nils-herrmann/clean_scirepeval huggingface dataset:
Sparse approximations for joint source-channel coding

This paper considers the application of sparse approximations in a joint source-channel (JSC) coding framework. The considered JSC coded system employs a real number BCH code on the input signal before the signal is quantized and further processed. Under an impulse channel noise model, the decoding of error is posed as a sparse approximation problem. The orthogonal matching pursuit (OMP) and basis pursuit (BP) algorithms are compared with the syndrome decoding algorithm in terms of mean square reconstruction error. It is seen that, with a Gauss-Markov source and Bernoulli-Gaussian channel noise, the BP outperforms the syndrome decoding and the OMP at higher noise levels. In the case of image transmission with channel bit errors, the BP outperforms the other two decoding algorithms consistently.
Wavelet construction using Lagrange halfband filters

Using the approach described by M.J.T. Smith and T.P. Barnwell (1986) for obtaining exact-reconstruction filter banks, the authors present conjugate-quadrature and linear-phase solutions for two-channel filter banks using Lagrange halfband filters. It is shown that the wavelet solutions obtained by I. Daubechies (1988) under certain regularity conditions are the same as the conjugate-quadrature solutions derived from Lagrange halfband filters using the above approach. The linear-phase solution that is described provides filters with simple coefficients.
Light field compression using depth image based view synthesis

This paper describes a novel light field compression scheme using a depth image-based view synthesis technique. A small subset of views is compressed with HEVC inter coding and then used to reconstruct the entire light field. The residual of the whole light field can be then restructured as a video sequence and encoded by HEVC inter coding. Experiments show that our scheme significantly outperforms a similar view synthesis method which utilizes convolutional neural networks, and does not require training with a large dataset of light fields as required by deep learning techniques. It also outperforms as well the direct encoding of all the light field views.
Syndrome decoding and performance analysis of DFT codes with bursty erasures

In this paper, we analyze the performance of DFT codes with bursty erasures in the framework of syndrome decoding. Bursty erasures give rise to a syndrome decoding matrix which has very large elements depending on the code parameters and the burst length. The largeness of the elements of the syndrome decoding matrix is studied by establishing various relationships between the syndrome decoding matrix, and the parity and the generator polynomial coefficients. With a suitable model for the quantization error, the reconstruction error performance of DFT codes in the context of the syndrome decoding is analyzed and then applied to the case of bursty erasures. Simulation results with a Gauss-Markov source verify the theoretical results obtained with the assumed quantization error model.
Image compression with Stochastic Winner-Take-All Auto-Encoder

This paper addresses the problem of image compression using sparse representations. We propose a variant of autoencoder called Stochastic Winner-Take-All Auto-Encoder (SWTA AE). “Winner-Take-All” means that image patches compete with one another when computing their sparse representation and “Stochastic” indicates that a stochastic hyperparameter rules this competition during training. Unlike auto-encoders, SWTA AE performs variable rate image compression for images of any size after a single training, which is fundamental for compression. For comparison, we also propose a variant of Orthogonal Matching Pursuit (OMP) called Winner-Take-All Orthogonal Matching Pursuit (WTA OMP). In terms of rate-distortion trade-off, SWTA AE outperforms auto-encoders but it is worse than WTA OMP. Besides, SWTA AE can compete with JPEG in terms of rate-distortion.
Error recovery properties of quasi-arithmetic codes and soft decoding with length constraint

In this paper, we propose a method to analyse the error recovery properties of quasi-arithmetic codes. This method is adapted from the one proposed in J. Maxted and J. Robinson (1985) for variable length codes. The expected number of symbols affected by a single bit error and the probability mass function of the gain/loss (P.F. Swaszek and P. DiCicco, 1995) of symbols following a single bit error can be computed with this method. A method to estimate this probability mass function when a bitstream is sent over a binary symmetrical channel is then proposed. The aggregated state model for soft decoding of variable length codes proposed in H. Jegou et al. (2005) is then extended to quasi-arithmetic codes, as the synchronisation recovery properties of both kind of codes are similar. The soft decoding results of this scheme reveal high performance with a reasonable computing cost
Frame-theoretic analysis of DFT codes with erasures

Discrete Fourier transform (DFT) codes have been used as joint source-channel codes in order to provide robustness against channel errors and erasures. This paper presents a frame-theoretic analysis of lowpass DFT codes with erasures. First, it is shown that such codes make a special class of frames. Then, the message reconstructions by the frame-theoretic signal space projection and coding-theoretic syndrome decoding of erasures are analyzed, and their equivalence is proved. The error performance of lowpass DFT codes is analyzed using the frame theory. The analysis helps in classifying the subframes of a DFT frame into several classes. This classification leads to the development of packetization criteria in order to guarantee minimum mean square reconstruction error and, thus, to minimize the effect of the quantization error. Simulation results substantiate the presented theory and the proposed packetization schemes.
Perceptual watermarking of non i.i.d. signals based on wide spread spectrum using side information

The theoretical foundations of data hiding have been revealed by formulating the problem as message communication over a noisy channel, i.e. the host document. In light of this definition, some solutions have been proposed. Unfortunately, the performances of those methods are limited due to interference with the host signal. Considering spread spectrum information hiding with non i.i.d. Gaussian host signals and weighted distortion measures, we propose a game-theoretic resolution of the problem using side information.
Mode Dependent Vector Quantization with a rate-distortion optimized codebook for residue coding in video compression

The High Efficiency Video Coding standard (HEVC) supports a total of 35 intra prediction modes which aim at reducing spatial redundancy by exploiting pixel correlation within a local neighborhood. In this paper, we show that spatial correlation remains after intra prediction, leading to high energy prediction residues. We propose a novel scheme for encoding the prediction residues using a Mode Dependent Vector Quantization (MDVQ) which aims at reducing the redundancy in residual domain. The MDVQ codebook is optimized in a rate-distortion (RD) sense. Experimental results show that the codebook can be independent of the quantization parameter (QP) with no loss in terms of coding efficiency. A bitrate reduction of 1.1% on average compared to HEVC can be achieved, while further tests indicate that codebook adaptivity could substantially improve the performance.
Tree-structured oversampled filterbanks as joint source-channel codes: application to image transmission over erasure channels

This paper studies oversampled filterbanks for robust transmission of multimedia signals over erasure channels. Oversampled filterbanks implement frame expansions of signals in l/sup 2/(Z). The dependencies between the expansion coefficients introduced by the oversampled filterbank are first characterized both in the z-domain and in the time-domain. Conditions for recovery of some typical erasure patterns like bursty erasure patterns are derived. The analysis leads to the design of two erasure recovery algorithms that are first studied without quantization noise. The reconstruction algorithm derived from the time-domain analysis exploits the fact that an oversampled filterbank represents signals with more than one set of basis functions. The erased samples are first reconstructed from the received ones, and then, signal space projection is applied. The effect of quantization noise on the reconstructed signal is studied for both algorithms. Using image signals, the theoretical results are validated for a number of erasure patterns, considering unequal error protection enabled tree-structured decompositions.
A subspace algorithm for error localization with overcomplete frame expansions and its application to image transmission

In this paper, we propose a subspace algorithm for error localization with quantized frame expansion. The algorithm is similar to the MUSIC algorithm for direction of arrival (DOA) estimation in the array signal processing. The frames considered are a class of error-correcting frames characterized by the BCH-like property of the parity check matrices of the associated complex block codes. In particular, we consider low-pass DFT, DCT and DST frames. Simulation results show that the subspace algorithm outperforms the error locator polynomial approach in localization efficiency. For a G-M source, the DFT frame is seen to have the best performance, however, when applied to an image, the DCT and DST frames are seen to perform somewhat better than the DFT frame especially at higher channel bit error rates.
Compressing the Laplacian Pyramid

The Laplacian pyramid (LP) is one of the earliest examples of multiscale representation of visual data. It is well known that an LP is overcomplete or redundant by construction, and has lower compression efficiency compared to critical representations such as wavelets and subband coding. In this paper, we propose to improve the rate-distortion (R-D) performance of the LP through critical representation. We consider an LP with biorthogonal decimation and interpolation filters, and show that the detail signals lie in lower-dimensional subspaces. This allows them to be represented using fewer coefficients than the original spatial representations. We derive orthogonal bases for these subspaces and represent the detail signals in terms of their projections onto these bases. Simulation results suggest that higher compression ratios can be achieved with the critical representation than with the standard LP with usual or dual frame based reconstructions
Locally linear embedding based texture synthesis for image prediction and error concealment

The template matching algorithm is a simple extension to exemplar-based texture synthesis. Average of template matching predictors or non-local means based approaches can be seen as heuristic extensions to template matching. These methods which linearly combine several texture patches have been shown to be more robust in synthesis and to give better results when compared to simple template matching. However, they do not search to minimize an approximation error on the known pixel values in the template. They are rather heuristic methods for calculating the linear weighting coefficients. This paper proposes a neighbor embedding based texture synthesis method by formulating the problem as a least-squares optimization using locally linear embedding. By this means, one calculates the linear weighting coefficients by solving a constrained optimization for approximating the template. The proposed texture synthesis framework has first been applied to the image prediction (predictive coding) problem. It has then been extended to a loss concealment application for transmission errors. Experimental results demonstrate the effectiveness of the proposed method for both image compression and error concealment.
Recursive syndrome decoding of DFT codes for bursty erasures

Recently DFT codes have been considered for use as joint source-channel codes in order to provide robustness against packet loss in IP networks. In this paper, we propose a recursive syndrome decoding algorithm of DFT codes for bursty erasure patterns. The algorithm is based on the statistical properties of the quantization error and the codevector. The statistical analysis of the codevector is independent of the proposed algorithm. We also analyze the error performance of the proposed decoding scheme, and show that it can perform better than the conventional syndrome decoding of DFT codes if the message signal model and quantization error model assumptions are valid. Simulation results with a Gauss-Markov source are presented.
Spatial prediction in distributed video coding using Wyner-Ziv DPCM

This paper deals with spatial prediction in Wyner-Ziv video coding. Coding structures that extend DPCM to the distributed coding scenario are presented. Using a high resolution approximation, the optimal prediction filters for these structures are derived. The resulting predictive coder is employed to compress the DC component of a transform based distributed video coding system. Preliminary results show rate-distortion gains compared to the case without spatial prediction.
3-D Model-Based Frame Interpolation for Distributed Video Coding of Static Scenes

This paper addresses the problem of side information extraction for distributed coding of videos captured by a camera moving in a 3-D static environment. Examples of targeted applications are augmented reality, remote-controlled robots operating in hazardous environments, or remote exploration by drones. It explores the benefits of the structure-from-motion paradigm for distributed coding of this type of video content. Two interpolation methods constrained by the scene geometry, based either on block matching along epipolar lines or on 3-D mesh fitting, are first developed. These techniques are based on a robust algorithm for sub-pel matching of feature points, which leads to semi-dense correspondences between key frames. However, their rate-distortion (RD) performances are limited by misalignments between the side information and the actual Wyner-Ziv (WZ) frames due to the assumption of linear motion between key frames. To cope with this problem, two feature point tracking techniques are introduced, which recover the camera parameters of the WZ frames. A first technique, in which the frames remain encoded separately, performs tracking at the decoder and leads to significant RD performance gains. A second technique further improves the RD performances by allowing a limited tracking at the encoder. As an additional benefit, statistics on tracks allow the encoder to adapt the key frame frequency to the video motion content
Joint Source–Channel Turbo Techniques for Discrete-Valued Sources: From Theory to Practice

The principles which have been prevailing so far for designing communication systems rely on Shannon's source and channel coding separation theorem. This theorem states that source and channel optimum performance bounds can be approached as close as desired by designing independently the source and channel coding strategies. However, this theorem holds only under asymptotic conditions, where both codes are allowed infinite length and complexity. If the design of the system is constrained in terms of delay and complexity, if the sources are not stationary, or if the channels are nonergodic, separate design and optimization of the source and channel coders can be largely suboptimal. For practical systems, joint source-channel (de)coding may reduce the end-to-end distortion. It is one of the aspects covered by the term cross-layer design, meaning a rethinking of the layer separation principle. This article focuses on recent developments of joint source-channel turbo coding and decoding techniques, which are described in the framework of normal factor graphs. The scope is restricted to lossless compression and discrete-valued sources. The presented techniques can be applied to the quantized values of a lossy source codec but the quantizer itself and its impact are not considered.
Shallow sparse autoencoders versus sparse coding algorithms for image compression

This paper considers the problem of image compression with shallow sparse autoencoders. We use both a T-sparse autoencoder (T-sparse AE) and a winner-take-all autoencoder (WTA AE). A performance analysis in terms of rate-distortion trade-off and complexity is conducted, comparing with LARS-Lasso, Coordinate Descent (CoD) and Orthogonal Matching Pursuit (OMP). We show that, WTA AE achieves the best rate-distortion trade-off, it is robust to quantization noise and it is less complex than LARS-Lasso, CoD and OMP.
Image coding with iterated contourlet and wavelet transforms

This paper presents a new coding technique based on a mixed contourlet and wavelet transform. The redundancy of the transform is controlled by using the contourlet at fine scales and by switching to a separable wavelet transform at coarse scales. The transform is then optimized through an iterative projection process in the transform domain in order to minimize the quantization error in the image domain. A gain of respectively up to 0.5 dB and to 1 dB over respectively contourlet and wavelet based coding has been observed for images with directional features.
A note on 'Wavelet construction using Lagrange halfband filters'

For the original article see ibid., vol.38, no.9, p.116-8 (1991). The author's previous letter describes the use of Lagrange halfband filters in obtaining conjugate-quadrature and linear-phase solutions for a two-channel exact reconstruction filter bank. They showed that the wavelet solutions obtained by I. Daubechies (1988) (under certain regularity conditions) are the same as the conjugate-quadrature solutions to exact reconstruction filter banks derived from Lagrange halfband filters. In this note, it is pointed out that the relation between Daubechies' result and Lagrange halfband filters was established independently by M.J. Shensa (1992), who brought his work to the authors' attention recently.
ESPRIT-like error localization algorithm for a class of real number codes

and channel coding of multimedia data for transmission over wireless networks is a challenging task. Recently, a class of BCH-like real number codes has been considered for providing robustness to data loss or data error over such networks. Unlike finite-field codes, the real codevectors undergo a quantization process before being transmitted, and therefore, the error localization efficiency becomes algorithm-dependent. The paper presents an ESPRIT-like subspace algorithm for localizing errors in such codes and compares its performance with other known localization algorithms. In particular, we consider lowpass DFT, DCT and DST codes, and compare the localization efficiency of the ESPRIT-like algorithm with the standard BCH decoding approach and the MUSIC-like subspace approach. Simulation results with a Gauss-Markov source are presented.
Out-of-Sample Generalizations for Supervised Manifold Learning for Classification

Supervised manifold learning methods for data classification map high-dimensional data samples to a lower dimensional domain in a structure-preserving way while increasing the separation between different classes. Most manifold learning methods compute the embedding only of the initially available data; however, the generalization of the embedding to novel points, i.e., the out-of-sample extension problem, becomes especially important in classification applications. In this paper, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with an iterative process. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.
Spatial intra-prediction based on mixtures of sparse representations

In this paper, we consider the problem of spatial prediction based on sparse representations. Several algorithms dealing with this problem can be found in the literature. We propose a novel method involving a mixture of sparse representations. We first place this approach into a probabilistic framework and then derive a practical procedure to solve it. Comparisons of the rate-distortion performance show the superiority of the proposed algorithm with regard to other state-of-the-art algorithms.
Optimal Reconstruction in Wyner-Ziv Video Coding with Multiple Side Information

This paper addresses the problem of optimal minimum mean-squared error reconstruction of quantised samples in Wyner-Ziv video coding systems. Closed-form expressions of the optimal reconstructed values are derived for a Laplacian correlation model. The method is used for both single and multiple side information scenarios (the latter is also referred to as multi-hypothesis Wyner-Ziv decoding). The efficiency of the proposed optimal reconstruction method is confirmed by rate-distortion performance results, showing significant decrease of the distortion of the decoded sequence, compared to simple reconstruction methods that have been employed so far.
Locally linear embedding methods for inter image coding

Image prediction methods based on data dimensionality reduction techniques have been recently introduced for still images. These techniques have been proven efficient, especially when used in a H.264 framework. This paper introduces a natural extension of this work from spatial prediction to temporal prediction. Locally Linear Embedding and Optimized Map-Aided Locally Linear Embedding methods are adapted within H.264 in order to improve inter image prediction. The resulting prediction methods are compared with the H.264 motion estimation/compensation method, template matching methods and Adaptive Interpolation Filters, and are shown to bring significant Rate-Distortion performance improvements (up to 20.21 % rate saving with respect to H.264).
Mesh-Based Motion-Compensated Interpolation for Side Information Extraction in Distributed Video Coding

This paper addresses the problem of side information generation in distributed video compression (DVC) schemes. Intermediate frames constructed by motion-compensated interpolation of key frames are used as side information to decode Wyner-Ziv frames. The limitations of block-based translational motion models call for new motion models. This article studies the benefits of mesh-based motion-compensated (MC) interpolation for side information extraction in DVC. A hybrid block-based and mesh-based solution addressing the problem of motion discontinuities and occlusions is also described. The increased correlation between the side information and the Wyner-Ziv encoded frames leads to performance gains which can reach up to 1 dB with respect to block-based solutions.
Perceptually-friendly H.264/AVC video coding

This paper presents a perceptual H.264/AVC video coding method based on a foveated just-noticeable-distortion (JND) model. Since the perceptual acuity decreases with the increased eccentricity, a foveation model is adopted to further explore the perceptual redundancy in addition to the spatial and temporal just-noticeable-distortion models. Bit allocation and rate-distortion optimization algorithms based on the foveated model are addressed. The performance of the foveated JND model is assessed with subjective visual tests. Applying the proposed model in H.264/AVC video coding can achieve better visual quality.