nils-herrmann/clean_scirepeval huggingface dataset:
Entropy coding with variable length re-writing systems

This paper describes a new set of block source codes well suited for data compression. These codes are defined by sets of productions rules of the form allowbar rarr blowbar where a isin A represents a value from the source alphabet A and llowbar, blowbar are small sequences of bits. These codes naturally encompass other variable length codes (VLCs) such as Huffman codes. It is shown that these codes may have a similar or even a shorter mean description length than Huffman codes for the same encoding and decoding complexity. A first code design method allowing to preserve the lexicographic order in the bit domain is described. The corresponding codes have the same mean description length (mdl) as Huffman codes from which they are constructed. Therefore, they outperform from a compression point of view the Hu-Tucker codes designed to offer the lexicographic property in the bit domain. A second construction method allows to obtain codes such that the marginal bit probability converges to 0.5 as the sequence length increases and this is achieved even if the probability distribution function is not known by the encoder
Entropy Coding With Variable-Length Rewriting Systems

This paper describes a family of codes for entropy coding of memoryless sources. These codes are defined by sets of production rules of the form almacr rarr bmacr, where a is a source symbol, and lmacr, bmacr are sequences of bits. The coding process can be modeled as a finite-state machine (FSM). A method to construct codes which preserve the lexicographic order in the binary-coded representation is described. For a given constraint on the number of states for the coding process, this method allows the construction of codes with a better compression efficiency than the Hu-Tucker codes. A second method is proposed to construct codes such that the marginal bit probability of the compressed bitstream converges to 0.5 as the sequence length increases. This property is achieved even if the probability distribution function is not known by the encoder
Graph-based representation for multiview images with complex camera configurations

Graph-Based Representation (GBR) has recently been proposed for rectified multiview dataset. The core idea of GBR is to use graphs for describing the color and geometry information of a multiview dataset. The color information is represented by the vertices of the graph while the scene geometry is represented by the edges of the graph. In this paper, we generalize the GBR to multi-view images with complex camera configurations. Compared with previous work, the GBR representation introduced in this paper can handle not only horizontal displacements of the cameras but also forward/backward displacements, rotations etc. In order to have a sparse (i.e., easy to code) graph structure, we further propose to use a distortion metric to select the most meaningful connections. For the graph transmission, each selected connection is then replaced by a disparity-based quantity. The experiments show that the proposed GBR achieves high reconstructing quality with less or comparable coding rate compared with traditional depth-based representations, that directly compress the depth signal without considering the rendering task.
A DCT chip based on a new structured and computationally efficient DCT algorithm

A discrete cosine transform (DCT) algorithm and architecture that minimize both software and hardware costs are presented. The proposed approaches are either direct or indirect and are based on the decomposition of the DCT in three operations: permutation, fast Fourier transform, and rotation. The main characteristics of the VLSI implementation chosen for this DCT and inverse DCT algorithm are show. Its data path coupled with a twin-pages memory and its controller, which contains the microprograms of the DCT algorithm are described. The results in terms of data processing rate and silicon area are given.<<ETX>>
Light Field Inpainting Propagation via Low Rank Matrix Completion

Building up on the advances in low rank matrix completion, this paper presents a novel method for propagating the inpainting of the central view of a light field to all the other views. After generating a set of warped versions of the inpainted central view with random homographies, both the original light field views and the warped ones are vectorized and concatenated into a matrix. Because of the redundancy between the views, the matrix satisfies a low rank assumption enabling us to fill the region to inpaint with low rank matrix completion. To this end, a new matrix completion algorithm, better suited to the inpainting application than existing methods, is also developed in this paper. In its simple form, our method does not require any depth prior, unlike most existing light field inpainting algorithms. The method has then been extended to better handle the case where the area to inpaint contains depth discontinuities. In this case, a segmentation map of the different depth layers of the inpainted central view is required. This information is used to warp the depth layers with different homographies. Our experiments with natural light fields captured with plenoptic cameras demonstrate the robustness of the low rank approach to noisy data as well as large color and illumination variations between the views of the light field.
Source multiplexed codes for error-prone channels

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 (VLC). 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 allow to confine the de-synchronization phenomenon to low priority data while allowing to reach asymptotically the entropy bound for both (low and high priority) sources. The idea consists in creating fixed length codes for high priority information and in using the inherent redundancy to describe low priority data, hence the name "multiplexed codes". Simulation results reveal very high error resilience at almost no cost in compression efficiency.
Source adaptive 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 (TCP-friendly rate control) protocol (see Floyd, S. et al.,  2000), a new protocol based on RTP/RTCP has been designed in order to take into better account the multimedia flow characteristics in the estimation of the bandwidth prediction model parameters. Given the current channel state, the encoder and decoder buffer states and the delay constraints of the real-time video are translated into encoder rate constraints. This global rate control model, that encompasses the source buffer model as well as the end-to-end delay characteristics, allows significant reduction of source timeouts, hence minimizes the expected distortion, for a comparable usage of the TCP compatible predicted bandwidth.
Erasure resilience of oversampled filter bank codes based on cosine modulated filter banks

This paper examines erasure resilience of oversampled filter bank (OFB) codes focusing on two families of codes based on cosine modulated filter banks (CMFB). We discuss frame properties of the CMFB based OFB codes and prove perfect reconstruction (PR) for erasure patterns for which PR depends only on the general structure of the code and not on the prototype filter coefficients. For some of these erasure patterns the expression for the mean square reconstruction error is also independent of filter coefficients and can be expressed in terms of the number of erasures, and of parameters such as number of channels and oversampling ratio. We further take an image coding system as an example and verify the performance of a particular OCMFB code for all possible erasure patterns by simulation.
Real error and erasure correction with DFT codes for communication channels

This paper deals with the decoding of lowpass DFT codes in presence of both errors and erasures. First we consider the case when the codevectors are not quantized and present a coding theoretic algorithm as well as subspace based decoding algorithm. Then we consider the quantization of the codevectors and show the equivalence between the error and erasure correction, and the estimation of directions of arrival (DOA) of plane waves in array signal processing. This analogy leads to the introduction of an adaptation of the subspace based DOA estimation techniques to the problem of error and erasure correction in the real field. The adaptation incorporates a whitening operation, which is shown to be equivalent to the projection of the syndrome vector onto the orthogonal complement of the subspace spanned by the erasure locator vectors. Simulation results with a Gauss-Markov source reveal that the proposed subspace based algorithm is more efficient than the coding theoretic approach in localizing the errors.
K-NN search using local learning based on regression for neighbor embedding-based image prediction

The paper describes a K-NN search method aided by local learning of subspace mappings for the problem of neighbor-embedding based image Intra prediction. The local learning of subspace mappings relies on multivariate linear regression. The method is used jointly with Locally Linear Embedding (LLE) as well as with a method inspired from Non Local Means (NLM) for prediction. Linear and kernel ridge regression are also considered directly for predicting the unknown pixels. Rate-distortion performances are then given in comparison with Intra prediction using LLE and classical K-NN search, as well as in comparison with H.264 Intra prediction modes.
Robust decoding of arithmetic codes for image transmission over error-prone channels

This paper addresses the issue of robust decoding of arithmetic codes. We first analyze dependencies between the variables involved in arithmetic coding by means of the Bayesian formalism. This provides a suitable framework for designing a soft decoding algorithm that provides high error-resilience. It also provides a natural setting for "soft synchronization", i.e., to introduce anchors favoring the likelihood of "synchronized" paths. In order to maintain the complexity of the estimation within a realistic range, a simple, yet efficient, pruning method is described. Models and algorithms are then applied to context-based arithmetic coding widely used in practical systems (e.g. JPEG-2000). Experimentation results with both theoretical sources and with real images coded with JPEG-2000 reveal very good error resilience performances.
Two-dimensional filters with wideband circularly symmetric frequency response

Two-dimensional filters with circularly symmetric frequency response find application in isotropic filtering of images. An effective method for designing this class of filters is based on applying the McClellan transformation to a one-dimensional prototype. The requirement of wideband circular symmetry necessitates the use of high-order generalized McClellan transformations, where the transformation has to be redesigned as the passband radius increases. In this paper a simple procedure for designing wideband two-dimensional FIR filters with circular symmetry is presented. The procedure is based on combining the low-order original McClellan frequency transformation with a decimation of the resulting 2-D filter impulse response. Conditions to be met by the one-dimensional prototype filter are derived along with the minimum decimation factor required in the design. For given specifications with circular symmetry, it is shown that a solution can always be derived using the fixed low-order transformation.
Soft decoding and synchronization of arithmetic codes: application to image transmission over noisy channels

This paper addresses the issue of robust and joint source-channel decoding of arithmetic codes. We first analyze dependencies between the variables involved in arithmetic coding by means of the Bayesian formalism. This provides a suitable framework for designing a soft decoding algorithm that provides high error-resilience. It also provides a natural setting for "soft synchronization", i.e., to introduce anchors favoring the likelihood of "synchronized" paths. In order to maintain the complexity of the estimation within a realistic range, a simple, yet efficient, pruning method is described. The algorithm can be placed in an iterative source-channel decoding structure, in the spirit of serial turbo codes. Models and algorithms are then applied to context-based arithmetic coding widely used in practical systems (e.g., JPEG-2000). Experimentation results with both theoretical sources and with real images coded with JPEG-2000 reveal very good error resilience performances.
Light Field Compression With Homography-Based Low-Rank Approximation

This paper describes a light field compression scheme based on a novel homography-based low-rank approximation method called HLRA. The HLRA method jointly searches for the set of homographies best aligning the light field views and for the low-rank approximation matrices. The light field views are aligned using either one global homography or multiple homographies depending on how much the disparity across views varies from one depth plane to the other. The light field low-rank representation is then compressed using high efficiency video coding (HEVC). The best pair of rank and quantization parameters of the coding scheme, for a given target bit rate, is predicted with a model defined as a function of light field disparity and texture features. The results are compared with those obtained by directly applying HEVC on the light field views restructured as a pseudovideo sequence. The experiments using different datasets show substantial peak signal to noise ratio (PSNR)-rate gain of our compression algorithm, as well as the accuracy of the proposed parameter prediction model, especially for real light fields. A scalable extension of the coding scheme is finally proposed.
Impact of light field compression on focus stack and extended focus images

Light Fields capturing all light rays at every point in space and in all directions contain very rich information about the scene. This rich description of the scene enables advanced image creation capabilities, such as re-focusing or extended depth of field from a single capture. But, it yields a very high volume of data which needs compression. This paper studies the impact of Light Fields compression on two key functionalities: refocusing and extended focus. The sub-aperture images forming the Light Field are compressed as a video sequence with HEVC. A focus stack and the scene depth map are computed from the compressed light field and are used to render an image with an extended depth of field (called the extended focus image). It has been first observed that the Light Field could be compressed with a factor up to 700 without significantly affecting the visual quality of both refocused and extended focus images. To further analyze the compression effect, a dedicated quality evaluation method based on contrast and gradient measurements is considered to differentiate the natural geometrical blur from the blur resulting from compression. As a second part of the experiments, it is shown that the texture distortion of the in-focus regions in the focus stacks is the main cause of the quality degradation in the extended focus and that the depth errors do not impact the extended focus quality unless the light field is significantly distorted with a compression ratio of around 2000:1.
Learning a tree-structured dictionary for efficient image representation with adaptive sparse coding

We introduce a new method, called Tree K-SVD, to learn a tree-structured dictionary for sparse representations, as well as a new adaptive sparse coding method, in a context of image compression. Each dictionary at a level in the tree is learned from residuals from the previous level with the K-SVD method. The tree-structured dictionary allows efficient search of the atoms along the tree as well as efficient coding of their indices. Besides, it is scalable in the sense that it can be used, once learned, for several sparsity constraints. We show experimentally on face images that, for a high sparsity, Tree K-SVD offers better rate-distortion performances than state-of-the-art “flat” dictionaries learned by K-SVD or Sparse K-SVD, or than the predetermined overcomplete DCT dictionary. We also show that our adaptive sparse coding method, used on a tree-structured dictionary to adapt the sparsity per level, improves the quality of reconstruction.
Error-resilient binary multiplexed source codes

The paper addresses the issue of robust transmission of VLC encoded sources over error-prone channels. We have recently introduced a new family of codes, called multiplexed codes. They exploit the fact that real signal compression systems generate sources of information with different levels of priority. Multiplexed codes allow the desynchronization phenomenon to be confined to low priority data while allowing the entropy bound to be reached asymptotically for both (low and high priority) sources. A multiplexing procedure based on an iterative Euclidian decomposition has been proposed. This paper introduces a variant of multiplexed codes, called binary multiplexed codes, together with a very simple multiplexing algorithm that exploits the structure of variable length codetrees. It is shown analytically and experimentally that this family of codes is more error resilient than fixed length codes while reaching the compression efficiency of classical variable length codes.
Object-based Layered Depth Images for improved virtual view synthesis in rate-constrained context

Layered Depth Image (LDI) representations are attractive compact representations for multi-view videos. Any virtual viewpoint can be rendered from LDI by using view synthesis technique. However, rendering from classical LDI leads to annoying visual artifacts, such as cracks and disocclusions. Visual quality gets even worse after a DCT-based compression of the LDI, because of blurring effects on depth discontinuities. In this paper, we propose a novel object-based LDI representation, improving synthesized virtual views quality, in a rate-constrained context. Pixels from each LDI layer are reorganised to enhance depth continuity.
Fast implementation of a l∞- l/1 penalized sparse representations algorithm: applications in image denoising and coding

Sparse representation techniques have become an important tool in image processing in recent years, for coding, de-noising and in-painting purposes, for instance. They generally rely on an lscr<sub>2</sub>-lscr<sub>1</sub> penalized criterion and fast algorithms have been proposed to speed up the applications. We propose to replace the lscr<sub>2</sub>-part of the criterion, which has been chosen both for its easy implementation and its relation to the PSNR quality measure, by a lscr<sub>∞</sub>-part. We present a new fast way to minimize a lscr<sub>∞</sub>- lscr<sub>1</sub> penalized criterion and assess its potential benefits for image De-noising and coding.
Performance analysis and recursive syndrome decoding of DFT codes for bursty erasure recovery

Packet loss is a common phenomenon in current image processing (IP) networks. Current network protocols manage this problem by retransmitting the lost packets. However, the delay due to the retransmission of the lost packets may be inappropriate for many real-time applications. Therefore, a better approach to resolve this problem is to recover the lost data from the received packets using some error control coding scheme. The conventional error control coding is based on a tandem source and channel coding framework, where the message signal is first compactly represented using a rate-distortion criterion, and then, the error correcting bits are appended to it. As an alternative, joint source-channel codes that can perform as good as, or better than, the tandem source and channel coding are being considered. In particular, lowpass discrete Fourier transform (DFT) codes have been studied as joint source-channel codes. This paper is dedicated to the analysis of lowpass DFT codes with bursty erasures. The analysis aims at studying the performance of lowpass DFT codes when consecutive samples of a DFT codevector are erased due to packet losses. It is known that lowpass DFT codes are highly sensitive to quantization error when there are consecutive erasures. This phenomenon is studied in the context of syndrome decoding. Bursty erasures give rise to a syndrome-decoding matrix that has very large elements depending on the code parameters and the burst length. The size 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 lowpass DFT codes is analyzed and then applied to the case of bursty erasures. The paper also proposes a recursive syndrome decoding algorithm to improve on the normal syndrome decoding of lowpass DFT codes in the case of bursty erasures. The proposed algorithm aims at recovering the quantized values of the erased samples based on a cost computed using the statistics of the codevector and of the quantization error.
Compact and coherent dictionary construction for example-based super-resolution

This paper presents a new method to construct a dictionary for example-based super-resolution (SR) algorithms. Example-based SR relies on a dictionary of correspondences of low-resolution (LR) and high-resolution (HR) patches. Having a fixed, prebuilt, dictionary, allows to speed up the SR process; however, in order to perform well in most cases, we need to have big dictionaries with a large variety of patches. Moreover, LR and HR patches often are not coherent, i.e. local LR neighborhoods are not preserved in the HR space. Our designed dictionary learning method takes as input a large dictionary and gives as an output a dictionary with a “sustainable” size, yet presenting comparable or even better performance. It firstly consists of a partitioning process, done according to a joint k-means procedure, which enforces the coherence between LR and HR patches by discarding those pairs for which we do not find a common cluster. Secondly, the clustered dictionary is used to extract some salient patches that will form the output set.
2-channel oversampled filter banks as joint source-channel codes for erasure channels

This paper studies oversampled filter banks (OFB) for robust transmission of image/video signals over erasure channels. The dependencies introduced by the OFB-based frame expansion are first analyzed in the z domain. This analysis paves the way to the design of an algorithm for message reconstruction in presence of erasures and quantization noise. Conditions for recovery from some typical erasure patterns like bursty erasures and periodic erasure patterns are derived for both orthogonal and biorthogonal filter banks. For some erasure patterns the equivalent transform coder forms a frame, hence the reconstruction could be based on operating its pseudo inverse, however at the expense of high computational cost. Alternately, the erased samples can first be reconstructed from the received ones and then signal space projection can be applied. Since in practical systems the codevectors are quantized before transmission, we study the effect of quantization noise on the reconstructed signal. The theoretical results are validated for a number of erasure patterns with image signals.