nils-herrmann/clean_scirepeval huggingface dataset:
Auto-regressive model based error concealment scheme for stereoscopic video coding

Stereoscopic video is an important manner for 3-D video applications, and robust stereoscopic video transmission has posed a technical challenge for stereoscopic video coding. In this paper, an auto-regressive (AR) model based error concealment scheme is proposed for stereoscopic video coding to address the challenging problem. The proposed error concealment scheme includes a temporal AR model for independent view, and a temporal-interview AR model for inter-view predicted view. First, appropriate motions and disparities for lost blocks are derived. Then, the proposed AR model coefficients are computed according to the spatial neighboring pixels and their temporal-correlated and interview-correlated pixels indicated by the selected prediction directions. Finally, applying the AR model, each pixel of the lost block is interpolated as a weighted summation of pixels in the reference frame along the selected prediction directions. Simulation results show that the performance of the proposed scheme is superior to conventional temporal error concealment methods for stereoscopic video coding.
High-Performance CABAC Engine for H.264/AVC High Definition Real-Time Decoding

This paper presents an efficient VLSI architecture for H.264/AVC CABAC decoding. We introduce several new techniques to extremely exploit, to the largest extent possible, the parallelism of the decoding process, including line-bit-rate decoding, multiple bin arithmetic decoding and efficient probability propagation scheme. The CABAC engine can ensure the real-time decoding for H.264/AVC main profile HD level 4.0. synthesis results show that the multi-bin decoder can run up to 45 MHz, and the total area is only 42K gates.
Formulation of loss minimization problem using genetic algorithm and line-flow-based equations

Optimization problems in the steady state analysis of power systems aim at minimizing or maximizing some objective function. Traditional methods use mathematical programming techniques to obtain the optimum solution. Artificial intelligence (AI) methods have been shown to exhibit greater flexibility in solving the optimization problem. Genetic algorithm (GA) technique is used in the paper with a new formulation of system equations based on line flow variables. MATLAB GA toolbox is applied to solve the equations. An example system demonstrates the effectiveness of the formulation. Future extension are indicated.
Extended direct mode for hierarchical B picture coding

In 3D subband coding, motion-compensated temporal filtering (MCTF) technique, especially with the adaptive 5/3 wavelet kernel, efficiently exploits the temporal correlation among neighboring pictures. It can be achieved alternatively by the hierarchical B picture coding technique with the same decomposition as the temporal 5/3 lifting structure but without update step. This technique has been supported by H.264/AVC (2003) with stored B technique. The performance of hierarchical B obviously outperforms that of the conventional IBBP...GOP structure. Direct mode which derives motion vectors from the co-located block of backward reference and does not require any bits for coding motion vectors is a very efficient bi-prediction technique for B pictures coding. Considering that the forward motion vectors in the backward reference is not available when it belongs to block only with backward motion vector, in this paper, we propose an extended direct mode for hierarchical B picture coding to further improve the accuracy of derived motion vectors. It effectively utilizes the forward and backward motion vectors of the co-located blocks in references for scaling.
2D to 3D convertion based on edge defocus and segmentation

This paper presents a depth estimation method which converts two-dimensional images into three-dimensional data. Based on two-dimensional wavelet analysis of Lipschitz regularity for defocus estimation on edges, this method can effectively eliminate the horizontal stripes in the depth map resulted from traditional one-dimensional wavelet based approaches. Besides, we also propose several techniques such as edge enhancement, color-based segmentation, and depth optimization to obtain a more reliable and smoother depth map. The experimental results demonstrate the effectiveness of our proposed techniques.
A Concatenational Graph Evolution Aging Model

Modeling the long-term face aging process is of great importance for face recognition and animation, but there is a lack of sufficient long-term face aging sequences for model learning. To address this problem, we propose a CONcatenational GRaph Evolution (CONGRE) aging model, which adopts decomposition strategy in both spatial and temporal aspects to learn long-term aging patterns from partially dense aging databases. In spatial aspect, we build a graphical face representation, in which a human face is decomposed into mutually interrelated subregions under anatomical guidance. In temporal aspect, the long-term evolution of the above graphical representation is then modeled by connecting sequential short-term patterns following the Markov property of aging process under smoothness constraints between neighboring short-term patterns and consistency constraints among subregions. The proposed model also considers the diversity of face aging by proposing probabilistic concatenation strategy between short-term patterns and applying scholastic sampling in aging prediction. In experiments, the aging prediction results generated by the learned aging models are evaluated both subjectively and objectively to validate the proposed model.
Adaptive Block-Size Transform Based on Extended Integer 8×8/4×4 Transforms for H.264/AVC

In this paper, a low-complexity adaptive block-size transform (ABT) scheme is proposed for the H.264/AVC. The integer 8x8/4x4 discrete cosine transforms with an extended relation are applied to the proposed ABT scheme for reducing the implementation complexities. In the proposed scheme, the 8x8/4x4 transforms can be merged together and share the same scale matrix. As a result, the transform units and storage resources can be effectively saved in both encoder and decoder. The 8x8 transform extended from the 4x4 transform of H.264/AVC can be easily implemented with several simple arithmetic operators, and its intermediate results can be strictly limited within 16-bit. The experimental results show that the proposed ABT scheme based on extended transforms achieves a similar coding performance to the original ABT scheme for H.264/AVC.
Enhancing Human Face Detection by Resampling Examples Through Manifolds

As a large-scale database of hundreds of thousands of face images collected from the Internet and digital cameras becomes available, how to utilize it to train a well-performed face detector is a quite challenging problem. In this paper, we propose a method to resample a representative training set from a collected large-scale database to train a robust human face detector. First, in a high-dimensional space, we estimate geodesic distances between pairs of face samples/examples inside the collected face set by isometric feature mapping (Isomap) and then subsample the face set. After that, we embed the face set to a low-dimensional manifold space and obtain the low-dimensional embedding. Subsequently, in the embedding, we interweave the face set based on the weights computed by locally linear embedding (LLE). Furthermore, we resample nonfaces by Isomap and LLE likewise. Using the resulting face and nonface samples, we train an AdaBoost-based face detector and run it on a large database to collect false alarms. We then use the false detections to train a one-class support vector machine (SVM). Combining the AdaBoost and one-class SVM-based face detector, we obtain a stronger detector. The experimental results on the MIT + CMU frontal face test set demonstrated that the proposed method significantly outperforms the other state-of-the-art methods.
New bi-prediction techniques for B pictures coding [video coding]

In the conventional video codec, B pictures usually employ five prediction modes, including bi-prediction, direct prediction, forward prediction, backward prediction and intra modes. Among them, the direct and bi-prediction modes, whose prediction values are achieved from both forward and backward reference pictures, are more efficient in exploiting the temporal correlation between the reference pictures and the current B picture. In addition, the direct mode does not require any bits for coding the motion vectors. Therefore, the blocks coded with direct or bi-predictive mode are usually much more than the other modes. To further take advantage of the bi-prediction, We propose a new bi-predictive coding technique, which can achieve a good tradeoff between the bit-rate saving for motion vector coding and the prediction accuracy. Moreover, we also propose spatial motion vector prediction and motion vector scaling techniques to improve the accuracy of the derived direct mode motion vectors. All these techniques have been adopted in the AVS video coding standard.
Hierarchical Ensemble of Global and Local Classifiers for Face Recognition

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.
Internal generative mechanism inspired reduced reference image quality assessment with entropy of primitive

In this paper, we propose a novel reduced-reference (RR) image quality assessment (IQA) algorithm based on the internal generative mechanism, which suggests that the human visual system (HVS) can actively predict the primary visual information and avoid the uncertainty. Specifically, the explanation of the visual scene is formulated as the process of sparse representation. In particular, the entropy of primitive accounts for the primary visual information and the discrepancy between the image signal and its best sparse description is regarded as the uncertainty in perception. As such, the combined feature that can summarize the primary visual information and uncertainty in sparse domain is required to be transmitted in the RR-IQA framework. Comparative studies of the proposed reduced reference metric is conduced on both single and multiple distortion databases, and experimental results demonstrate that the proposed metric can achieve high correlation with the human perception by only sending ignorable additional information.
A hierarchical charging strategy for electric vehicles considering the users' habits and intentions

With the rapid growth of electric vehicles (EVs), charging large-scale EVs will affect the security and economy of power system operation. When developing the charging strategy, not only technical and economic issues but also the patterns of EVs' utilization should be considered. This paper presents a hierarchical charging strategy for EVs considering both system level and user level benefits, together with the travel habits and intentions of users. Firstly, a system level charging strategy is proposed as a multi-objective optimization problem, which can minimize the customers' charging fees, reduce the peak-valley difference and smooth the load fluctuation. Then a distribution charging strategy of EVs at user level based on the users' habits and intentions is presented, which aims for the minimization of each EV battery's charging times based on historical travel miles and real-time State of Charge (SOC). Finally, a case study indicates the effectiveness of proposed methodology to meet the requirements of power system and users.
Linear transform based motion compensated prediction for luminance intensity changes

A linear transform based motion compensated prediction (LT-MCP) scheme is proposed in this paper to improve the efficiency of inter-frame prediction when the intensity of luminance changes abruptly across frames. Either uniform or non-uniform intensity changes can be well processed as the proposed scheme is applied at macroblock (MB) level. LT-MCP is implemented based on the H.264 reference software to evaluate its performance. For those sequences with significant luminance changes across frames, such as Crew, we can achieve about 1.5dB improvement over the conventional MCP with intra prediction disabled and about 0.8dB gain with intra prediction enabled. For those sequences without obvious luminance changes, the proposed scheme also works better.
Face reconstruction using fixation positions and foveated imaging

Face representation is important for face recognition system. Though most popular face representations are based on uniform grid sampling, some recent face recognition systems adopt weighted sampling on the different regions of a face. Psychological analysis of visual attention or human eye fixations on human face images may suggest some cues for face representation. human visual system (HVS) gives different weight to different region of human face via space-variant sampling on fovea and non-uniform distribution of fixations. This paper focuses on the problem of simulation of the foveated imaging phenomenon in HVS, and introduction of foveated imaging method into reconstruction of face in region of interest (ROI) using different fixation sources. We compare the effectiveness of actual fixation on reconstruction of face in ROI with uniform, random distribution fixation as well as fixation generated by artificial model. The experimental results on 100 face images from FRGC [7] data set show that actual fixation positions and model-generated fixation positions reconstruct the face in ROI with considerably better quality. A further analysis on the statistics of fixation positions also shows that the distribution of the fixation points is consistent with the weights of different regions on face images used in some other face recognition systems.
Fast disparity estimation utilizing depth information for multiview video coding

Multiview video coding (MVC) improves the coding efficiency by motion estimation (ME) and disparity estimation (DE). ME and DE at encoder side involve in heavy computation, which needs to be further reduced for practical applications. This paper presents fast disparity estimation by utilizing depth information to reduce DE's computational complexity. First, the coordinate offset of the encoding block is calculated by 3D warping in the reference view frame utilizing depth map. Then the search region for DE is adjusted according to the coordinate offset. Experimental results verify that the proposed algorithm can save roughly 50% coding time with negligible loss of coding efficiency.
A novel algorithm of spatial scalability for scrambled video

Although much research have investigated transparent scrambling video techniques, an issue of which transcoding relates to downsizing scrambled video without unscrambling has hardly been solved in single-layer MPEG-2 coded video content. In the paper, a novel algorithm based on approximation for compressed domain linear operation and using a mix of linear transformation of coefficient values in DCT for scrambling video is proposed for downsizing the scrambled video by a factor of two. Experimental results show that the proposed algorithm can reduce computational complexity and efficiently degrade video quality. Compared to the existing scrambling video methods, this algorithm not only can scramble video that is compliant to block-coded format but also can realize downsizing the scrambled video in the compressed domain by a factor of two without unscrambling and impact on the performance of the compression.
PCNN Edge Detection of Sintering Pellets Image Based on Hybrid Harmony Search Algorithm

Harmony search (HS) is a newly developed derivativefree and meta-heuristic algorithm mimicking the improvisation process of musicians, which has been very successful in a wide variety of optimization problems. A hybrid optimization method is proposed for self-tuning pulse coupled neural network (PCNN) parameters, a biologically inspired spiking neural network, based on harmony search algorithm and simulated annealing (SA) idea and was used to detect sintering pellets image edges automatically and successfully. The effective of the proposed method is verified by simulation results, that is to say, the quality of the sintering pellets grayscale image edge detection is much better and parameters are set automatically.
Eigen features technique and its application

We introduce a basic technique, eigen features, which has been proved effectively in the field of human face perception. Its spirit is to represent pattern categories with essence of the data by getting rid of relativity among the data. We first describe the principle of eigen features and the measure of realization. Then, we introduce our three human face perception system which are based on eigen features-human face detection and tracking system, ID card photograph identification system and facial expression recognition system.
Patch-Based Gabor Fisher Classifier for Face Recognition

Face representations based on Gabor features have achieved great success in face recognition, such as elastic graph matching, Gabor Fisher classifier (GFC), and AdaBoosted Gabor Fisher classifier (AGFC). In GFC and AGFC, either down-sampled or selected Gabor features are analyzed in holistic mode by a single classifier. In this paper, we propose a novel patch-based GFC (PGFC) method, in which Gabor features are spatially partitioned into a number of patches, and on each patch one GFC is constructed as component classifier to form the final ensemble classifier using sum rule. The positions and sizes of the patches are learned from a training data using AdaBoost. Experiments on two large-scale face databases (FERET and CAS-PEAL-R1) show that the proposed PGFC with only tens of patches outperforms the GFC and AGFC impressively
Real time simulation for wind power generation system using RTDS

This paper presents a real time simulation method of wind power generation system with doubly fed induction generator (DFIG) using real time digital simulator (RTDS). A vector control strategy is proposed to realize decoupled control of real power and reactive power. Rotor side converter controls the complex power of stator while grid side converter regulates the DC link voltage and the reactive power injected from rotor circuit. The whole wind power generation system and its controllers are modeled and implemented in RSCAD, real time simulation is carried out to test the system and proposed control methods.
A novel RED-Based hop-by-hop congestion control

With the rapid emergence of new services and quick changes of traffic in the Internet, the widely used end-to-end congestion control mechanism becomes insufficient. This paper presents a novel RED-based hop-by-hop congestion control mechanism which is based on the coordination of the routers and the hosts. No per-flow state information is maintained in the routers. The application itself need not take care of the rate adaptation. The mechanism provides a uniform solution accommodating both responsive and unresponsive traffic with trivial overhead. It reduces the packet loss to zero and leads to higher network resource utilization. Response time to congestion is greatly reduced. The mechanism has been implemented in the Linux kernel. Some preliminary experiments show its advantages.
Contract design of CLSC with recycled quantity affected by the interaction of recycling price and sales volume

On the base of the assumption that the recycled quantity of used products is affected by the interaction of recycling price and sales volume, this paper researches closed-loop supply chain's pricing and coordination, gives system members' optimal pricing and profits in decentralized decision and centralized decision and finds that reverse supply chain itself is in coordination and traditional forward supply chain is in incoordiantion under decentralized decision. Then it designs a revenue sharing contract which can coordinate closed-loop supply chain. At last, a numerical example is given to verify the conclusions drew in this paper.
A low-noise fully-differential open-loop interface for high-G capacitive micro-accelerometers with 112.2 dB dynamic range

A fully-differential open-loop switched capacitor interface is presented in this paper for a ±30 g capacitive micro-accelerometer. Considering that input common-mode feedback capacitors in a fully-differential charge sensitive amplifier increase the gain of noise, a full-swing input common-mode feedback scheme is proposed in this work to minimize the feedback capacitance and reduce the noise. Moreover, since flicker noise contributed by the output stage cannot be ignored in an ultra low-noise design, a novel low-noise fully-differential sample- and-hold circuit is proposed, which not only suppresses the flicker noise considerably but also has smaller output spikes with lower power dissipation. The interface implemented in a 0.35 μm CMOS process occupies 9 mm2 and consumes 8.11 mA from a 3.3-V supply at a sampling clock of 1 MHz. The sensor achieves 80.4 mV/g sensitivity, 10 μg/√Hz resolution and 112.2 dB dynamic range over a signal bandwidth of 200 Hz.
Local Stereo Matching with Improved Matching Cost and Disparity Refinement

Recent local stereo matching methods have achieved comparable performance with global methods. However, the final disparity map still contains significant outliers. In this article, the authors propose a local stereo matching method that employs a new combined cost approach and a secondary disparity refinement mechanism. They formulate combined cost using a modified color census transform and truncated absolute differences of color and gradients. They also use symmetric guided filter for cost aggregation. Unlike in traditional stereo matching, they propose a novel secondary disparity refinement to further remove the remaining outliers. Experimental results on the Middlebury benchmark show that their method ranks fifth out of 153 submitted methods, and it's the best cost-volume filtering-based local method. Experiments on real-world sequences and depth-based applications also validate the proposed method's effectiveness.
A flexible VLSI architecture of transport processor for an AVS HDTV decoder SoC

In this paper, we present a VLSI design of transport processor for an AVS HDTV decoder SoC. The design provides a flexible data flow, which supports both broadcasting service and IPTV service with DVR capability. The design is characterized by two-bus architecture, in which a RISC CPU is used for control of general purpose and some dedicated hardware for accelerating data processing. The common on-chip SRAM is used to store input transport packets and the intermediate result in order to improve system performance and reduce the area. The design is described in Verilog HDL, simulated with VCS simulator, and implemented using 0.18 mum CMOS cell library. The circuit costs about 75 k equivalent logic gates and the processing capability is up to 90 Mbps