nils-herrmann/clean_scirepeval huggingface dataset:
Overlapped Quasi-Arithmetic Codes for Distributed Video Coding

This paper describes Slepian-Wolf codes based on overlapped quasi-arithmetic codes, where overlapping allows lossy compression of the source below its entropy. In the context of separate decoding, these codes are not uniquely decodable: the overlap introduces ambiguity in the decoding process leading to decoding errors. The presence of correlated side information at the decoder is used to remove this ambiguity and achieve a vanishing error probability. The state models and the automata of the overlapped quasi-arithmetic codes are described. The soft decoding algorithm with side information is then presented. The performance of these codes has been assessed first on theoretical sources and integrated in a distributed video coding platform.
3D Scene Modeling for Distributed Video Coding

The compression efficiency of distributed video-coding (DVC) suffers from the necessity of transmitting a large number of key-frames which are intra-coded. This paper describes a new 3D model-based DVC approach which reduces the key- frame frequency. The decoder first recovers a 3D model from the key-frames. It then predicts the intermediate frames by projecting it onto 2D image planes and applying image-based rendering techniques. This paper also introduces a new quasi-DVC method relying on a limited point tracking at the encoder. It greatly improves the prediction PSNR, while only slightly increasing the encoder complexity. It also allows the encoder to adaptively select the key-frames based on the video motion-content.
Bit-rate allocation for multi-view video plus depth

The efficient compression of multi-view-video-plus-depth (MVD) data raises the bit-rate allocation issue for the compression of texture and depth data. This question has not been solved yet because not all surveys reckon on a shared framework. This paper studies the impact of bit-rate allocation for texture and depth data relying on the quality of an intermediate synthesized view. The results show that depending on the acquisition configuration, the synthesized views require a different ratio between the depth and texture bit-rate: between 40% and 60% of the total bit-rate should be allocated to depth.
Neighbor embedding with non-negative matrix factorization for image prediction

The paper studies several non-negative matrix factorization methods with nearest neighbors constrained dictionaries for image prediction. The methods considered include the multiplicative update algorithm, the projected gradient algorithm, as well as the graph-regularized NMF solution which aims at taking into account the geometrical structure of the input data. The Intra prediction problem based on these NMF solutions amounts to a neighbor embedding problem. Both prediction and rate-distortion performances are then given in comparison with other neighbor embedding methods like locally linear embedding (LLE) and locally linear embedding with low dimensional neigborhood representation (LLE-LDNR).
Online dictionaries for image prediction

This paper presents a novel dictionary learning method which, because of its simplicity and the limited number of training samples it requires, can be used for online learning of dictionaries for spatial texture prediction. The proposed learning method has first been described to address the problem of intra image prediction based on signal expansion on overcomplete dictionaries. It has then been evaluated in a complete image codec. The experimental results obtained show a significant improvement in terms of the quality of the predicted image compared to H.264/AVC intra prediction. Significant rate-distortion gains have also been achieved on the reconstructed image, after coding and decoding the prediction residue, compared with the H.264/AVC and a sparse spatial prediction method which will be referred to as the generalized template matching approach.
A simplified rate-distortion optimization procedure relying on statistical subband and noise modelling

Rate-distortion optimization procedures are very often used in rate allocation problems encountered in source encoders. However, these procedures can be, depending on the number of quantizers considered, very time consuming. This paper describes a simplified approach relying on statistical signal and noise modeling. The strategy presented leads to performances comparable to a classical rate-distortion procedure with a significant reduction of computational cost, making real-time implementations very feasible.
Sparse approximation with an orthogonal complementary matching pursuit algorithm

This paper presents the orthogonal extension of the recently introduced complementary matching pursuit (CMP) algorithm for sparse approximation . The CMP algorithm is analogous to the matching pursuit (MP) but done in the row-space of the dictionary matrix. It suffers from a similar sub-optimality as the MP. The orthogonal complementary matching pursuit algorithm (OCMP) presented here tries to remove this sub-optimality by updating the coefficients of all selected atoms at each iteration. Its development from the CMP follows the same procedure as of the orthogonal matching pursuit (OMP). In contrast with OMP, the residual errors resulting from the OCMP may not be orthogonal to all the atoms selected up to the respective iteration. Though the residual energy may increase over the OMP during the first iterations, it is shown that, compared with OMP, the convergence speed is increased in the subsequent iterations and the sparsity of the solution vector is improved.
Single-Image Super-Resolution via Linear Mapping of Interpolated Self-Examples

This paper presents a novel example-based single-image superresolution procedure that upscales to high-resolution (HR) a given low-resolution (LR) input image without relying on an external dictionary of image examples. The dictionary instead is built from the LR input image itself, by generating a double pyramid of recursively scaled, and subsequently interpolated, images, from which self-examples are extracted. The upscaling procedure is multipass, i.e., the output image is constructed by means of gradual increases, and consists in learning special linear mapping functions on this double pyramid, as many as the number of patches in the current image to upscale. More precisely, for each LR patch, similar self-examples are found, and, because of them, a linear function is learned to directly map it into its HR version. Iterative back projection is also employed to ensure consistency at each pass of the procedure. Extensive experiments and comparisons with other state-of-the-art methods, based both on external and internal dictionaries, show that our algorithm can produce visually pleasant upscalings, with sharp edges and well reconstructed details. Moreover, when considering objective metrics, such as Peak signal-to-noise ratio and Structural similarity, our method turns out to give the best performance.
Incremental-LDI for multi-view coding

This paper describes an Incremental algorithm for Layer Depth Image construction (I-LDI) from multi-view plus depth data sets. A solution to sampling artifacts is proposed, based on pixel interpolation (inpainting) restricted to isolated unknown pixels. A solution to ghosting artifacts is also proposed, based on a depth discontinuity detection, followed by a local foreground / background classification. We propose a formulation of warping equations which reduces time consumption, specifically for LDI warping. Tests on Breakdancers and Ballet MVD data sets show that extra layers in I-LDI contain only 10% of first layer pixels, compared to 50% for LDI. I-LDI Layers are also more compact, with a less spread pixel distribution, and thus easier to compress than LDI Visual rendering is of similar quality with I-LDI and LDI.
Robust multiplexed codes for compression of heterogeneous data

Compression systems of real signals (images, video, audio) generate sources of information with different levels of priority which are then encoded with variable-length codes (VLCs). This paper addresses the issue of robust transmission of such VLC encoded heterogeneous sources over error-prone channels. VLCs are very sensitive to channel noise: when some bits are altered, synchronization losses can occur at the receiver. This paper describes a new family of codes, called multiplexed codes, that confine the de-synchronization phenomenon to low-priority data while reaching asymptotically the entropy bound for both (low- and high-priority) sources. The idea consists of creating fixed-length codes for high-priority information and of using the inherent redundancy to describe low-priority data, hence the name multiplexed codes. Theoretical and simulation results reveal a very high error resilience at almost no cost in compression efficiency.
Local inverse tone curve learning for high dynamic range image scalable compression

This paper presents a scalable high dynamic range (HDR) image coding scheme in which the base layer is a low dynamic range version of the image that may have been generated by an arbitrary tone mapping operator (TMO). No restriction is imposed on the TMO, which can be either global or local, so as to fully respect the artistic intent of the producer. Our method successfully handles the case of complex local TMOs thanks to a block-wise and non-linear approach. A novel template-based interlayer prediction (ILP) is designed in order to perform the inverse tone mapping of a block without the need to transmit any additional parameter to the decoder. This method enables the use of a more accurate inverse tone mapping model than the simple linear regression commonly used for block-wise ILP. In addition, this paper shows that a linear adjustment of the initially predicted block can further improve the overall coding performance by using an efficient encoding scheme of the scaling parameters. Our experiments have shown an average bitrate saving of 47% on the HDR enhancement layer, compared with the previous local ILP methods.
Accuracy-scalable motion coding for efficient scalable video compression

For a scalable video coder to remain efficient over a wide range of bit-rates, covering e.g. both mobile video streaming and TV broadcasting, some form of scalability must exist in the motion information. In this paper we propose a new (1+2D) wavelet-based spatio-SNR-temporal-scalable video codec, coupled with an accuracy-scalable motion codec. It allows to decode a reduced amount of motion information at subresolutions, taking advantage that motion compensation requires less and less accuracy at lower spatial resolutions. This new motion codec proves its efficiency in our full-scalable framework, by improving significantly video quality at subresolutions without inducing any noticeable penalty at high bit-rates.
Non-asymmetric Slepian-Wolf coding of non-uniform Bernoulli sources

We address the problem of non-asymmetric Slepian-Wolf (SW) coding of two correlated non-uniform Bernoulli sources. We first show that the problem is not symmetric in the two sources, contrarily to the case of uniform sources, due to the asymmetry induced by two underlying channel models, namely additive and predictive Binary Symmetric Channels (BSC). That asymmetry has to be accounted for during the decoding. In view of that result, we describe the implementation of a joint non-asymmetric decoder of the two sources based on Low-Density Parity-Check (LDPC) codes and Message Passing (MP) decoding. We also give a necessary and sufficient condition for the recovery of the two sources, that imposes a triangular structure of a sub-part of the equivalent matrix representation of the code.
Computation of posterior marginals on aggregated state models for soft source decoding

Optimum soft decoding of sources compressed with variable length codes and quasi-arithmetic codes, transmitted over noisy channels, can be performed on a bit/symbol trellis. However, the number of states of the trellis is a quadratic function of the sequence length leading to a decoding complexity which is not tractable for practical applications. The decoding complexity can be significantly reduced by using an aggregated state model, while still achieving close to optimum performance in terms of bit error rate and frame error rate. However, symbol a posteriori probabilities can not be directly derived on these models and the symbol error rate (SER) may not be minimized. This paper describes a two-step decoding algorithm that achieves close to optimal decoding performance in terms of SER on aggregated state models. A performance and complexity analysis of the proposed algorithm is given.
Joint source and channel coding/decoding by concatenating an oversampled filter bank code and a redundant entropy code [image coding application]

This paper examines a joint source and channel coding (JSCC) technique based on oversampled filter banks (OFB). We consider an image compression system with an OFB based signal decomposition followed by scalar quantization and a redundant entropy coding scheme. This encoding chain can be viewed as a concatenation of an OFB code and an error correcting entropy code. The error correcting ability of an OFB comes from the explicit redundancy introduced into the transmitted signal by oversampling while the error correcting ability of the entropy code comes from the implicit redundancy due to the Markov property of the OFB outputs. The decoder consists of the soft-input soft-output entropy decoder and a syndrome decoder for the OFB code. The soft information at the output of the entropy decoder is utilized in the error localization procedure of the OFB syndrome decoder. The performance of the algorithm is examined for an image coding system with a wavelet filter bank.
SIFT-based local image description using sparse representations

This paper addresses the problem of efficient SIFT-based image description and searches in large databases within the framework of local querying. A descriptor called the bag-of-features has been introduced in  which first vector quantizes SIFT descriptors and then aggregates the set of resulting codeword indices (so-called visual words) into a histogram of occurence of the different visual words in the image. The aim is to make the image search complexity tractable by transforming the set of local image descriptor vectors into a single sparse vector as sparsity particularly permits efficient inner product calculations. However, aggregating local descriptors into a single histogram decreases the discerning power of the system when performing local queries. In this paper, we propose a new approach that aims to enjoy the complexity benefits of sparsity while at the same time retaining the local quality of the input descriptor vectors. This is accomplished by searching for a sparse approximation of the input SIFT descriptors. The sparse approximation yields a sparse vector per local SIFT descriptor, and helps preserving local description properties by using each sparse-transformed descriptor independently in a voting system to retrieve indexed images. Our system is shown experimentally to perform better than histogram based systems under query locality, albeit at an increased complexity.
Atomic decomposition dedicated to AVC and spatial SVC prediction

This work, we propose the use of sparse signal representation techniques to solve the problem of closed-loop spatial image prediction. The reconstruction of signal in the block to predict is based on basis functions selected with the matching pursuit (MP) iterative algorithm, to best match a causal neighborhood. We evaluate this new method in terms of PSNR and bitrate in a H.264/AVC encoder. Experimental results indicate an improvement of rate-distortion performance. In this paper, we also present results concerning the use of this technique for intra-inter layer prediction refinement, in a scalable video coding (SVC) like scheme.
Subspace algorithms for error localization with DFT codes

We propose two subspace algorithms for error localization with quantized DFT codes. The algorithms are similar to the MUSIC and the minimum-norm algorithms employed in array signal processing for direction of arrival (DOA) estimation. We present the algorithms in a generalized form with a variable dimension syndrome covariance matrix. Simulation results show that their localization performances are similar, and they achieve the peak values when the rank of the covariance matrix reaches the maximum value. They also perform better than the coding theoretic approach over a broad range of channel-error-to-quantization-noise ratio.
The Iteration-Tuned Dictionary for sparse representations

We introduce a new dictionary structure for sparse representations better adapted to pursuit algorithms used in practical scenarios. The new structure, which we call an Iteration-Tuned Dictionary (ITD), consists of a set of dictionaries each associated to a single iteration index of a pursuit algorithm. In this work we first adapt pursuit decompositions to the case of ITD structures and then introduce a training algorithm used to construct ITDs. The training algorithm consists of applying a K-means to the (i −1)-th residuals of the training set to thus produce the i-th dictionary of the ITD structure. In the results section we compare our algorithm against the state-of-the-art dictionary training scheme and show that our method produces sparse representations yielding better signal approximations for the same sparsity level.
Joint source-channel turbo decoding of entropy-coded sources

We analyze the dependencies between the variables involved in the source and channel coding chain. This analysis is carried out in the framework of Bayesian networks, which provide both an intuitive representation for the global model of the coding chain and a way of deriving joint (soft) decoding algorithms. Three sources of dependencies are involved in the chain: (1) the source model, a Markov chain of symbols; (2) the source coder model, based on a variable length code (VLC), for example a Huffman code; and (3) the channel coder, based on a convolutional error correcting code. Joint decoding relying on the hidden Markov model (HMM) of the global coding chain is intractable, except in trivial cases. We advocate instead an iterative procedure inspired from serial turbo codes, in which the three models of the coding chain are used alternately. This idea of using separately each factor of a big product model inside an iterative procedure usually requires the presence of an interleaver between successive components. We show that only one interleaver is necessary here, placed between the source coder and the channel coder. The decoding scheme we propose can be viewed as a turbo algorithm using alternately the intersymbol correlation due to the Markov source and the redundancy introduced by the channel code. The intermediary element, the source coder model, is used as a translator of soft information from the bit clock to the symbol clock.
Real-time constrained TCP-compatible rate control for video over the Internet

This paper describes a rate control algorithm that captures not only the behavior of TCP's congestion control avoidance mechanism but also the delay constraints of real-time streams. Building upon the TFRC protocol , a new protocol has been designed for estimating the bandwidth prediction model parameters. Making use of RTP and RTCP, this protocol allows to better take into account the multimedia flows characteristics (variable packet size, delay ...). Given the current channel state estimated by the above protocol, encoder and decoder buffers states as well as delay constraints of the real-time video source are translated into encoder rate constraints. This global rate control model, coupled with an H.263+ loss resilient video compression algorithm, has been extensively experimented with on various Internet links. The experiments clearly demonstrate the benefits of 1/ the new protocol used for estimating the bandwidth prediction model parameters, adapted to multimedia flows characteristics, and of 2/ the global rate control model encompassing source buffers and end-to-end delay characteristics. The overall system leads to reduce significantly the source timeouts, hence to minimize the expected distortion, for a comparable usage of the TCP-compatible predicted bandwidth.
Subspace algorithms for error localization with quantized DFT codes

Recently, a class of real-number Bose-Chaudhuri-Hocquengem codes known as discrete Fourier transform (DFT) codes have been considered as joint source and channel codes for providing robustness to erasures and errors over wireless networks. We propose three subspace algorithms for error localization with quantized DFT codes. The algorithms are similar to the MUSIC, the minimum-norm, and the ESPRIT algorithms used in array signal processing for direction-of-arrival estimation. They provide different but related formulations of the error localizations by first partitioning a vector space into the channel error subspace and its orthogonal complement, the noise subspace. The locations of the errors are determined from either the error subspace eigenvectors or the noise subspace eigenvectors. We also present a brief performance analysis of the localization error in terms of the perturbation of the error subspace due to quantization. Simulation results show that their localization performances are similar, and they perform better than the coding-theoretic approach over a broad range of channel-error-to-quantization-noise ratios.
Subspace-based error and erasure correction with DFT codes for wireless channels

This paper deals with the decoding of lowpass discrete Fourier transform (DFT) codes in the presence of both errors and erasures. We propose a subspace-based approach for the error localization that is similar to the subspace approaches followed in the array signal processing for direction-of-arrival (DOA) estimation. The basic idea is to divide a vector space into two orthogonal subspaces of which one is spanned by the error locator vectors. The locations of the errors are estimated from the spanning eigenvectors of the complement subspace. However, unlike the subspace approach in DOA estimation, which is similar to estimating the subspaces from the syndrome covariance matrix after a projection, in the proposed approach, the subspaces are estimated from the modified syndrome covariance matrix after a whitening transform. Simulation results with a Gauss-Markov source reveal that the proposed algorithm is more efficient than the coding theoretic approach on impulsive channels as well as the subspace approach with projection on lossy channels.
Learning clustering-based linear mappings for quantization noise removal

This paper describes a novel scheme to reduce the quantization noise of compressed videos and improve the overall coding performances. The proposed scheme first consists in clustering noisy patches of the compressed sequence. Then, at the encoder side, linear mappings are learned for each cluster between the noisy patches and the corresponding source patches. The linear mappings are then transmitted to the decoder where they can be applied to perform de-noising. The method has been tested with the HEVC standard, leading to a bitrate saving of up to 9.63%.
Wyner-Ziv coding of three correlated Gaussian sources using punctured turbo codes

In this article, we address the problem of distributed coding of three correlated memoryless Gaussian sources using punctured turbo codes. We first revisit the problem of distributed coding of two Gaussian sources. The impact of the distortion induced by the quantization of the side information on the performance of Wyner-Ziv decoding of two continuous valued Gaussian correlated sources is analyzed. Theoretic bounds as well as practical coding performances, when the side information is quantized, are given. This leads to the derivation of appropriate settings for the problem of distributed coding of three sources. The Wyner-Ziv theorem is then extended for three sources. Practical solutions using punctured turbo codes are described. Simulation results are presented and analyzed for different amounts of correlation