nils-herrmann/clean_scirepeval huggingface dataset:
Constant SNR, Rate Control, and Entropy Coding for Predictive Lossy Hyperspectral Image Compression

Predictive lossy compression has been shown to represent a very flexible framework for lossless and lossy onboard compression of multispectral and hyperspectral images with quality and rate control. In this paper, we improve predictive lossy compression in several ways, using a standard issued by the Consultative Committee on Space Data Systems, namely CCSDS-123, as an example of application. First, exploiting the flexibility in the error control process, we propose a constant-signal-to-noise-ratio algorithm that bounds the maximum relative error between each pixel of the reconstructed image and the corresponding pixel of the original image. This is very useful to avoid low-energy areas of the image being affected by large errors. Second, we propose a new rate control algorithm that has very low complexity and provides performance equal to or better than existing work. Third, we investigate several entropy coding schemes that can speed up the hardware implementation of the algorithm and, at the same time, improve coding efficiency. These advances make predictive lossy compression an extremely appealing framework for onboard systems due to its simplicity, flexibility, and coding efficiency.
Joint despeckling and edge detection of SAR images based on the Mumford-Shah functional

In this paper, we propose a joint despeckling and edge detection algorithm based on the Mumford-Shah functional, which accomplishes the image filtering and segmentation as a result of an analytical variational problem. This approach turns out to be well suited to jointly despeckle and segment SAR image data; the experimental results demonstrate that the proposed technique yields high quality despeckling without impairing critical image features and with the additional advantage to provide a detailed edge map
Joint estimation of the number and the frequencies of multiple sinusoids from very noisy data

We address the problem of the joint estimation of the number and frequencies of multiple sinusoids. Extremely severe noise conditions are dealt with; consequently, an approximation of the MLE must be employed. A closed loop detection and estimation system is studded, where the statistical information about the unknown frequencies is progressively refined using the results of the estimation algorithm, in such a way that the detection algorithm can exploit improved a priori information. Simulations have shown that this scheme achieves encouraging performance as for robustness towards noise.
Multiband Lossless Compression of Hyperspectral Images

Hyperspectral images exhibit significant spectral correlation, whose exploitation is crucial for compression. In this paper, we investigate the problem of predicting a given band of a hyperspectral image using more than one previous band. We present an information-theoretic analysis based on the concept of conditional entropy, which is used to assess the available amount of correlation and the potential compression gain. Then, we propose a new lossless compression algorithm that employs a Kalman filter in the prediction stage. Simulation results are presented on Airborne Visible Infrared Imaging Spectrometer, Hyperspectral Digital Imagery Collection Experiment, and Hyperspectral Mapper scenes, showing competitive performance with other state-of-the-art compression algorithms.
Integrated compression and linear feature detection in the wavelet domain

In many Earth observation missions, a large amount of data are collected by the on-board sensors, and must be transmitted to ground through a channel with limited capacity; in this case, besides lossy compression, one often has to select a subset of the original images for ground transmission. It is then desirable that the discarded images are those with a minor information content. In this paper we propose an algorithm for on-board image selection, which is fully integrated within a wavelet-based image compression scheme. The algorithm selects images possibly containing straight patterns, and uses the Hough transform, performed in the wavelet domain, for the detection task. It is shown that this method achieves a notable complexity reduction at the expense of a limited performance degradation, strongly increasing the sustainable real-time data throughput.
Steerable Discrete Cosine Transform

In image compression, classical block-based separable transforms tend to be inefficient when image 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, namely a discrete cosine transform (DCT) that can be steered in any chosen direction. Such transform, called steerable DCT (SDCT), allows to rotate in a flexible way pairs of basis vectors, and enables precise matching of directionality in each image block, achieving improved coding efficiency. The optimal rotation angles for SDCT can be represented as solution of a suitable rate-distortion (RD) problem. We propose iterative methods to search such solution, and we develop a fully fledged image encoder to practically compare our techniques with other competing transforms. Analytical and numerical results prove that SDCT outperforms both DCT and state-of-the-art directional transforms.
A syntax-preserving error resilience tool for JPEG 2000 based on error correcting arithmetic coding

JPEG 2000 is the novel ISO standard for image and video coding. Besides its improved coding efficiency, it also provides a few error resilience tools in order to limit the effect of errors in the codestream, which can occur when the compressed image or video data are transmitted over an error-prone channel, as typically occurs in wireless communication scenarios. However, for very harsh channels, these tools often do not provide an adequate degree of error protection. In this paper, we propose a novel error-resilience tool for JPEG 2000, based on the concept of ternary arithmetic coders employing a forbidden symbol. Such coders introduce a controlled degree of redundancy during the encoding process, which can be exploited at the decoder side in order to detect and correct errors. We propose a maximum likelihood and a maximum a posteriori context-based decoder, specifically tailored to the JPEG 2000 arithmetic coder, which are able to carry out both hard and soft decoding of a corrupted codestream. The proposed decoder extends the JPEG 2000 capabilities in error-prone scenarios, without violating the standard syntax. Extensive simulations on video sequences show that the proposed decoders largely outperform the standard in terms of PSNR and visual quality.
Fast and Lightweight Rate Control for Onboard Predictive Coding of Hyperspectral Images

Predictive coding is attractive for compression of hyperspectral images onboard of spacecrafts in light of the excellent rate-distortion performance and low complexity of recent schemes. In this letter, we propose a rate control algorithm and integrate it in a lossy extension to the CCSDS-123 lossless compression recommendation. The proposed rate algorithm overhauls our previous scheme by being orders of magnitude faster and simpler to implement, while still providing the same accuracy in terms of output rate and comparable or better image quality.
Integer wavelet packets and their application to a lossy compression system for SAR images

In this paper the integer wavelet transform (IWT) is described and compared with the classical real-valued discrete wavelet transform (DWT); it is shown that, with a simple and computationally inexpensive modification of the algorithm, the performance of the IWT can be raised to that of the DWT, so overcoming a limitation which is reported in the literature. Then, a new application of the lifting scheme to the generation of integer wavelet packets (IWP) is presented. IWP yields a representation which can be lossless, as it maps an integer-valued sequence onto integer-valued coefficients in the transformed domain; moreover, it allows for the selection of an adaptive representation, which can match the variable characteristics of SAR images better than the DWT. The proposed image compression scheme is based on the classical Coifman and Wickerhauser (1992) algorithm for best basis selection, applied to the IWP tree. Extensive simulations have proven that the proposed algorithm yields good performance for both generic and SAR images.
A parallel compressive imaging architecture for one-shot acquisition

A limitation of many compressive imaging architectures lies in the sequential nature of the sensing process, which leads to long sensing times. In this paper we present a novel architecture that uses fewer detectors than the number of reconstructed pixels and is able to acquire the image in a single acquisition. This paves the way for the development of video architectures that acquire several frames per second. We specifically address the diffraction problem, showing that deconvolution normally used to recover diffraction blur can be replaced by convolution of the sensing matrix, and how measurements of a 0/1 physical sensing matrix can be converted to -1/1 compressive sensing matrix without any extra acquisitions. Simulations of our architecture show that the image quality is comparable to that of a classic Compressive Imaging camera, whereas the proposed architecture avoids long acquisition times due to sequential sensing. This one-shot procedure also allows to employ a fixed sensing matrix instead of a complex device such as a Digital Micro Mirror array or Spatial Light Modulator. It also enables imaging at bandwidths where these are not efficient.
Distributed ADMM for in-network reconstruction of sparse signals with innovations

In this paper, we address the problem of in-network reconstruction of correlated sparse signals. Specifically, we adopt a JSM-1 model by which the signals to be reconstructed are the sum of a common sparse term and an individual sparse term (or innovation). We tackle the problem using an Alternating Direction Method of Multipliers approach, which is prone to be distributed. We also propose a version that requires to exchange only binary messages among neighboring nodes. Performance of the different methods is shown to be satisfactory.
Robust video transmission over error-prone channels via error correcting arithmetic codes

We propose arithmetic codes (ACs) with a forbidden symbol as a joint entropy coding and error protection tool for robust video transmission over error-prone channels. In particular, we introduce a MAP decoder of ACs, and apply it to the transmission of 3-D SPIHT coded video and RTP/UDP/IP H.264 video with data partitioning. Experimental results show that the proposed system exhibits several advantages with respect to other schemes, and outperforms rate-compatible punctured convolutional (RCPC) codes in the applications considered.
Curl-Constrained Gradient Estimation for Image Recovery From Highly Incomplete Spectral Data

In this paper, we introduce new gradient-based methods for image recovery from a small collection of spectral coefficients of the Fourier transform, which is of particular interest for several scanning technologies, such as magnetic resonance imaging. Since gradients of a medical image are much more sparse or compressible than the corresponding image, classical  $\ell _{1}$ -minimization methods have been used to recover these relative differences. The image values can then be obtained by integration algorithms imposing boundary constraints. Compared with classical gradient recovery methods, we propose two new techniques that improve reconstruction. First, we cast the gradient recovery problem as a compressed sensing problem taking into account that the curl of the gradient field should be zero. Second, inspired by the emerging field of signal processing on graphs, we formulate the gradient recovery problem as an inverse problem on graphs. Iteratively reweighted  $\ell _{1}$  recovery methods are proposed to recover these relative differences and the structure of the similarity graph. Once the gradient field is estimated, the image is recovered from the compressed Fourier measurements using least squares estimation. Numerical experiments show that the proposed approach outperforms the state-of-the-art image recovery methods.
Feedback-driven network coding for cooperative video streaming

In this work, we propose a feedback scheme to drive the packet recombination process at the network nodes in a collaborative Network Coding (NC) scenario. Our scheme addresses the issue of determining which symbols are more helpful at the receivers to recover the message and how to accordingly recombine the received packets at the intermediate nodes where the original symbols are not available. We experimentally demonstrate that our scheme increases the coding efficiency and reduces the computational complexity at the decoder in a video communication scenario without using explicit feedback messages.
Smoothness-constrained image recovery from block-based random projections

In this paper we address the problem of visual quality of images reconstructed from block-wise random projections. Independent reconstruction of the blocks can severely affect visual quality, by displaying artifacts along block borders. We propose a method to enforce smoothness across block borders by modifying the sensing and reconstruction process so as to employ partially overlapping blocks. The proposed algorithm accomplishes this by computing a fast preview from the blocks, whose purpose is twofold. On one hand, it allows to enforce a set of constraints to drive the reconstruction algorithm towards a smooth solution, imposing the similarity of block borders. On the other hand, the preview is used as a predictor of the entire block, allowing to recover the prediction error, only. The quality improvement over the result of independent reconstruction can be easily assessed both visually and in terms of PSNR and SSIM index.
Improved low-complexity intraband lossless compression of hyperspectral images by means of Slepian-Wolf coding

In remote sensing systems, on-board data compression is a crucial task that has to be carried out with limited computational resources. In this paper we propose a novel lossless compression scheme for multispectral and hyperspectral images, which combines low encoding complexity and high-performance. The encoder is based on distributed source coding concepts, and employs Slepian-Wolf coding of the bitplanes of the CALIC prediction errors to achieve improved performance. Experimental results on AVIRIS data show that the proposed scheme exhibits performance similar to CALIC, and significantly better than JPEG 2000.
Context-Based Distributed Wavelet Video Coding

In this paper a novel scalable video coder, based on the principle of distributed source coding with side information is proposed. Coding scalability is achieved by means of bitplane coding in the wavelet domain. The distributed coding paradigm is applied to encode the wavelet coefficients of a given frame, by considering those of the previous frame as side information. LDPC syndrome encoding with proper context modeling of the frame correlation allowed us to significantly outperform intra coding obtained with JPEG 2000. Moreover, the proposed approach permits to perform motion compensation at the decoder side, thus opening a new perspective in the field of scalable video coding
Transform Coding Techniques for Lossy Hyperspectral Data Compression

Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3-D transform to be used for compression. In this paper, we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. First, we select a set of 3-D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen-Loegraveve transform (KLT), and evaluate the coding efficiency of these combinations. Second, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Third, we integrate this, as well as other existing transforms, in the framework of Part 2 of the Joint Photographic Experts Group (JPEG) 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multiclass hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications
An edge and texture preserving algorithm for video error concealment

We present a novel error concealment algorithm for block-based video transmission over error-prone networks. We develop a spatial error concealment technique, which combines edge-preserving interpolation and texture analysis and synthesis, providing a reconstruction of lost macroblocks optimized for visual perception. In particular, the algorithm recovers image edges by coarse-to-fine MAP estimation with a Markov random field prior, and replenishes lost textured areas with a texture synthesized from neighboring macroblocks. Experimental results show that texture synthesis allows achieving improved visual quality of the reconstructed area with respect to other state-of-the-art spatial concealment techniques.
Progressive 3-D coding of hyperspectral images based on JPEG 2000

In this letter we propose a new technique for progressive coding of hyperspectral data. Specifically, we employ a hybrid three-dimensional wavelet transform for spectral and spatial decorrelation in the framework of Part 2 of the JPEG 2000 standard. Both onboard and on-the-ground compression are addressed. The resulting technique is compliant with the JPEG 2000 family of standards and provides competitive performance with respect to state-of-the-art techniques.
Band Codes: Controlled Complexity Network Coding for Peer-to-Peer Video Streaming

We present Band Codes (BC), a novel class of rate less codes that makes possible to control the computational complexity of Network Coding (NC). NC increases throughput of the networks via packet recombinations at the network nodes. In a NC scenario based on rate less codes, the recombinations at the nodes alter the packet degree distribution selected at the source and increase the computational complexity of the packet decoding process. Unlike other classes of rate less codes, BC preserve the degree distribution of the encoded packets through the recombinations at the nodes. Furthermore, BC enable to control the decoding complexity of each network node independently from the rest of the network. We evaluate BC in a P2P scenario using a purposely designed random-push protocol for live video streaming. The experiments show that BC achieve high encoding efficiency, enable nodes with different computational capabilities to coexist within the same network and reduce the processor load on a real mobile device by nearly 50%.
Few decoders in the encoder: a low complexity encoding strategy for H.26L

We propose a reduced complexity technique for the rate-distortion optimization in JVT/H.26L in the presence of packet erasures. It is named "few decoders in the encoder", and is based on the idea of generating a selected number of error patterns in the encoder, so that a limited number of co-decoding processes can be implemented to estimate the transmission distortion term. The correlation amongst packet erasures is taken into account by employing a binary Gilbert model. The proposed algorithm exhibits competitive performance in terms of average PSNR and probability of decoding failure, with very affordable complexity and memory requirements.
Joint recovery algorithms using difference of innovations for distributed compressed sensing

Distributed compressed sensing is concerned with representing an ensemble of jointly sparse signals using as few linear measurements as possible. Two novel joint reconstruction algorithms for distributed compressed sensing are presented in this paper. These algorithms are based on the idea of using one of the signals as side information; this allows to exploit joint sparsity in a more effective way with respect to existing schemes. They provide gains in reconstruction quality, especially when the nodes acquire few measurements, so that the system is able to operate with fewer measurements than is required by other existing schemes. We show that the algorithms achieve better performance with respect to the state-of-the-art.
Fast code-rate optimization for robust image transmission over lossy packet networks

In this paper, we propose an efficient method for the allocation of Reed-Solomon codes to source symbols, for unequal loss protection. The proposed formulation recasts the multivariate optimization problem into a univariate one, dramatically reducing the computational complexity. Results are shown for image transmission over lossy packet networks, employing the JPEG2000 and SPIHT encoders. The proposed algorithm exhibits performance equivalent to previous methods, while providing a significant complexity reduction.
Distributed ADMM for In-Network Reconstruction of Sparse Signals With Innovations

In this paper, we tackle the in-network recovery of sparse signals with innovations. We assume that the nodes of the network measure a signal composed by a common component and an innovation, both sparse and unknown, according to the joint sparsity model 1 (JSM-1). Acquisition is performed as in compressed sensing, hence the number of measurements is reduced. Our goal is to show that distributed algorithms based on the alternating direction method of multipliers (ADMM) can be efficient in this framework to recover both the common and the individual components. Specifically, we define a suitable functional and we show that ADMM can be implemented to minimize it in a distributed way, leveraging local communication between nodes. Moreover, we develop a second version of the algorithm, which requires only binary messaging, significantly reducing the transmission load.
Fine-grained vehicle classificationusing deep residual networks with multiscale attention windows

Fine-grained vehicle classiflcation is a challenging task due to the subtle differences between vehicle classes. Several successful approaches to fine-grained image classification rely on part-based models, where the image is classified according to discriminative object parts. Such approaches require however that parts in the training images be manually annotated, a laborintensive process. We propose a convolutional architecture realizing a transform network capable of discovering the most discriminative parts of a vehicle at multiple scales. We experimentally show that our architecture outperforms a baseline reference if trained on class labels only, and performs closely to a reference based on a part-model if trained on loose vehicle localization bounding boxes.
Loop detection in robotic navigation using MPEG CDVS

The choice for image descriptor in a visual navigation system is not straightforward. Descriptors must be distinctive enough to allow for correct localization while still offering low matching complexity and short descriptor size for real-time applications.  Descriptor for Visual Search is a low complexity image descriptor that offers several levels of compromises between descriptor distinctiveness and size. In this work we describe how these trade-offs can be used for efficient loop-detection in a typical indoor environment.