nils-herrmann/clean_scirepeval huggingface dataset:
Layered image/video softcast with hybrid digital-analog transmission for robust wireless visual communication

Due to the mobility of transceivers and the interference from other signals, the condition of a wireless channel may vary drastically and unpredictably. In such scenario, conventional communication systems usually suffer from cliff effect due to the nature of entropy coding and channel coding. The recently proposed SoftCast scheme, on the contrary, achieves smooth quality degradation by employing analog-like transmission, together with efficient decorrelation and power allocation. However, the analog-like transmission in SoftCast is not always efficient in terms of power usage, when compared with digital approaches. In this paper, we propose a layered image/video SoftCast scheme, in which a coarse approximation of the image is coded in a base layer and transmitted in digital way while the remained image details are represented in an enhancement layer and sent out using the SoftCast way. Since a major part of the signal energy is transmitted in the base layer using digital approach, the efficiency of power usage in the enhancement layer is improved remarkably. Experimental results show that the proposed scheme can outperform the original SoftCast scheme remarkably, while still preserving the smooth quality degradation characteristic of SoftCast.
Compression artifact reduction for low bit-rate images based on non-local similarity and across-resolution coherence

This paper proposes a method to estimate coefficients for blocking artifact reduction at low bit rate. Across-resolution coherence that low and high resolution image are similar is introduced to preserve signal continuity. Non-local similarity is used to provide samples for estimation by searching similar blocks of reference block. We have two sources of estimation. One source is exploiting non-local similarity to estimate coefficients of low resolution of decoded image, and interpolating the low resolution image to high resolution. We obtain the coefficients estimation for high resolution image based on the coherence across different resolutions. The other source of estimation is the quantization coefficients. These estimations are fused by their reliability respectively. Experimental results demonstrate that the proposed algorithm outperforms some recently presented methods in terms of both objective and subjective qualities of the reconstruction images.
Image Restoration Using Joint Statistical Modeling in a Space-Transform Domain

This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-fold. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving the image inverse problem is formulated using JSM under a regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split Bregman-based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring, and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.
Image Compressive Sensing Recovery via Collaborative Sparsity

Compressive sensing (CS) has drawn quite an amount of attention as a joint sampling and compression approach. Its theory shows that when the signal is sparse enough in some domain, it can be decoded from many fewer measurements than suggested by the Nyquist sampling theory. So one of the most challenging researches in CS is to seek a domain where a signal can exhibit a high degree of sparsity and hence be recovered faithfully. Most of the conventional CS recovery approaches, however, exploited a set of fixed bases (e.g., DCT, wavelet, and gradient domain) for the entirety of a signal, which are irrespective of the nonstationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor rate-distortion performance. In this paper, we propose a new framework for image compressive sensing recovery via collaborative sparsity, which enforces local 2-D sparsity and nonlocal 3-D sparsity simultaneously in an adaptive hybrid space-transform domain, thus substantially utilizing intrinsic sparsity of natural images and greatly confining the CS solution space. In addition, an efficient augmented Lagrangian-based technique is developed to solve the above optimization problem. Experimental results on a wide range of natural images are presented to demonstrate the efficacy of the new CS recovery strategy.
Denoising and Resource Allocation in Uncoded Video Transmission

The recently revitalized uncoded transmission has shown great potential in handling channel variations and user heterogeneities for wireless video communications. In this paper, we propose to adopt motion-compensated temporal filtering (MCTF) at the sender and denoising techniques at the receiver to fully exploit the temporal and spatial redundancy in video signals and therefore significantly improve the efficiency of uncoded video transmission. Although there are readily applicable MCTF and denoising techniques, integrating them into the uncoded video transmission framework is nontrivial, especially in the case of denoising. To address these challenges, we first propose spatial-domain transmission and cascaded denoising to harness the potential of denoising techniques. Second, we develop novel resource allocation algorithms based on variable-size L-shaped chunk division, which are not only compatible with spatial-domain transmission, but also achieves better energy efficiency than existing schemes based on fixed-size rectangular chunks. Our experimental results show that when transmitting 720p videos, our scheme achieves up to a 3.3 dB gain in video PSNR over the state-of-the-art uncoded transmission scheme SoftCast and up to a 5.3 dB gain over a digital scheme based on robust rate adaptation and H.264 scalable video coding (SVC).
Band-Wise Adaptive Sparsity Regularization for Quantized Compressed Sensing Exploiting Nonlocal Similarity

The theory of compressive sensing (CS) has attracted considerable research interests from signal and image processing communities. And in practice, because of the considerations of data storage and transmission, scalar quantization is necessary to be implemented on the CS measurements. In this paper, we propose an adaptive bandwise sparsity regularization to handle the recovery problem of quantized compressive sensing. The sparsity regularization constraints every patch by using bandwise distribution model in transform domain. In addition, we bring in the quantization cost function to quantify the influence of measurement quantization. Experimental results demonstrate that our CS recovery strategy achieves significant performance improvements over the current state-of-the-art schemes with both unquantized measurements and quantized measurements.
Quad-tree based inter-view motion prediction

As a 3D video extension of Audio Video Coding Standard (AVS), 3D-AVS is being developed to improve the coding efficiency of multi-view video. Since multi-view video is composed of projections of the same scenery from different viewpoints at the same time instant, it contains a large amount of inter-view redundancies. To exploit the inter-view correlation, this paper presents a method to derive the motion parameters for a coding unit (CU) in the dependent view from the already coded inter-view picture. The algorithm is based on quad-tree partitioning, each CU could be recursively split into four sub-CUs of the same size and whether each sub-CU is further split is determined by comparing the derived motion parameters. Experimental results show that the proposed method provides 8.7% BD-rate saving for both video 1 and video 2 in low delay configuration and the BD-rate saving is up to 14.5% and 13.7% on video 1 and video 2 for Balloons. This method has been proposed and adopted into the 3D-AVS standard.
High accuracy sub-pixel image registration under noisy condition

Image registration plays an important role in many image processing applications. A key problem is that the accuracy of registration can be severely affected by noise. This paper presents a sub-pixel registration method for noisy images. In particular, we investigate how to estimate transitional shift to a high precision using the noisy phase data in frequency domain. Based on theoretical analysis, we find that the noise-caused phase change for every frequency component of high signal-to-noise ratio (SNR) can be well approximated by a Gaussian distribution. Furthermore, we show that the reliability of phase data can also be measured by the SNR of corresponding frequency component. A noise-robust registration framework is proposed to utilize high-SNR frequency components adaptively, while masking out the components of SNR lower than a threshold. Experiments demonstrate that the proposed method is superior to existing image registration methods in the presence of noise.
Adaptive Image Deblurring via Tanner Graph Representation and Belief Propagation

In this paper, we propose a deblurring framework based on a factor graph representation of the image and the image formation process. Each pixel is described by a variable node, while the statistical relation among pixels is formulated by two sets of check nodes, describing the local image structures and the image formation process, respectively. Belief propagation is employed to solve the pixel values and it is reduced to mechanisms to generate and fuse predictions for each pixel iteratively. A key work is that we analyzed the origin of ringing artifacts and found that it is due to the propagation of estimation error in previous iterations. We propose a method to estimate the uncertainty in each pixel of previous estimation, which is then used to adapt the generation and fusion of prediction in the next iteration. Experimental results show that the proposed solution can significantly eliminate ringing artifacts without employing any image priors.
Image super-resolution based on adaptive joint distribution modeling

This paper combines an adaptive reconstruction based approach and a learning based technique into an effective scheme for single image super-resolution. Unlike conventional schemes that adopt pre-trained dictionaries to tell the relationship between high-resolution (HR) image and the low-resolution (LR) observation, the proposed method attempts to learn the joint distribution of highly-correlated patch couples from the input image itself instead of an external dataset, so that the learnt models are specially tailored for the current patches and thus can better fit the image data of interest. To be specific, we first apply spatially adaptive gradient sparsity regularization in the reconstruction of the HR image using the contour information, and then utilize the generated HR output to guide the joint distribution learning to infer the relationship between the highly-correlated HR and LR patches. In this way, we simultaneously exploit the inter-scale correlation as well as the local and non-local correlation of the image contents. Empirical results show that the performance of the proposed method is highly competitive with state-of-the-art schemes in terms of peak signal-to-noise ratio (PSNR) and perceptual quality.
Balanced multiple description scalar quantization

This paper tackles the problem of the generation of an arbitrary number of balanced descriptions with multiple description scalar quantization (MDSQ). We show how, with a very low complexity, we can vary the number of descriptions and the redundancy between them, in order to adapt to different channel characteristics. A comparison with state-of-the-art MDSQ schemes shows a better performance of our solution in terms of an average distortion at the receiver, which comes from the flexibility of our solution to better adapt to various lossy conditions.
Classification of unions of subspaces with sparse representations

We propose a preliminary investigation on the benefits and limitations of classifiers based on sparse representations. We specifically focus on the union of subspaces data model and examine binary classifiers built on a sparse non linear mapping (in a redundant dictionary) followed by a linear classifier. We study two common sparse non linear mappings (namely l0 and l1) and show that, in both cases, there exists a finite dictionary such that the classifier discriminates the two classes correctly. This result paves the way towards a better understanding of the increasingly popular classifiers based on sparse representations, and provides initial insights on appropriate dictionary design.
Flexible motion-adaptive video coding with redundant expansions

This paper presents a highly flexible video coding scheme, based on the use of a redundant dictionary of spatio-temporal three-dimensional (3-D) functions. Directionality and anisotropic scaling are key ingredients to the spatial components, which form a rich collection of two-dimensional (2-D) visual primitives. The temporal component is tuned to capture most of the energy in the temporal signal evolution, along motion trajectories in the video sequence. The video coding scheme (MP3D) first computes motion trajectories that are eventually entropy coded and sent as side information to the decoder. It then applies a spatio-temporal decomposition along motion trajectories, using an adaptive approximation algorithm based on matching pursuit (MP). Quantized coefficients and basis function parameters are entropy-coded in a embedded stream that is constructed to respect multiple rate constraints. The geometric properties of the 2-D primitive dictionary allow for flexible spatial resolution adaptation, so that the flexible MP3D stream enables decoding at different spatio-temporal resolutions, and multiple rates. The MP3D scheme is shown to provide rate-distortion performance that are comparable with state-of-the-art schemes, such as H.264, MPEG-4, at low and medium bit rate. However, the use of a redundant dictionary is penalizing at high coding rate, which makes the MP3D algorithm mostly interesting for low rate applications, or as a flexible base layer in hierarchical coding schemes.
Laplacian matrix learning for smooth graph signal representation

The construction of a meaningful graph plays a crucial role in the emerging field of signal processing on graphs. In this paper, we address the problem of learning graph Laplacians, which is similar to learning graph topologies, such that the input data form graph signals with smooth variations on the resulting topology. We adopt a factor analysis model for the graph signals and impose a Gaussian probabilistic prior on the latent variables that control these graph signals. We show that the Gaussian prior leads to an efficient representation that favours the smoothness property of the graph signals, and propose an algorithm for learning graphs that enforce such property. Experiments demonstrate that the proposed framework can efficiently infer meaningful graph topologies from only the signal observations.
Network Coding Node Placement for Delay Minimization in Streaming Overlays

Network coding has been proposed recently as an efficient method to increase network throughput by allowing network nodes to combine packets instead of simply forwarding them. However, packet combinations in the network may increase delay, complexity and even generate overly redundant information when they are not designed properly. Typically, the best performance is not achieved when all the nodes perform network coding. In this paper, we address the problem of efficiently placing network coding nodes in overlay networks, so that the rate of innovating packets is kept high, and the delay for packet delivery is kept small. We first estimate the expected number of duplicated packets in each network node. These estimations permit to select the nodes that should implement network coding, so that the innovating rate increases. Two algorithms are then proposed for the cases where a central node is aware of the full network statistics and where each node knows the local statistics from its neighbor, respectively. The simulation results show that in the centralized scenario the maximum profit from network coding comes by adding only a few network coding nodes. A similar result is obtained with the algorithm based on local statistics, which moreover performs very close to the centralized solution. These results show that the proper selection of the network coding nodes is crucial for minimizing the transmission delay in streaming overlays.
Progressive low bit rate coding of simple 3D objects with matching pursuit

The paper presents a low rate progressive 3D mesh compression scheme for simple, genus-zero 3D objects. The proposed scheme is based on signal representation using redundant expansions on the 2D-sphere. First, generic input data is re-sampled as a function on the 2D-sphere, and the signal value for each point on the regular grid is obtained by performing nearest neighbor interpolation within four points from the initial 3D model. The model representation is then constructed using a matching pursuit algorithm, with an over-complete dictionary of atoms, defined on a sphere. In order to capture the particular characteristics of the 3D models efficiently, we propose a dictionary construction based on two generating functions, a Gaussian to capture low-frequency components, and a modified combination of a Gaussian and its second derivative to capture high-frequency components of the input signal. Compared to state-of-the-art encoders, our method has been shown to offer very good compression efficiency, but the performance is limited by the resampling step that maps the input model on the 2D-sphere. Matching pursuit has, however, the advantage of providing an intrinsically progressive scheme, that is also very flexible.
Sparse stereo image coding with learned dictionaries

This paper proposes a framework for stereo image coding with effective representation of geometry in 3D scenes. We propose a joint sparse approximation framework for pairs of perspective images that are represented as linear expansions of atoms selected from a dictionary of geometric functions learned on a database of stereo perspective images. We then present a coding solution where atoms are selected iteratively as a trade-off between distortion and consistency of the geometry information. Experimental results on stereo images from the Middlebury database show that the new coder achieves better rate-distortion performance compared to the MPEG4-part10 scheme, at all rates. In addition to good rate-distortion performance, our flexible framework permits to build consistent image representations that capture the geometry of the scene. It certainly represents a promising solution towards the design of multi-view coding algorithms where the compressed stream inherently contains rich information about 3D geometry.
The M-term pursuit for image representation and progressive compression

This paper introduces a sparse signal representation algorithm in redundant dictionaries, called the M-term pursuit (MTP), with an application to image representation and scalable coding. The MTP algorithm belongs to the framework of the matching pursuit (MP) (S. Mallat and Z. Zhang, 1993); it expands the image into a linear combination of atoms, selected from a large collection of spatial atoms. The MTP relies on the concept of dictionary partitioning, i.e., as splitting the dictionary into L disjoint sub-dictionaries, each carrying some specific information. Then, it iteratively finds a K-term approximation, by selecting M atoms at a time, where M /spl les/ L, followed by an orthogonal projection. The approximation performances of the MTP algorithm have been shown to yield comparable results with those of the matching pursuit. However, it presents the advantage of a reduced computational complexity. For progressive image compression, an embedded quantization and coding step is applied on the series of obtained atoms based on the subset approach (A. Rahomoune, et al, 2004); to generate a flexible bitstream. The performances of the MTP image coder are finally shown to compare favorably against those of the state-of-the-art JPEG-2000 scheme, in terms of rate-distortion characteristics.
Wyner-Ziv Coding of Multi-View Omnidirectional Imageswith Overcomplete Decompositions

This paper addresses the problem of distributed coding of light fields in camera networks. A novel distributed coding scheme with side information is presented, based on spherical image expansion over an over complete dictionary of geometric atoms. We propose to model the correlation between views with local geometrical transformations of corresponding features in the sparse representations of different views. We design a Wyner-Ziv encoder by partitioning the dictionary into cosets of dissimilar atoms, with respect to their shape and position on the image. The joint decoder finds pairwise correspondences between atoms in the reference image and atoms in cosets of the Wyner-Ziv image. It selects the most likely correspondence among pairs of atoms that satisfy epipolar geometry constraints. This permits to estimate local transformations between correlated images that eventually help to refine the side information provided by the reference image. Experiments demonstrate that the proposed method is capable of estimating the geometric transformations between views, and hence to reconstruct the Wyner-Ziv image.
Optimal decentralized coded caching for heterogeneous files

Caching is a technique to reduce the peak network load by pre-fetching some popular contents at the local caches of end users. Coded caching can facilitate and exploit the coded-multicasting opportunities for users with different demands, resulting in an additional and significant reduction of the peak traffic. However, most existing researches on coded caching are limited by the assumption that all files to be delivered have the same size. We show in this paper that current schemes can only achieve suboptimal performance when the files have different sizes. To address this, we propose a novel optimization strategy for coded caching that minimizes the worst-case transmission rate of multicasting the coded content upon users requests, subject to the storage constraint at the local caches, by the optimal allocation of the caching proportion among heterogeneous files. In order to efficiently solve this problem, we develop a practical algorithm by using the Lagrange multiplier method and the sequential quadratic programming (SQP) approach. Experiment results show that the worst-case transmission rate can be reduced by the proposed scheme compared to state-of-the-art coded caching schemes. It certainly offers an important advantage in the deployment of data delivery systems.