nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Robust Optimization for Scalable Video Multirate Multicast Over Wireless Networks

This paper proposes a distributed robust optimization scheme to jointly optimize overall video quality and traffic performance for scalable video multirate multicast over practical wireless networks. In order to guarantee layered utility maximization, the initial nominal joint source and network optimization is defined, where each scalable layer is tailored in an incremental order and finds jointly optimal multicast paths and associated rates with network coding. To enhance the robustness of the nominal convex optimization formulation with nonlinear constraints, we reserve partial bandwidth for backup paths disjoint from the primal paths. It considers the path-overlapping allocation of backup paths for different receivers to take advantage of network coding, and takes into account the robust multipath rate-control and bandwidth reservation problem for scalable video multicast streaming when possible link failures of primary paths exist. Specifically, an uncertainty set of the wireless medium capacity is introduced to represent the uncertain and time-varying property of parameters related to the wireless channel. The targeted uncertainty in the robust optimization problem is studied in a form of protection functions with nonlinear constraints, to analyze the tradeoff between robustness and distributedness. Using the dual decomposition and primal-dual update approach, we develop a fully decentralized algorithm with regard to communication overhead. Through extensive experimental results under critical performance factors, the proposed algorithm could converge to the optimal steady-state more quickly, and adapt the dynamic network changes in an optimal tradeoff between optimization performance and robustness than existing optimization schemes.
A novel Slepian-Wolf decoding algorithm exploiting geometric regularity constraints with anisotropic MRF modeling

This paper proposes a novel Slepian-Wolf decoding algorithm for distributed video coding by exploiting not only the statistical correlation between the side-information and source but also the spatio-temporal consistency constraint of video sequences. The proposed algorithm models the log-likelihood-ratio (LLR) information for Slepian-Wolf decoding as an anisotropic MRF model and solving the inference by iteratively performing conventional probabilistic Slepian-Wolf decoding, which imposes the global bit-wise constraint from the Slepian-Wolf encoding process, and MRF optimization with belief propagation (BP) to enforce the local geometric regularity constraint of video frames. Experimental results demonstrate a considerable performance gain beyond existing Slepian-Wolf decoding algorithms in literature.
Multiscale dictionary learning for hierarchical sparse representation

In this paper, we propose a multiscale dictionary learning framework for hierarchical sparse representation of natural images. The proposed framework leverages an adaptive quadtree decomposition to represent structured sparsity in different scales. In dictionary learning, a tree-structured regularized optimization is formulated to distinguish and represent high-frequency details based on varying local statistics and group low-frequency components for local smoothness and structural consistency. In comparison to traditional proximal gradient method, block-coordinate descent is adopted to improve the efficiency of dictionary learning with a guarantee of recovery performance. The proposed framework enables hierarchical sparse representation by naturally organizing the trained dictionary atoms in a prespecified arborescent structure with descending scales from root to leaves. Consequently, the approximation of high-frequency details can be improved with progressive refinement from coarser to finer scales. Employed into image denoising, the proposed framework is demonstrated to be competitive with the state-of-the-art methods in terms of objective and visual restoration quality.
Structure-aware priority belief propagation for depth estimation

2D to 3D video conversion has become a popular manner to produce 3D contents, in which the key task is to estimate the depth for the images. However, the main limitation of conventional motion-based depth propagation methods is that they are susceptible to inaccurate motion estimation and occlusion. To address this problem, we propose a two-stage structure-aware depth propagation method for semi-automatic 2D-to-3D conversion. In the first stage, an initial depth map is generated by shifted bilateral filtering over the temporally consistent region, which is determined by validating the forward and backward optical flow fields. In the second stage, the depth of the temporally inconsistent region will be estimated by solving a multi-label graph inference problem. In particular, an efficient priority belief propagation algorithm is developed, in which the priority of nodes to propagate messages depends on the structure saliency from tensor voting. Experimental results show that the proposed method outperforms existing depth estimation methods in public benchmark.
Prioritized Flow Optimization With Multi-Path and Network Coding Based Routing for Scalable Multirate Multicasting

In this paper, we study performance optimization for scalable video coding and multicast over networks. Multi-path video streaming, network coding based routing, and network flow control are jointly optimized to maximize a network utility function defined over heterogeneous receivers. Content priority of video coding layers is considered during the flow routing to determine the optimal multicast paths and associated data rates for each layer. Our optimization scheme attempts to find content distribution meshes with minimum path costs for each video coding layer while satisfying the inter-layer dependency during scalable video coding. Based on primal decomposition and primal-dual analysis, we develop a decentralized algorithm with two optimization levels to solve the performance optimization problem. We also prove the stability and convergence of the proposed iterative algorithm using Lyapunov theories. Extensive experimental results demonstrate that the proposed algorithm not only achieves the max-flow throughput using network coding, but also provides better video quality with balanced layered access for heterogeneous receivers.
Joint source-channel rate control for multiview video streaming over erasure channels

To enable robust multiview video streaming over current wireless broadcast infrastructures, this paper proposes a joint source and channel coding optimization framework for video streaming based upon multiview video coding technique. It utilizes random linear coding (RLC) with ladder-shaped global coding coefficient matrix (GCM) for unequal erasure protection. An accurate expression of the decoding probability for RLC with ladder-shaped GCM is derived. A fast two-layer local search algorithm is developed to solve the source and channel coding rate allocation problem, where the source and channel rate of each layer can be adjusted independently for the overall video quality. Simulation results demonstrate significant video quality improvement to existing rate allocation schemes.
Modeling and Optimization of Network Lifetime in Wireless Video Sensor Networks

This paper studies the power consumption performance and resource allocation optimization in wireless video sensor networks. Network coding based multipath routing, network flow control and video encoding bit rate are jointly optimized, aiming to maximize the network lifetime at a given power budget and video quality requirement. Importantly, to concretely measure the network coding power utilized on error recovery, a generalized power consumption model for network coding is first developed in this paper. Through the Lagrange dual and subgradient approach, a fully decentralized algorithm is proposed to solve the target convex optimization problem. Numerical results validate the convergence and performance of the proposed algorithm.
On Non-sequential Context Modeling with Application to Executable Data Compression

The sequential context modeling framework is generalized to a non-sequential one by context relaxation from consecutive suffix of the subsequences of symbols to the permutation of the preceding symbols as result of considering complex context structures in such sources as video and program binaries. Context weighting tree is also extended to a series of context trees which are built according to the "model tree", in which the descendent relationship in the formation of non-sequential context sets is described. Model redundancy and maximum a posteriori model in the framework are discussed and compared. A decision method based on the greedy algorithm is proposed to customize sets of models fitting the concrete sources. Brief description of application to executable data files incorporating with the semantics and syntax constraints are given and experiment are made accordingly as a validation.
Picking Neural Activations for Fine-Grained Recognition

It is a challenging task to recognize fine-grained subcategories due to the highly localized and subtle differences among them. Different from most previous methods that rely on object/part annotations, this paper proposes an automatic fine-grained recognition approach, which is free of any object/part annotation at both training and testing stages. The key idea includes two steps of picking neural activations computed from the convolutional neural networks, one for localization, and the other for description. The first picking step is to find distinctive neurons that are sensitive to specific patterns significantly and consistently. Based on these picked neurons, we initialize positive samples and formulate the localization as a regularized multiple instance learning task, which aims at refining the detectors via iteratively alternating between new positive sample mining and part model retraining. The second picking step is to pool deep neural activations via a spatially weighted combination of Fisher Vectors coding. We conditionally select activations to encode them into the final representation, which considers the importance of each activation. Integrating the above techniques produces a powerful framework, and experiments conducted on several extensive fine-grained benchmarks demonstrate the superiority of our proposed algorithm over the existing methods.
Scalable video compression framework with adaptive multiresolution directional filter bank design

Regarding orientation resolution as an isolated variable from scale, this paper proposes a dual (scale and orientation) multiresolution transform into scalable video coding (SVC) framework. By projecting 2D signals (textures and edges) onto nonuniformly divided orientation subspaces, the dual multiresolution SVC (DMSVC) can capture 2-D curve smoothness with less coefficients, and provide more flexible spatial decomposition structures than traditional wavelet-based SVC (WSVC). In the spatial decomposition module of DMSVC, the nonuniform directional distribution along scale of each frame is detected by phase congruency in the overcomplete wavelet domain. The corresponding orientational multi-resolution is achieved by nonuniform directional filter banks (NUDFB) which is fulfilled via a non-symmetric binary tree (NSBT) structured frequency division. The wavelet basis function in each scale is converted to an adaptive set of nonuniform directional basis by employing nonuniform directional filter banks. Experimental results validate a superior coding performance and visual quality over WSVC especially on those sequences full of directional edges and textures.
Conditional random field based side-information fusion for distributed multi-view video coding

This paper presents a new temporal and inter-view side-information fusion algorithm for distributed multi-view video coding (DMVC). Unlike existing fusion algorithms in DMVC schemes that produce the fusion mask by finding the motion vector outliers, it introduces conditional random fields (CRF) to exploit the intrinsic geometric regularity and temporal consistency constraint in multi-view video sequences. Specifically, Wyner-Ziv (WZ) frames are modeled by CRF with the temporal and the inter-view side-information as two observations. The observation distribution models the local accuracy of the temporal and the inter-view side-information. The transition distribution of the CRF model represents the local geometric regularity, e.g., the edge directions and the local smoothness of the WZ frame. Its parameters are trained from previously decoded WZ frames, and the inference is made on trained weights to generate fused side-information. The accurate modeling is validated to show a significant performance gain over the existing fusion algorithms by experiments.
Progressive Dictionary Learning With Hierarchical Predictive Structure for Low Bit-Rate Scalable Video Coding

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between the neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a closed-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the state-of-the-art scalable extension of H.264/AVC and latest High Efficiency Video Coding (HEVC), standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest scalable extension of HEVC and HEVC simulcast over extensive test sequences with various resolutions.
Illumination compensation via low rank matrix completion for multiview video coding

A multi-view video system would capture the same scene from different viewpoints, and suffer from significant illumination variations due to inaccurate camera calibration and varying light conditions. It deteriorates the inter-view correlations and the quality of synthesized views at decoder side. By converting the problem of illumination compensation to noise removing, this paper proposes a low-rank matrix completion algorithm to reduce the effect of illumination variation. Diverged from the existing work which chooses a central view as reference and keeps views consistent, it is dedicated to compensating all the views to match the low-rank structure of views. The discrepancies among views are regarded as mixed noise, and constructed as an incomplete matrix with low-rank. It is solved by a stable matrix completion and obtains a mapping function for color correction. It is robust to outliers since only plausible corresponding points are involved with completion. Experimental results show that the proposed algorithm can increase coding efficiency by up to 0.7 dB for luminance component and up to 2.1 dB for chrominance components.
STOL: Spatio-Temporal Online Dictionary Learning for Low Bit-Rate Video Coding

To speed up the convergence rate of learning dictionary in low bit-rate video coding, this paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to improve the original adaptive regularized dictionary learning with K-SVD which involves a high computational complexity and interfere with the coding efficiency. Considering the intrinsic dimensionality of the primitives in training each series of 2-D sub dictionaries is low, the 3-D low-frequency and high-frequency dictionary pair would be formed by the online dictionary learning to update the atoms for optimal sparse representation and convergence. Instead of classical first-order stochastic gradient descent on the constraint set, e.g. K-SVD, the online algorithm would exploit the structure of sparse coding in the design of an optimization procedure in terms of stochastic approximations. It depends on low memory consumption and lower computational cost without the need of explicit learning rate tuning. Through drawing a cubic from i.i.d. samples of a distribution in each inner loop and alternating classical sparse coding steps for computing the decomposition coefficient of the cubic over previous dictionary, the dictionary update problem is converted to solve the expected cost instead of the empirical cost. For dynamic training data over time, online dictionary learning behaves faster than second-order iteration batch alternatives, e.g. K-SVD. Through experiments, the super-resolution reconstruction based on STOL obviously reduces the computational complexity to 40% to 50% of the K-SVD learning-based schemes with a guaranteed accuracy.
Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization

Fine-grained visual categorization is an emerging research area and has been attracting growing attention recently. Due to the large inter-class similarity and intra-class variance, it is extremely challenging to recognize objects in fine-grained domains. A traditional spatial pyramid matching model could obtain desirable results for the basic-level category classification by weak alignment, but may easily fail in fine-grained domains, since the discriminative features are extremely localized. This paper proposes a new framework for fine-grained visual categorization. First, an efficient part localization method incorporates semantic prior into geometric alignment. It detects the less deformable parts, such as the head of birds with a template-based model, and localizes other highly deformable parts with simple geometric alignment. Second, we learn one-vs-all features, which are simple and transplantable. The learned mid-level features are dimension friendly and more robust to outlier instances. Furthermore, in view that some subcategories are too similar to tell them apart easily, we fuse the subcategories iteratively according to their similarities, and learn fused one-vs-all features. Experimental results show the superior performance of our algorithms over the existing methods.
Transductive video co-segmentation on the temporal trees

This paper proposes a novel multi-component video co-segmentation approach to simultaneously separate the foreground from the background in the video frames. To capture the variance of appearance of the foreground object, a multi-component foreground model is developed. Each component of the model characterizes a specific viewpoint/pose/appearance of the foreground object. To learn the parameters of the multi-component model, a transductive learning algorithm is leveraged to “transfer” the information of the labeled frames to the unlabeled frames in a tree-structured model, namely, temporal tree. Each branch of the temporal tree consists of the exemplars of a foreground component, and a transductive support vector regressor is capable of being trained. Experiments show that the proposed method outperforms quite a few state-of-the-art video segmentation algorithms in public benchmark.
A Unified QoS Optimization for Scalable Video Multirate Multicast over Hybrid Coded Network

This paper aims to optimize joint overall video quality and traffic performance of multirate multicast of scalable video streaming accessing hybrid wired/wireless paths. In order to guarantee layered utility maximization over tiered wired/wireless coded networks, we propose a joint source and network flow optimization scheme where each scalable layer is tailored in an incremental order and finds jointly optimal multicast paths and associated rates. It not only achieves the highest sustainable layered video quality, but also takes into account both path-overlapping allocation for different receivers which takes advantage of network coding and the wireless link contention in a shared transmission medium. The convex programming is imposed on the total variation minimization of layered rate-distortion. Using primal decomposition and the primal-dual approach, we develop a decentralized algorithm with two levels of optimization. Experimental results demonstrate that the proposed algorithm not only provides better video quality with optimal layered access for heterogeneous receivers, but benefits the asymptotic capacity of a hierarchical "hybrid" wired/wireless architecture in a scalable sense.
Transmission Distortion Analysis for Real-Time Video Encoding and Streaming Over Wireless Networks

A major challenge in video encoding and transmission over wireless networks is that the channel is error-prone while the compressed video data is highly sensitive to errors. The transmission errors will cause decoding failure at the receiver side. More importantly, the transmission errors introduced in one video frame will propagate to its subsequent frames along the motion prediction path and significantly degrade video presentation quality. This type of picture distortion is called transmission distortion. In this work, we propose a control system approach to transmission distortion modeling. More specifically, we consider the wireless video transmission and decoding system as a linear system with the transmission errors as system input and the transmission distortion as output. We study the fading behavior of the impulse transmission distortion. We analyze the low-pass filtering behavior of video encoders and develop a scheme to estimate the instantaneous transmission distortion. Our extensive simulation results demonstrate that the proposed transmission distortion model is accurate and robust. In addition, it has very low computational complexity. More importantly, because it is a predictive model, it allows the encoder to predict the transmission distortion even before the video is compressed and transmitted. This type of transmission distortion model has important applications in resource allocation and performance optimization in real-time wireless video communication
Pulmonary Blood Vessels and Nodules Segmentation via Vessel Energy Function and Radius-Variable Sphere Model

To help diagnose the early stage of lung cancer, this paper studies pulmonary nodule and blood vessel detection and segmentation. Owing to the fact that variation in the shape and number of pulmonary blood vessels would reveal the progress of lung cancer, automatic segmentation of pulmonary nodules and blood vessels is desirable for chest computer-aided diagnosis (CAD) systems. The proposed algorithm is composed of four steps: pre-segmentation, structure enhancement, active evolution, and refinement. Through the initial extraction of 3D region growing, the line structure of vessel and blob-like structure of nodule would be enhanced by multi-scale filtering. In particular, the active evolution is devoted to the maximum likelihood estimation with a vessel energy function (VEF) of intensity, gradient, and structure. The VEF aims to shape a precise extraction by adapting all the cue distribution along the vessel region from nodules. Furthermore, a radius-variable sphere model is adopted to refine the contour with the smoothness of radius alone the centerline of the blood vessel. Finally, the proposed scheme is sufficiently evaluated to exceed the existing techniques on lung image database consortium (LIDC) database and DICOM images.