nils-herrmann/clean_scirepeval huggingface dataset:
Wavelet packet coding with jointly optimized lattice vector quantization and data rate allocation

Describes a new coding scheme based on a joint optimization of signal decomposition and of lattice vector quantizers. The adaptive signal decomposition uses wavelet packets that allow to best match the decomposition to the signal nonstationary characteristics. In order to obtain a good tradeoff between frequency and spatial localization, nonstationary wavelet packets based on filters varying at each stage of the tree structured decomposition are considered. The complete tree is pruned into the best basis subtree which minimizes a perceptually weighted distortion for a given bit rate. Each subband is then quantized using data rate constrained lattice vector quantizers. The choice of the quantizer is usually determined by the minimization of the overall mean squared error of the reconstructed signal under the constraint of a given bit rate [Ramchandran and Vetterli, 1993]. However, this procedure is not optimum in terms of visual quality. A procedure of quantization based on the minimization of perceptually weighted distortion of each frequency band is introduced. The results show a very significant improvement both in terms of SNR and of visual quality versus the JPEG algorithm.<<ETX>>
Symmetric delay factorization: generalized framework for paraunitary filter banks

The symmetric delay factorization (SDF) was introduced to synthesize linear-phase paraunitary filter banks (LPPUFBs) with uniform order (i.e., filter length equal to NM for arbitrary N) and real-valued coefficients. The SDF presents the advantage of decomposing the polyphase transfer matrix (PTM) into only orthogonal matrices, even at the boundary of finite-duration signals, simplifying significantly the design of time-bounded filter banks (TBFBs) or of time-varying filter banks (TVFBs). However, the symmetric delay factorization applies only to LPPUFBs. On the other hand, lattice structures, as well as finite-size lattice structures, are proposed for classes of nonlinear-phase paraunitary filter banks, as the modulated lapped transform (MLT) and the extended tapped transform (ELT). This paper describes a new minimal and complete symmetric delay factorization valid for a larger class of paraunitary filter banks, encompassing paraunitary cosine modulated filter banks, with nonlinear phase basis functions, as well as for a set of LPPUFBs including the linear-phase lapped orthogonal transforms (LOTs) and the generalized tapped orthogonal transforms (GenLOTs). The derivations for filter banks with even and odd numbers of channels are formulated in a unified form. This approach opens new perspectives in the design of time-varying filter banks used for image and video compression, especially in the framework of region or object-based coding.
Distributed coding using punctured quasi-arithmetic codes for memory and memoryless sources

This paper considers the use of punctured quasi-arithmetic (QA) codes for the Slepian-Wolf problem. These entropy codes are defined by finite state machines for memory-less and first-order memory sources. Puncturing an entropy coded bit-stream leads to an ambiguity at the decoder side. The decoder makes use of a correlated version of the original in order to remove this ambiguity. A complete DSC scheme based on QA encoding with side information at the decoder is presented. The proposed scheme is adapted to memoryless and first-order memory sources. Simulation results reveal that the proposed scheme is efficient in terms of decoding performance for short sequences compared to well-known DSC using channel codes.
Image compression using the Iteration-Tuned and Aligned Dictionary

We present a new, block-based image codec based on sparse representations using a learned, structured dictionary called the Iteration-Tuned and Aligned Dictionary (ITAD). The question of selecting the number of atoms used in the representation of each image block is addressed with a new, global (image-wide), rate-distortion-based sparsity selection criterion. We show experimentally that our codec outperforms JPEG2000 in both quantitative evaluations (by 0.9 dB to 4 dB) and qualitative evaluations.
TCP-compatible rate control for FGS layered multicast video transmission based on a clustering algorithm

This paper addresses the problem of rate and congestion control for video transmission in large multicast groups. A classification of the receivers is performed according to a predefined similarity measure (e.g. packet loss rate observed or TCP-friendly bandwidth share). The RTCP receiver reports are then filtered accordingly. The classification of receivers done by feedback aggregation agents helps in solving the feedback implosion problem in large multicast groups. The clustering and feedback aggregation mechanisms are coupled with an FGS layered video coding system in which the number of layers as well as their respective rates are adapted dynamically to aggregated feedback. The algorithm has been validated via NS 2 simulations.
Image Prediction Based on Neighbor-Embedding Methods

This paper describes two new intraimage prediction methods based on two data dimensionality reduction methods: nonnegative matrix factorization (NMF) and locally linear embedding. These two methods aim at approximating a block to be predicted in the image as a linear combination of k-nearest neighbors determined on the known pixels in a causal neighborhood of the input block. Variable k can be seen as a parameter controlling some sort of sparsity constraints of the approximation vector. The impact of this parameter as well as of the nonnegativity and sum-to-one constraints for the addressed prediction problem has been analyzed. The prediction and RD performances of these two new image prediction methods have then been evaluated in a complete image coding-and-decoding algorithm. Simulation results show gains up to 2 dB in terms of the PSNR of the reconstructed signal after coding and decoding of the prediction residue when compared with H.264/AVC intraprediction modes, up to 3 dB when compared with template matching, and up to 1 dB when compared with a sparse prediction method.
Region-Based Prediction for Image Compression in the Cloud

Thanks to the increasing number of images stored in the cloud, external image similarities can be leveraged to efficiently compress images by exploiting inter-images correlations. In this paper, we propose a novel image prediction scheme for cloud storage. Unlike current state-of-the-art methods, we use a semi-local approach to exploit inter-image correlation. The reference image is first segmented into multiple planar regions determined from matched local features and super-pixels. The geometric and photometric disparities between the matched regions of the reference image and the current image are then compensated. Finally, multiple references are generated from the estimated compensation models and organized in a pseudo-sequence to differentially encode the input image using classical video coding tools. Experimental results demonstrate that the proposed approach yields significant rate-distortion performance improvements compared with the current image inter-coding solutions such as high efficiency video coding.
Super-resolution using neighbor embedding of back-projection residuals

In this paper we present a novel algorithm for neighbor embedding based super-resolution (SR), using an external dictionary. In neighbor embedding based SR, the dictionary is trained from couples of high-resolution and low-resolution (LR) training images, and consists of pairs of patches: matching patches (m-patches), which are used to match the input image patches and contain only low-frequency content, and reconstruction patches (r-patches), which are used to generate the output image patches and actually bring the high-frequency details. We propose a novel training scheme, where the m-patches are extracted from enhanced back-projected interpolations of the LR images and the r-patches are extracted from the back-projection residuals. A procedure to further optimize the dictionary is followed, and finally nonnegative neighbor embedding is considered at the SR algorithm stage. We consider singularly the various elements of the algorithm, and prove that each of them brings a gain on the final result. The complete algorithm is then compared to other state-of-the-art methods, and its competitiveness is shown.
Robust and fast non asymmetric distributed source coding using turbo codes on the syndrome trellis

We consider the distributed compression of two (binary memoryless) correlated sources and propose a unique codec that can reach any point in the Slepian-Wolf region. In a previous method based on channel codes, the decoder multiply the compressed data by an inverse submatrix of the code. This multiplication presents two drawbacks. First, if turbo codes are used, the submatrix has no periodic structure s.t. the whole inverse has to be stored and no fast implementation exists for the multiplication. Second, this multiplication may lead to error propagation. In this paper, we propose a method that is both robust and fast.
Trellis state aggregation for soft decoding of variable length codes

This paper describes a new set of state models for soft decoding of variable length codes. A single parameter T allows to trade complexity against estimation accuracy. The extrema choices for this parameter lead respectively to construct the well-known bit-level and bit/symbol trellises. For a proper choice of the parameter T, the results obtained by running a BCJR or Viterbi estimation algorithm on the proposed state models are close to those obtained with the optimum state model. The complexity is however significantly reduced. It can be further decreased by projecting the state model on two state models of reduced size, and by combining their decoding results. This combination is shown to be optimal for the Viterbi algorithm.
Compression of Laplacian Pyramids Through Orthogonal Transforms and Improved Prediction

Scalable representation of visual signals, such as image and video signals, has become a subject of active research since early 1980s. Scalability allows the adaptation of the bit rate and/or the resolution of the transmitted data to the network bandwidth and/or the rendering capability of the receiving device. For many years, spatial scalability has been achieved through wavelets, but recently the Laplacian pyramid (LP) has become an alternative choice because of reduced aliasing in the lower resolutions. In this paper, we focus on the coding efficiency of the LP with a view to transmitting it over a communication channel. In particular, we aim to improve the compression efficiency of the LP detail layers through improved interlayer prediction and orthogonal spatial transforms. First, we consider an LP in the open-loop configuration and propose to improve its rate-distortion performance by compressing it to a critically sampled representation. We derive four different orthogonal spatial transforms from the upsampling and downsampling filters that can achieve this representation, and apply them on the detail layers. The application of these transforms to the detail layers renders a fixed number of transform coefficients either zero or redundant, thus making their transmission unnecessary. Then we consider the compression of an LP in the closed-loop configuration through similar spatial transforms. Because of the introduction of quantization in the prediction loop, these spatial transforms applied on the detail layers do not produce the same number of zero or redundant transform coefficients as in the open-loop case. Nevertheless, the insight obtained from the open-loop coding leads us to enhance the interlayer prediction, and the subsequent application of the spatial transforms to the new detail layers aims to achieve better energy compaction.
Perceptually-Friendly H.264/AVC Video Coding Based on Foveated Just-Noticeable-Distortion Model

Traditional video compression methods remove spatial and temporal redundancy based on the signal statistical correlation. However, to reach higher compression ratios without perceptually degrading the reconstructed signal, the properties of the human visual system (HVS) need to be better exploited. Research effort has been dedicated to modeling the spatial and temporal just-noticeable-distortion (JND) based on the sensitivity of the HVS to luminance contrast, and accounting for spatial and temporal masking effects. This paper describes a foveation model as well as a foveated JND (FJND) model in which the spatial and temporal JND models are enhanced to account for the relationship between visibility and eccentricity. Since the visual acuity decreases when the distance from the fovea increases, the visibility threshold increases with increased eccentricity. The proposed FJND model is then used for macroblock (MB) quantization adjustment in H.264/advanced video coding (AVC). For each MB, the quantization parameter is optimized based on its FJND information. The Lagrange multiplier in the rate-distortion optimization is adapted so that the MB noticeable distortion is minimized. The performance of the FJND model has been assessed with various comparisons and subjective visual tests. It has been shown that the proposed FJND model can increase the visual quality versus rate performance of the H.264/AVC video coding scheme.
Epitomic image factorization via neighbor-embedding

We describe a novel epitomic image representation scheme that factors a given image content into a condensed epitome and a low-resolution image to reduce the memory space for images. Given an input image, we construct a condensed epitome such that all image patches can successfully be reconstructed from the factored representation by means of an optimized neighbor-embedding strategy. Under this new scope of epitomic image representations aligned with the manifold sampling assumption, we end up a more generic epitome learning scheme with increased optimality, compactness, and reconstruction stability. We present the performance of the proposed method for image and video up-scaling (super-resolution) while extensions to other image and video processing are straightforward.
Indexing algorithms for Zn, An, Dn, and Dn++ lattice vector quantizers

This paper describes vector indexing algorithms valid for a large class of lattices (Z/sub n/, A/sub n/, D/sub n/, and D/sub n//sup ++/ including as special cases the Gosset (E/sub 8/) and Barnes-Wall (/spl Lambda//sub 16//sup n/) lattices), widely used in audio-visual signal compression. The indexing mechanism relies on reverse lexicographic ordering of the vectors, in classes of equivalence defined as sets of vectors obtained by "signed" permutations of the components of initial vectors called leaders. The approach makes it possible to trade the size of the lookup tables-or codebooks-for arithmetic operations. The reduction of codebook sizes leads in turn to reduced encoder and decoder complexities. Pursuing the goal of best tradeoff between storage requirements and arithmetic complexity, two algorithms, based on the proposed indexing mechanisms, allowing "on-the-fly" generation of adaptive portions of codebooks are then described. These algorithms make it possible to overcome the problems of lattice truncating usually encountered in lattice vector quantization (LVQ). Combined with product codes, these indexing techniques lead to increased compression performances.
Oriented wavelet transform on a quincunx pyramid for image compression

This paper presents a new image coding technique based on oriented ID multiscale decompositions on a quincunx sampling grid. A transform obtained by adapting the lifting steps of a ID wavelet transform along a local orientation is described. A quad-tree structure is used to efficiently code the orientation map, and optimized in the rate-distortion sense. A gain of up to 1.3 dB over the separable wavelet transform is observed for a set of images, with the rate estimated from the stationary entropy of the subbands.
Gradient-Based Tone Mapping for Rate-Distortion Optimized Backward-Compatible High Dynamic Range Compression

This paper addresses the problem of designing a global tone mapping operator for rate distortion optimized backward compatible compression of high dynamic range (HDR) images. We address the problem of tone mapping design for two different use cases leading to two different minimization problems. The first problem considered is the minimization of the distortion on the reconstructed HDR signal under a rate constraint on the standard dynamic range (SDR) layer. The second problem remains the same minimization with an additional constraint to preserve a good quality for the SDR signal. Both the distortion and rate are expressed as a function of the spatial gradient in HDR images. Experiments show that the proposed rate and distortion models based on the HDR image gradient accurately predict the real image rate and distortion measures. Experimental results show that for the first minimization, the optimal rate-distortion performances are achieved, and that the second optimization yields the best tradeoff between rate-distortion performance and quality preservation of the SDR signal.
Rate-distortion optimization of a tone mapping with SDR quality constraint for backward-compatible high dynamic range compression

This paper addresses the problem of designing a global tone mapping operator for rate-distortion optimized backward compatible compression of HDR images. We consider a two layer coding scheme in which a base SDR layer is coded with HEVC, inverse tone mapped and subtracted from the input HDR signal to yield the enhancement HDR layer. The tone mapping curve design is formulated as the minimization of the distortion on the reconstructed HDR signal under the constraint of a total rate cost on both layers, while preserving a good quality for the SDR signal. We first demonstrate that the optimum tone mapping function only depends on the rate of the base SDR layer and that the minimization problem can be separated in two consecutive minimization steps. Experimental results show that the proposed tone mapping optimization yields the best trade-off between rate-distortion performance and quality preservation of the coded SDR.
Epitome inpainting with in-loop residue coding for image compression

This paper describes a novel image coding scheme based on epitome inpainting. An epitome containing a factorized texture representation of the image is first coded and transmitted. The decoded epitome is then inpainted by propagating its structure and texture with extended H.264 Intra directional prediction modes and advanced neighbor embedding methods. The blocks to be inpainted are processed according to an adaptive scanning order which adapts to the image content and gives a higher priority to the blocks having strong structures. The inpainting step has been incorporated in the coding loop of an H.264 encoder, leading to a bitrate saving of up to 48.12% with respect to H.264 intra.
Improved Intra Prediction for H.264/AVC Scalable Extension

This paper presents an improved intra prediction scheme for H.264/AVC scalable extension. First, a more flexible intra prediction is realized by introducing a sub-macroblock (sub-MB) level inter-layer intra prediction. Second, the upsampled reconstructed base layer helps to reduce the number of candidate modes for the spatial intra prediction. Finally, the neighboring pixels of the reconstructed enhancement layer are used to improve the quality of the reference from the base layer by a simple cross boundary Alter. Our scheme improves the PSNR up to 0.53 dB when compared with the JSVM standard implementation for all intra coding.
Data-rate constrained lattice vector quantization: a new quantizing algorithm in a rate-distortion sense

This paper describes an image coding scheme using lattice vector quantizers where the emphasis is put on a new lattice vector quantization approach. The adaptive signal decomposition uses wavelet packets which allow to best match the decomposition to the signal non-stationary characteristics. The quantizers used here are data rate constrained lattice vector quantizers. A classical rate-distortion algorithm [Ramchandran and Vetterli, 1993] based on two nested optimization processes allows to jointly optimize transformation and quantization and is used as a first step in the quantization procedure described here. It allows to define in each subband the lattice spacing (or scaling factor) minimizing the overall distortion for a given bit rate and to choose by a pruning algorithm the best structure of decomposition. An additional procedure developed here allows (by exploiting the lattice properties) to project a vector on a lattice point providing a better rate-distortion tradeoff. In addition, a new partitioning of the vector space allowing to divide the source of vectors into three sub-sources is introduced in order to improve the coding efficiency. In the rate distortion plane, this new quantization procedure brings a significant improvement of about 0.5-0.6 dB with respect to the classical rate-distortion algorithm.
Rate-adaptive turbo-syndrome scheme for Slepian-Wolf Coding

We consider asymmetric distributed source coding (DSC) and propose a new rate-adaptive DSC code that is needed when the correlation varies in time. The proposed scheme is based on the puncturing of the syndromes of a convolutional code. Optimal decoding (under syndrome puncturing) is derived with complexity that grows only linearly with the number of punctured positions. Finally, an error resilient scheme is proposed. Both decoders are applied to the case of turbo-codes.
Distributed coding using punctured quasi-arithmetic codes for memory and memoryless sources

This correspondence considers the use of punctured quasi-arithmetic (QA) codes for the Slepian-Wolf problem. These entropy codes are defined by finite state machines for memoryless and first-order memory sources. Puncturing an entropy coded bit-stream leads to an ambiguity at the decoder side. The decoder makes use of a correlated version of the original message in order to remove this ambiguity. A complete distributed source coding (DSC) scheme based on QA encoding with side information at the decoder is presented, together with iterative structures based on QA codes. The proposed schemes are adapted to memoryless and first-order memory sources. Simulation results reveal that the proposed schemes are efficient in terms of decoding performance for short sequences compared to well-known DSC solutions using channel codes.
Filter bank frame-expansion with erasures-a para-pseudo inverse based reconstruction

In this paper, we propose a decoder which reconstructs signals which are joint-source channel encoded using oversampled filter banks. The decoder is based on operating the para-pseudoinverse corresponding to the cascaded transform function consisting of the source encoder and the erasure channel on the received signal. Hence, the proposed method minimizes the reconstruction mean square error. The decoder consists of a filter bank which can comprise of FIR or IIR filters depending on the erasure channel. Hence the computational complexity of the decoder is also lower compared to earlier proposed techniques. The theoretical results are validated by simulations using image data.