-threaded CPU implementation. These results highlight the significant benefits of GPUs for onboard image processing, and particularly image compression, demonstrating the potential of GPUs as a future hardware platform for very high data rate instruments.
A New Low Complexity KLT for Lossy Hyperspectral Data Compression

Transform-based lossy compression has a huge po- tential for hyperspectral data reduction. In this paper we propose a lossy compression scheme for hyperspectral data based on a new low-complexity version of the Karhunen-Lo` eve transform, in which complexity and performance can be balanced in a scalable way, allowing one to choose the best trade off that better matches a specific application. Moreover, we integrate this transform in the framework of Part 2 of the JPEG 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. Hyperspectral imaging amounts to collecting the energy reflected or emitted by ground targets at a typically very high number of wavelengths, resulting in a data cube consisting of tens to hundreds of bands. These data have become increas- ingly popular, since they enable plenty of new applications, in- cluding detection and identification of surface and atmospheric constituents present, analysis of soil type, agriculture and forest monitoring, environmental studies and military surveil- lance. The data are usually acquired by a remote platform (a satellite or an aircraft), and then downlinked to a ground sta- tion. Due to the huge size of the datasets, compression is nec- essary to match the available transmission bandwidth. In the past, scientific data have been almost exclusively compressed by means of lossless methods, in order to preserve their full quality. However, more recently, there has been an increasing interest in their lossy compression. Many of these techniques are based on decorrelating transforms, in order to exploit spatial and inter-band (i.e., spectral) correlation, followed by a quantization stage and an entropy coder. Examples include the
Subspace-sparsifying steerable discrete cosine transform from graph fourier transform

In image compression, block-based transforms tend to be inefficient when blocks contain arbitrarily shaped discontinuities. For this reason, transforms incorporating directional information are an appealing alternative. Starting from the graph Fourier transform, in this paper we present a new transform, called Subspace-Sparsifying Steer-able DCT, that can be obtained by rotating the basis vectors of the 2D DCT using a set of angles that best matches the block to be encoded. In particular, this new transform chooses the set of angles providing the sparsest image representation in the transform domain, yielding a matrix of transform coefficients that is triangular. In this way, it nearly halves the number of coefficients that need to be transmitted, obtaining a significant coding gain in comparison to the classical DCT.
A novel low complexity and efficient progressive quadtree wavelet encoder

In this paper the progressive quadtree wavelet (PQW) codec for lossy image compression is presented. PQW is based on the well known quadtree technique, applied in the wavelet transform domain. The proposed algorithm is designed with the aim of achieving low complexity and reduced memory requirements, keeping in mind applications such as Internet browsing on wireless (e.g. UMTS) channels. Besides its lower complexity, PQW provides the same subjective performance as the popular SPIHT codec while, in terms of the peak signal-to-noise ratio, it exhibits a slight performance loss of about 0.5 dB.
On-board lossless compression of solar corona images

The compression algorithm developed for METIS arises from the standard CCSDS 123.0-r-1 , a lossless data compressor suitable for multispectral and hyperspectral imagers and sounders. This paper presents an extension of the standard to handle lossless compression specifically tailored to solar corona images. The main contributions of this paper are the following: the adaptation to the radial geometry of solar corona images through a remapping process called “radialization” and the ability to exploit three-dimensional image compression of the standard through the compression of a multi-temporal data cube composed by successive acquisitions. The development of the algorithm took care of many aspects based on the geometry and the nature of the expected acquisitions; this adaptation process leads to a significant increase in compression performance through routines resulting very light from a computational point of view, which is a key aspect for on-board applications.
Distributed scheduling for scalable P2P video streaming with network coding

Previous research has shown the benefits of random-push Network Coding (NC) for P2P video streaming. On the other hand, scalable video coding provides graceful quality adaptation to heterogeneous network conditions. Nevertheless, packet scheduling for scalable media streaming with P2P NC is still a largely unexplored problem. Our ongoing research aims at designing a packet scheduling scheme that maximizes the quality of the video with minimal coordination among peers. In this work, we provide a preliminary description of our scheduling scheme and preliminary performance measurements.
Distributed Arithmetic Coding for the Slepian–Wolf Problem

Distributed source coding schemes are typically based on the use of channels codes as source codes. In this paper we propose a new paradigm, named ldquodistributed arithmetic coding,rdquo which extends arithmetic codes to the distributed case employing sequential decoding aided by the side information. In particular, we introduce a distributed binary arithmetic coder for the Slepian-Wolf coding problem, along with a joint decoder. The proposed scheme can be applied to two sources in both the asymmetric mode, wherein one source acts as side information, and the symmetric mode, wherein both sources are coded with ambiguity, at any combination of achievable rates. Distributed arithmetic coding provides several advantages over existing Slepian-Wolf coders, especially good performance at small block lengths, and the ability to incorporate arbitrary source models in the encoding process, e.g., context-based statistical models, in much the same way as a classical arithmetic coder. We have compared the performance of distributed arithmetic coding with turbo codes and low-density parity-check codes, and found that the proposed approach is very competitive.
PISTA: Parallel Iterative Soft Thresholding algorithm for sparse image recovery

We present PISTA, a GPU-accelerated Iterative Soft Thresholding (IST) algorithm for sparse image recovery in Compressive Sensing applications. As the time required to recover an image increases with the number of pixels, GPU-acceleration enables to recover even large images in reasonable time. With respect to equivalent methods, IST-like algorithms have lower computational complexity per-iteration and lower memory requirements, plus the operations are inherently suitable for parallelization. Our experiments show that our algorithm enables a significant reduction in the time required to recover an image even over a highly-optimized CPU-only reference.
Error resilient MQ coder and map JPEG 2000 decoding

In this paper a novel error resilient MQ coder for reliable JPEG 2000 image delivery is designed. The proposed coder uses a forbidden symbol in order to force a given amount of redundancy in the codestream. At the decoder side, the presence of the forbidden symbol allows for powerful error correction. Moreover the added redundancy can be easily controlled and the proposed coder is kept backward compatible with MQ. In this work excellent improvements in the case of image transmission across both BSC and AWGN channels are obtained by means of a maximum a posteriori estimation technique.