nils-herrmann/clean_scirepeval huggingface dataset:
Multiple Semantic Matching on Augmented $N$ -Partite Graph for Object Co-Segmentation.

Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object co-segmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure. To this end, we first propose to incorporate candidate generation with semantic context. Based on the regions extracted from semantic segmentation of each image, we design a merging mechanism to hierarchically generate candidates with high semantic responses. Second, all candidates are taken into consideration to globally formulate multiple maximum weighted matching cliques, which complement the discovery of part of the common objects induced by a single clique. To facilitate the discovery of multiple matching cliques, an N-partite graph, which inherently excludes intralinks between candidates from the same image, is constructed to separate multiple cliques without additional constraints. Further, we augment the graph with an additional virtual node in each part to handle irrelevant matches when the similarity between the two candidates is too small. Finally, with the explored multiple cliques, we statistically compute pixel-wise co-occurrence map for each image. Experimental results on two benchmark data sets, i.e., iCoseg and MSRC data sets achieve desirable performance and demonstrate the effectiveness of our proposed framework.
Super-resolution reconstruction with prior manifold on primitive patches for video compression

This paper proposes a generic video compression framework with low-quality video data and a learning-based approach, which is rooted in sparse representation for the ill-posed problem of video super-resolution reconstruction. It is regularized by the prior manifold only on the “primitive patches”, and each primitive patch is modeled by a sparse representation concerning an over-complete dictionary of trained set. Due to low intrinsic dimensionality of primitives, the number of samples in the dictionary can be greatly reduced. Considering the similar geometry of the manifolds of the feature spaces from the low-frequency and the high-frequency primitives, we hypothesize that the low-frequency and its corresponding high-frequency primitive patches share the same sparse representation structure. In this sense, high-resolution frame primitives are divided into low-frequency and high-frequency frame primitives, and high-frequency frame primitive patches can be synthesized from both the high-frequency primitive patch dictionary and the sparse structure of the corresponding low-frequency frame primitive patches. It does not involve with explicit motion estimation and any assistant information, and decomposes the original video sequence into key frames and low-resolution frames with low entropy. The corresponding high-resolution frames would be reconstructed by combining the high-frequency and the low-frequency patches with smoothness constraints and the backpro-jection process. Experimental results demonstrate the objective and subjective efficiency in comparison with H.264/AVC and existing super-resolution reconstruction approaches.
Design and implementation of an end-to-end system importing DTV from broadcasting network to broadband IP network

This paper describes design and implementation of an end-to-end system which implements digital television (DTV) broadcasting to broadband IF' networks. We have developed a DTV-IF' gateway for introducing DTV program from broadcasting headend into IF' network with special considerations of the challenges, which consist of transmitting DTV in the IP network environment and different transmitting protocols between the traditional satellite, cable or terrestrial network and the broadband IP network. Moreover, we have proposed a method to auto-regenerate the standard transport stream (TS) packet for the standard set-top-box (STB) with consideration of timing synchronization. This end-to-end system has been verified to work well in the broadband IP network environment.
Graph Matching Based Side Information Generation for Distributed Multi-View Video Coding

In this paper, we adopt constrained relaxation for distributed multi-view video coding (DMVC). The novel framework integrates the graph-based segmentation and matching to generate inter-view correlated side information without knowing the camera parameters. Moreover, graph-based representations of multi-view images are incorporated to form more distinctive feature constraints. The sparse data as a good hypothesis space aim for a best matching optimization of inter-view side information with compact syndromes, from inferred relaxed coset. The plausible filling-in from a priori feature constraints between neighboring views could reinforce a promising compensation to inter-view side information generation for joint multi-view decoding. In order to find distinctive feature matching with a more stable approximation, PCA-SIFT and TPS (thin plate spline) are adopted to reduce the dimension of SIFT descriptors and construct a more accurate inter-view motion model. The experimental results validate the high estimation precision and the rate-distortion improvements.
Multitask Learning of Compact Semantic Codebooks for Context-Aware Scene Modeling

In the past few decades, we have witnessed the success of bag-of-features (BoF) models in scene classification, object detection, and image segmentation. Whereas it is also well acknowledged that the limitation of BoF-based methods lies in the low-level feature encoding and coarse feature pooling. This paper proposes a novel scene classification method, which leverages several semantic codebooks learned in a multitask fashion for robust feature encoding, and designs a context-aware image representation for efficient feature pooling. Apart from conventional universal codebook learning approaches, the proposed method encodes each class of local features with a unique semantic codebook, which captures the distinct distribution of different semantic classes more effectively. Instead of learning each semantic codebook separately, we learn a compact global codebook, of which each semantic codebook is a sparse subset, with a two-stage iterative multitask learning algorithm. While minimizing the clustering divergence, the semantic codeword assignment is solved by submodular optimization simultaneously. Built upon the global and semantic codebooks, a context-aware image representation is further developed to encode both global and semantic features in image representation via contextual quantization, semantic response computation, and semantic pooling. Extensive experiments have been conducted to validate the effectiveness of the proposed method on various public benchmarks with several popular local features.
Architecture and rate control for network TV broadcasting on application-oriented QOS

This paper addresses the implementation architecture and protocol procedure of the dedicated broadband network TV broadcast system. According to application-oriented quality of service (QoS), two alternative rate control schemes for multiple DTV programs transmission are proposed collaborating with MPEG-2 system strategy and IP-base network multiplexing mechanism. As to the network bandwidth utilization, a flexible RTF-based encapsulation scheme for VBR transport stream (TS) transmission is realized to ensure the time synchronization requirement for MPEG-2 standard. As to the statistical multiplexing gain, moreover, a parallel VBR transcoding/encoding rate control algorithm for CBR TS transmission could achieve a consistent picture quality for multiple DTV programs under a constrained network bandwidth. For adapting the variable access network bandwidth, the system main control module can flexibly schedule the available rate control scheme for multiple slave service multiplexers
Joint Coding/Routing Optimization for Correlated Sources in Wireless Visual Sensor Networks

This paper studies a joint coding/routing optimization between network lifetime and rate-distortion, by applying information theory to wireless visual sensor networks for correlated sources. Arbitrary coding (distributed source coding and network coding) from both combinatorial optimization and information theory could make significant progress towards the performance limit of information networks and tractable. Also, multipath routing can spread energy utilization across nodes within the entire network to keep a potentially longer lifetime, and solve the wireless contention issues by the splitting traffic. The objective function not only keeps a total energy consumption of encoding power, transmission power, and reception power minimized, but ensures the information received by sink nodes to approximately reconstruct the visual field. Based on the localized Slepian-Wolf coding and network coding-based multipath routing, the balance problem between distortion (capacity) and lifetime (costs) is modeled as an optimization formulation with a distributed solution. Through a primal decomposition, a two-level optimization is relaxed with Lagrangian dualization and solved with the gradient algorithm. The low-level optimization problem is decomposed into a secondary master dual problem (encoding, energy, and congestion prices update) with four cross-layer subproblems: a rate control problem, a channel contention problem, a distortion control problem, and an energy conservation problem. Numerical results validate the convergence and performance of the proposed algorithm.
Implementation of digital HDTV encoder with parallel sub-picture encoding modules and its joint bit-allocation strategy

Parallel processing is a feasible scheme for a digital HDTV encoder, to get round the dilemma in high-speed digital processing circuitry and real-time compression. This paper suggests an implementation procedure and a novel bit-allocation strategy for a digital high definition television (HDTV) encoder system with parallel processing architecture based on six horizontal strips division. Multiple sub-pictures encoding modules (SEMs) perform MPEG-2 coding respectively and simultaneously. Owing to the inherent deficiencies of MPEG-2 Test Model 5 (TM5) rate control scheme, how to allocate bit-rate among sub-pictures would become an inevitable challenge, which might behave with the coding quality to decrease worse and the buffer to underflow at scene changes, moreover give birth to non-consistent sub-pictures visual quality and dynamic quality variation in a composite HDTV frame on subjective and objective evaluation. We adapt a hierarchical joint optimized bit-allocation with sub-pictures' average complexity measure and average bits per pixel in terms of their average quantization distortion, moreover incorporates scene detection to alleviate a possible worse sub-pictures quality inconsistency. In the paper, we describe an elaborate procedure including general framework, sub-pictures division module, sub-picture encoding module and sub-pictures synthesis module, then deduce the joint bit-allocation strategy for sub-pictures encoding and the auxiliary rate adaptive procedure for scene changes. Experimental results demonstrate that our proposed scheme not only alleviates the boundary effect, but also promises the sub-pictures quality consistency. The proposed scheme might be devoted to the multiprogram broadcast application and the multiple access sceneries.
Nonconvex Optimization with Alternating Direction Method of Multipliers for Tensor-Based Compressed Sensing with Group Sparsity

Tensor-based compressive sensing (CS) can preserve the intrinsic multidimensional structures with a reduced computational complexity. However, its recovery performance is degraded by simple sparsity. This paper proposes an efficient recovery algorithm GT-ADMM to solve a nonconvex optimization problem for tensor-based CS with group sparsity. Group-sparse representations of tensors is derived based on the Kronecker product of adaptively trained basis. The proposed algorithm incorporates alternating direction method of multipliers (ADMM) to obtain desirable recovery quality with improved efficiency. It requires few degrees of freedom to achieve tensor recovery with a guarantee of minimized approximation error. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art tensor-based methods in compressive video sampling.
Advanced inpainting-based macroblock prediction with regularized structure propagation in video compression

In this paper, we propose an optimized inpainting-based macroblock (MB) prediction mode (IP-mode) in the state-of-the-art H.264/AVC video compression engine, and investigate a natural extension of structured sparsity over the ordered Belief Propagation (BP) inference in inpainting-based prediction. The IP-mode is regularized by a global spatio-temporal consistency between the predicted content and the co-located known texture, and could be adopted in both Intra and Inter frames without redundant assistant information. It is solved by an optimization problem under Markov Random Field (MRF), and the structured sparsity of the predicted macroblock region is inferred by tensor voting projected from the decoded regions to tune the priority of message scheduling in BP with a more convergent manner. Rate-distortion optimization is maintained to select the optimal mode among the inpainting-based prediction (IP-), the intra-, and inter-modes. Compared to the existing prediction modes in H.264/AVC, the proposed inpainting-based prediction scheme is validated to achieve a better R-D performance for homogeneous visual patterns and behave a more robust error resilience capability with an intrinsic probabilistic inference.
Optimal Power Allocation for Hybrid Overlay/Underlay Spectrum Sharing in Multiband Cognitive Radio Networks

In this paper, we consider power allocation in multiband cognitive radio (CR) networks, where multiple secondary users (SUs) transmit via a common relay and compete for the transmit power of the relay. We employ a hybrid overlay/underlay spectrum sharing scheme, allowing the SU to adapt its way of accessing the licensed spectrum to the status of the primary user (PU). If the PU is detected to be idle at the selected channel, the SU works in an overlay mode; else, it works in spectrum underlay. In addition, an auction-based power-allocation scheme is proposed to solve power competition of multiple SUs. For the SU working in spectrum overlay, the relay allocates the power in proportion to its payment without additional constraints; for the SU in spectrum underlay, its own transmit power and that of the relay are upper bounded for the quality of service (QoS) of the PU. Then, the convergence of the proposed auction algorithm and the outage probability of secondary transmissions is theoretically analyzed. Finally, the performance of the proposed scheme is verified by the simulation results.
Joint Coding/Routing Optimization for Distributed Video Sources in Wireless Visual Sensor Networks

This paper studies a joint coding/routing optimization between network lifetime and video distortion by applying information theory to wireless visual sensor networks for correlated sources. Arbitrary coding [distributed video coding and network coding (NC)] from both combinatorial optimization and information theory could make significant progress toward the performance limit and tractable. Also, multipath routing can spread energy utilization across nodes within the entire network to keep a potentially longer lifetime, and solve the wireless contention issues by the splitting traffic. The objective function not only keeps the total energy consumption of encoding power, transmission power, and reception power minimized, but ensures the information received by sink nodes to approximately reconstruct the visual field. Also, a generalized power consumption model for distributed video sources is developed, in which the coding complexity of Key frames and Wyner-Ziv frames is measured by translating specific coding behavior into energy consumption. On the basis of the distributed multiview video coding and NC-based multipath routing, the balance problem between lifetime (costs) and distortion (capacity) is modeled as an optimization formulation with a fully distributed solution. Through a primal decomposition, a two-level optimization is relaxed with Lagrangian dualization and solved by the gradient algorithm. The low-level optimization problem is further decomposed into a secondary master dual problem with four cross-layer subproblems: a rate control problem, a channel contention problem, a distortion control problem, and an energy conservation problem. The implementation of the distributed algorithm is discussed with regard to the communication overhead and dynamic network change. Simulation results validate the convergence and performance of the proposed algorithm.
Graph Theory Application in Cell Nuleus Segmentation, Tracking and Identification

A novel cell nucleus tracking approach is raised in this paper, developed from applied graph theory that simultaneously handles major challenges of cells touching, splitting, disappearing and emerging. The key point of our work lies in the graphs with robust structures that constructed from large cells groups. Then the problem of nucleus tracking is simplified to that of vertex matching between graphs generated from successive frames. Single cells are modeled as vertices in the graph and edges are built between neighboring ones, through which cells in large quantities are connected altogether. The algorithm works through a process of graph saturation following vertices with maximal entering belief-propagation flows. Feasibility of our approach is validated by so-called 'Crowds Model' that explains motional features of multi-cells groups. In processing CT image sources with high cell intensities as well as complex variations in structures, robustness of our approach is confirmed in that both successful tracking rates and segmentation rates are kept above 96%.
2-D dual multiresolution decomposition through NUDFB and its application

This paper aims to attain sparser representation of a 2-D signal by introducing orientation resolution as a second multiresolution besides multiscale, which is formulated to achieve a dual multiresolution decomposition framework by nonuniform directional frequency decompositions (NUDFB) under arbitrary scales. In this scheme, NUDFB is fulfilled by changing the topology structure of a non-symmetric binary tree (NSBT). Through this nonuniform division, we can get arbitrary orientation resolution r at a direction of c2-r under a target scale. Every two-channel filter bank on each node of this NSBT is designed to be a paraunitary perfect reconstruction filter bank, so NUDFB is an orthogonal filter bank. This dual multiresolution decomposition will definitely have bright prospect in its application, such as texture analysis, image processing or video coding. A potential application is presented by applying NUDFB in wavelet domain.
Side information generation with constrained relaxation for distributed multi-view video coding

Apart from the existing temporal and inter-view interpolation technique in distributed multi-view video coding (DMVC), this paper is dedicated to not only investigating multiple side information implication at the decoder, but also preserving the constrained relaxation with high-level features matching. We present a novel feature-based Wyner-Ziv coding framework (FWZC) for DMVC, which devotes scale-invariant features extraction and matching to generate inter-view correlated side information without knowing the camera parameters and has a more significant improvement rate-distortion performance. The scale-invariant local features are identified as the most resistant to image deformations and affine distortion between different views of an object or scene. The plausible filling-in from a priori distinctive feature constraints between neighboring views could make a promising compensation to inter-view side information generation for joint multi-view decoding. The experimental results show high precision for objects with high motion and quite significant improvement in the rate-distortion performance.
Monitoring and Troubleshooting in Operational IP-TV System

When deploying large scale of QoS guaranteed IP-TV services over Triple-Play networks, there is a growing demand to access to the live network services, monitor in real time the broadcast chains, acquire the dynamic network parameters to ensure an uninterrupted, high quality service. Considering the difference of IP packet switching network and the de-facto transport stream (TS) standard of broadcast digital video network, this paper describes design consideration and implementation of a powerful embedded IP-TV monitor for real time monitoring the IP-TV QoS and using this equipment to construct a payload-aware monitoring network for on-line troubleshooting when the QoS deteriorate. The monitor should be designed to have ability to monitor and troubleshoot the blended combination of network status and digital TV quality in real time and cross layer way, meanwhile the procedures of monitoring does not interfere with the on-line services. This monitor can be deployed in the networks to measure the three ultimate metrics: IP cumulative jitter, packet loss and MPEG TS errors and provide helpful information about the network and service when service failed. Implementation and experimental results show promise of being practical in the real IP-TV trial networks.
Receiver-driven layered overlay multicast for scalable video streaming

In this paper, we seek the optimal overlay multicast performance for scalable video streaming. To adapt to layered multicast with multiple paths, a new metric layer stretch is introduced, measuring video layer's dissemination latency along the overlay. Multi-path routing, network coding based layer subscription and congest control are jointly optimized, formulating a linear programming to minimize the average layer stretch over all receivers. It not only solves the issue of inter-layer dependency of scalable video coding, but allows each receiver to determine the required video quality. Moreover, a heuristic algorithm that can be implemented in a distributed manner is provided to approximate the optimal solution. Simulation results validate the network performance of the proposed scheme.
Subspace Learning with Structured Sparsity for Compressive Video Sampling

Existing sparse representation with subspace learning is hampered by the intersection of subspaces of bases. With structured sparsity to enable the prior knowledge of signal statistics, this paper proposes a novel compressive video sampling by subspace learning to minimize the intersection of subspaces. As the measurement, the block coherence is optimized with the regularized learning to generate a class of independent bases associated with the subspaces. Thus, the proposed framework can make a compact block sparse representation based on the derived basis in an efficient and adaptive manner. The block-based recovery of video sequences is demonstrated to be stable under the constraint of block restricted isometric property (RIP). Experimental results show that the proposed method outperforms existing compressive video sampling schemes.