nils-herrmann/clean_scirepeval huggingface dataset:
Quantized frame expansions based on tree-structured oversampled filter banks for erasure recovery

The paper studies quantized frame expansions based on tree-structured oversampled filter banks for robust transmission of multimedia signals over erasure channels. The dependencies between the expansion coefficients introduced by the oversampled tree-structured filter banks are analysed in the z-transform domain. The capacity of correction in the presence of different erasure patterns then follows naturally. This analysis leads to the design of a reconstruction algorithm in the presence of erasures, and first without quantization noise. The impact of quantization error on the reconstruction error is then studied. For the tree-structured oversampled filter banks, we prove that reconstruction from a larger class of erasure patterns is possible as compared to regular oversampled filter banks. The reconstruction mean square error is significantly lower in the case of the tree-structured filter bank as compared to regular oversampled filter banks (Motwani, R. and Guillemot, C., 2003) and block transforms, even if the erasures are bursty in nature.
Soft decoding of multiple descriptions

A multiple description scalar quantization (MDSQ) based coding system can be regarded as a source coder (quantizer) followed by a channel coder, i.e. the combination of index and codeword assignment. The redundancy, or the correlation between the descriptions, is controlled by the number of diagonals covered by the index assignment. In this paper, we analyse dependencies between the variables involved in the MDSQ coding chain and design an estimation strategy making use of part of the global model of dependencies at each time. Inference of the hidden states of the connected models is done by applying belief propagation principles on the resulting Bayesian network. We try to evidence the most appropriate form of redundancy one should introduce in the context of variable length code compressed streams in order to fight against de-synchronizations when impaired by channel noise.
Improved Interlayer Prediction for Scalable Video Coding

In this paper, we present a novel technique for efficient compression of enhancement layers in scalable video coding (SVC). First we propose an improved interlayer prediction scheme which exploits the inherent redundancy of the underlying Laplacian pyramid with nonbiorthogonal filters. Secondly we introduce an orthogonal transform in parallel with the current 4×4 transform to improve the coding efficiency of the enhancement layer further. The improved prediction and the transform are implemented in the SVC reference software JSVM 4.0 as additional prediction modes. Experimental results demonstrate coding gains up to 1 dB for I pictures, and up to 0.7 dB for both I and P pictures, over a current implementation.
Adaptive re-quantization for high dynamic range video compression

High Dynamic Range (HDR) images contain more intensity levels than traditional image formats. Instead of 8 or 10 bit integers, floating point values are generally used to represent the pixel data. To extend the use of existing video codecs such as HEVC to HDR floating point video sequences, we propose a method that converts the floating point data and reduces the bit depth of input images with minimal loss. Several variants of the method are proposed. They are adapted to different quality requirements. In particular, near lossless compression is addressed.
Perception-oriented video coding based on foveated JND model

The ultimate goal of video compression is to provide best visual quality of the video source by using minimum bit budget. To achieve this target, spatial, temporal and perceptual redundancies should be exploited and removed such that the video source could be compressed efficiently. In this paper, we present how to improve the video compression performance by using a foveated just-noticeable-distortion model. Since the perceptual acuity decreases with the increased eccentricity, a foveation model is developed to exploit the global perceptual redundancy in addition to the spatial and temporal just-noticeable-distortion models. The foveated just-noticeable-distortion model is incorporated into the H.264/AVC video coding system. As demonstrated in the subjective tests, applying the proposed model in video coding can improve visual quality.
Filter bank frame expansions as joint source-channel codes for robustness to erasures

This paper studies oversampled filter banks (OFBs) for robust transmission of multimedia signals over erasure channels. OFBs implement frame expansions, which is an overcomplete expansion of the signal. The 2-channel orthonormal OFB provides a representation of the signal using two orthonormal basis functions. In the event of erasures, conditions under which it is possible to reconstruct the erased values from the received data are studied. This study paves way for message reconstruction in the presence of erasures and quantization noise. Conditions for recovery from some typical erasure patterns like bursty erasures are obtained. We prove that it is not possible to obtain perfect reconstruction if a burst of consecutive erasures of length more than 2 times the filter length occurs.
Rate-adaptive codes for the entire Slepian-Wolf region and arbitrarily correlated sources

In this paper, we focus on the design of distributed source codes that can achieve any point in the Slepian-Wolf (SW) region and at the same time adapt to any correlation between the sources. A practical solution based on punctured accumulated LDPC codes extended to the non asymmetric case is described. The approach allows flexible rate allocation to the two sources with a gap of 0.0677 bits with respect to the minimum achievable rate.
Cosine-modulated wavelets: new results on design of arbitrary length filters and optimization for image compression

This paper presents a procedure for designing cosine-modulated wavelet filters of arbitrary length applicable to image compression. Both criteria of frequency selectivity and of maximum coding gain are first considered in the design. In order to obtain cosine modulated bases of compactly supported M-band wavelets, additional regularity constraints are then imposed on the filters. The approach presented here allows to derive arbitrary length solutions with high stopband attenuation even in the presence of additional constraints such as regularity. The impact of the different parameters of the wavelet filters on the compression efficiency is evaluated. These filter solutions are also tested comparatively to wavelet packets in a subband coding scheme using lattice vector quantization optimized in the rate-distortion sense.<<ETX>>
Exact reconstruction filter banks using cosine modulation: matrix formalization for arbitrary length prototype filters

This paper describes generalized frequency domain matrix formalizations for two families of filter banks using different structures of polyphase component decomposition that are valid for prototype filters of arbitrary lengths and for an arbitrary number of channels M. In each case, closed form expressions of the polyphase components of the FIR prototype filter are provided and necessary and sufficient conditions on these polyphase components so that the analysis/synthesis system satisfies the perfect reconstruction condition are derived. It is shown that solutions with arbitrary lengths of the form mM-R can be obtained. For both structures, the relations between the polyphase component pairs lead naturally to fast computation algorithms, with very regular structures and low arithmetic complexity.<<ETX>>
A Hybrid Encoder/Decoder Rate Control for Wyner-Ziv Video Coding with a Feedback Channel

This paper describes a hybrid coder/decoder rate control for a Wyner-Ziv video coding scheme with a feedback channel. The approach is first based on a method to estimate at the encoder the minimum rate required for the Slepian-Wolf (SW) encoded data. This estimation makes use only of the Lapacian correlation model. The decoder then estimates the bit error rate (BER) for each decoded bit plane from likelihood ratios computed at the output of the SW decoder. The robustness of the BER estimation is further improved by the use of an error detection mechanism based on a cyclic redundancy checksum. Experimental results show that rate-distortion performances are comparable to those which would be obtained by computing the Hamming distance between the original and the decoded data. In addition the hybrid coder/decoder rate control reduces the decoding complexity as well as the usage of the return channel.
Scalable light field compression scheme using sparse reconstruction and restoration

This paper describes a light field scalable compression scheme based on the sparsity of the angular Fourier transform of the light field. A subset of sub-aperture images (or views) is compressed using HEVC as a base layer and transmitted to the decoder. An entire light field is reconstructed from this view subset using a method exploiting the sparsity of the light field in the continuous Fourier domain. The reconstructed light field is enhanced using a patch-based restoration method. Then, restored samples are used to predict original ones, in a SHVC-based SNR-scalable scheme. Experiments with different datasets show a significant bit rate reduction of up to 24% in favor of the proposed compression method compared with a direct encoding of all the views with HEVC. The impact of the compression on the quality of the all-in-focus images is also analyzed showing the advantage of the proposed scheme.
White lenslet image guided demosaicing for plenoptic cameras

Most modern cameras use a color filter array on their sensor in order to capture color images. This array is composed of red, green and blue filters and so, each pixel on the sensor lacks two color channels which can be retrieved by a process called demosaicing. In this paper, we propose a new demosaicing method for plenoptic cameras. This type of cameras has become a growing trend and their captured raw images have a particular lenslet structure which must be taken into account to retrieve the sub-aperture images which compose the light field. First, we analyze and describe the flaws of the state-of-the-art light field decoding pipeline. To better identify the different sources of artifacts, our analysis is performed by generating ideal lenslet images from synthetic light fields and use them as input of the decoding pipeline. Then, we detail a new method of demosaicing based on the provided white lenslet images serving as guide. Furthermore, we show that this kind of guided interpolation can be useful on other steps of the decoding pipeline. Finally, the quality of the resulting sub-aperture images is assessed for both synthetic and real light fields using visual comparisons as well as objective metrics.
Face Hallucination Using Linear Models of Coupled Sparse Support

Most face super-resolution methods assume that low- and high-resolution manifolds have similar local geometrical structure; hence, learn local models on the low-resolution manifold (e.g., sparse or locally linear embedding models), which are then applied on the high-resolution manifold. However, the low-resolution manifold is distorted by the one-to-many relationship between low- and high-resolution patches. This paper presents the Linear Model of Coupled Sparse Support (LM-CSS) method, which learns linear models based on the local geometrical structure on the high-resolution manifold rather than on the low-resolution manifold. For this, in a first step, the low-resolution patch is used to derive a globally optimal estimate of the high-resolution patch. The approximated solution is shown to be close in the Euclidean space to the ground truth, but is generally smooth and lacks the texture details needed by the state-of-the-art face recognizers. Unlike existing methods, the sparse support that best estimates the first approximated solution is found on the high-resolution manifold. The derived support is then used to extract the atoms from the coupled low- and high-resolution dictionaries that are most suitable to learn an up-scaling function for every facial region. The proposed solution was also extended to compute face super-resolution of non-frontal images. Extensive experimental results conducted on a total of 1830 facial images show that the proposed method outperforms seven face super-resolution and a state-of-the-art cross-resolution face recognition method in terms of both quality and recognition.
Inter-Layer Prediction of Color in High Dynamic Range Image Scalable Compression.

This paper presents a color inter-layer prediction (ILP) method for scalable coding of high dynamic range (HDR) video content with a low dynamic range (LDR) base layer. Relying on the assumption of hue preservation between the colors of an HDR image and its LDR tone mapped version, we derived equations for predicting the chromatic components of the HDR layer given the decoded LDR layer. Two color representations are studied. In a first encoding scheme, the HDR image is represented in the classical Y'CbCr format. In addition, a second scheme is proposed using a colorspace based on the CIE u'v' uniform chromaticity scale diagram. In each case, different prediction equations are derived based on a color model ensuring the hue preservation. Our experiments highlight several advantages of using a CIE u'v'-based colorspace for the compression of HDR content, especially in a scalable context. In addition, our ILP scheme using this color representation improves on the state-of-the-art ILP method, which directly predicts the HDR layer u'v' components by computing the LDR layers u'v' values of each pixel.
Neighbor embedding based single-image super-resolution using Semi-Nonnegative Matrix Factorization

This paper describes a novel method for single-image super-resolution (SR) based on a neighbor embedding technique which uses Semi-Nonnegative Matrix Factorization (SNMF). Each low-resolution (LR) input patch is approximated by a linear combination of nearest neighbors taken from a dictionary. This dictionary stores low-resolution and corresponding high-resolution (HR) patches taken from natural images and is thus used to infer the HR details of the super-resolved image. The entire neighbor embedding procedure is carried out in a feature space. Features which are either the gradient values of the pixels or the mean-subtracted luminance values are extracted from the LR input patches, and from the LR and HR patches stored in the dictionary. The algorithm thus searches for the K nearest neighbors of the feature vector of the LR input patch and then computes the weights for approximating the input feature vector. The use of SNMF for computing the weights of the linear approximation is shown to have a more stable behavior than the use of LLE and lead to significantly higher PSNR values for the super-resolved images.
Source Modeling for Distributed Video Coding

This paper studies source and correlation models for distributed video coding (DVC). It first considers a two-state HMM, i.e., a Gilbert-Elliott process, to model the bit-planes produced by DVC schemes. A statistical analysis shows that this model allows us to accurately capture the memory present in the video bit-planes. The achievable rate bounds are derived for these ergodic sources, first assuming an additive binary symmetric correlation channel between the two sources. These bounds show that a rate gain can be achieved by exploiting the sources memory with the additive BSC model. A Slepian-Wolf decoding algorithm which jointly estimates the sources and the source model parameters is then described. Simulation results show that the additive correlation model does not always fit well with the correlation between the actual video bit-planes. This has led us to consider a second correlation model (the predictive model). The rate bounds are then derived for the predictive correlation model in the case of memory sources, showing that exploiting the source memory does not bring any rate gain and that the noise statistic is a sufficient statistic for the MAP decoder. We also evaluate the rate loss when the correlation model assumed by the decoder is not matched to the true one. An a posteriori estimation of the correlation channel has hence been added to the decoder in order to use the most appropriate correlation model for each bit-plane. The new decoding algorithm has been integrated in a DVC decoder, leading to a rate saving of up to 10.14% for the same PSNR, with respect to the case where the bit-planes are assumed to be memoryless uniform sources correlated with the SI via an additive channel model.
Distributed video compression: Basics, research problems, applications

Distributed source coding has emerged as an enabling technology for sensor networks. It refers to the compression of correlated signals captured by different sensors which do not communicate between themselves. Distributed source coding finds its foundation in the seminal work of Slepian-Wolf (1973) and Wyner-Ziv (1976). The proof of the Slepian-Wolf W theorem is based on random binning, which is non-constructive, i.e., it does not reveal how practical code design should be done. In 1974, Wyner suggested the use of parity check codes to approach the corner points of the Slepian-Wolf rate region. It is only recently that practical solutions based on channel capacity-achieving codes (block codes, turbo codes or LDPC codes) have been explored for applications ranging from video compression, resilient video transmission, to minimization of transmit energy in sensor networks. Video compression, as well as scalable video compression, can be recast into a distributed source coding framework leading to distributed video coding schemes targeting mainly low coding complexity and error resilience functionalities. Correlated samples (pixels or transform coefficients) from different frames are regarded as outputs of different sensors. However, the application of the Wyner-Ziv principles to video compression is not straightforward and requires solving a number of issues. This article presents the underlying theory, the latest developments of distributed video compression and some of the research trends in the area.
Hybrid sender and receiver driven rate control in multicast layered video transmission

Layered coding is often proposed as a solution for rate-based congestion control of video transmission in heterogeneous environments. The problem addressed more specifically here is the design of a responsive mechanism for rate allocation in each layer, that would guarantee the best bandwidth usage for all the receivers. One key features, in contrast with existing solutions, are an optimization of the overall PSNR seen by the receivers instead of a measure of goodput. A second key feature is that the optimization does not have to be carried out on the network nodes but only by the sender. Only feedback mergers have to be supported by some nodes in the network leading to a clustering of the receivers according to their bottleneck rate of the rate they are allowed following some TCP-friendliness criteria.
Synchronization Recovery and State Model Reduction for Soft Decoding of Variable Length Codes

Variable length codes (VLCs) exhibit loss of synchronization problems when transmitted over noisy channels. Trellis decoding techniques based on Maximum A Posteriori (MAP) estimators are often used to minimize the error rate on the estimated sequence. If the number of symbols and/or bits transmitted is known by the decoder, termination constraints can be incorporated in the decoding process. All the paths in the trellis which do not lead to a valid sequence length are suppressed. This correspondence presents an analytic method to assess the expected error resilience of a VLC when trellis decoding with a sequence length constraint is used. The approach is based on the computation, for a given code, of the amount of information brought by the constraint. It is then shown that this quantity is not significantly altered by appropriate trellis states aggregation. This proves that the performance obtained by running a length-constrained Viterbi decoder on aggregated state models approaches the one obtained with the bit/symbol trellis, with a significantly reduced complexity. It is then shown that the complexity can be further decreased by projecting the state model on two state models of reduced size
Robust face hallucination using quantization-adaptive dictionaries

Existing face hallucination methods are optimized to super-resolve uncompressed images and are not able to handle the distortions caused by compression. This work presents a new dictionary construction method which jointly models both distortions caused by down-sampling and compression. The resulting dictionaries are then used to make three face super-resolution methods more robust to compression. Experimental results show that the proposed dictionary construction method generates dictionaries which are more representative of the low-quality face image being restored and makes the extended face hallucination methods more robust to compression. These experiments demonstrate that the proposed robust face hallucination methods can achieve Peak Signal-to-Noise Ratio (PSNR) gains between 2-4.48dB and recognition improvement between 2.9-8.1% compared with the low-quality image and outperforming traditional super-resolution methods in most cases.
Error-resilient first-order multiplexed source codes: performance bounds, design and decoding algorithms

This paper describes a new family of error-resilient variable-length source codes (VLCs). The codes introduced can be regarded as generalizations of the multiplexed codes described by Je/spl acute/gou and Guillemot. They allow to exploit first-order source statistics while, at the same time, being resilient to transmission errors. The design principle consists of creating a codebook of fixed-length codewords (FLCs) for high-priority information and in using the inherent codebook redundancy to describe low-priority information. The FLC codebook is partitioned into equivalence classes according to the conditional probabilities of the high-priority source. The error propagation phenomenon, which may result from the memory of the source coder, is controlled by choosing appropriate codebook partitions and index assignment strategies. In particular, a context-dependent index assignment strategy, called crossed-index assignment, is described. For the symbol error rate criterion, the approach turns out to maximize the cardinality of a set of codewords, called the code kernel, offering synchronization properties. The decoder resynchronization capability is shown to be further increased by periodic use of memoryless multiplexed codes. Theoretical and practical performances in terms of compression efficiency and error resilience are analyzed.
An SVD Based Transform for Critical Representation of Laplacian Pyramids

Recently the Laplacian pyramid (LP) has come to prominence as a tool for obtaining spatial scalability in the context of scalable image and video compression. In this paper, we present a critical representation scheme for the LP. The representation is obtained by transforming the detail signals to lower dimensional representations. Unlike the wavelet transform, the proposed transform depends on the decimation and the interpolation filters used for the classical LP. Simulation results suggest that higher compression ratios can be achieved with the critical representation than with the standard LP with usual or frame based reconstructions.