nils-herrmann/clean_scirepeval huggingface dataset:
Wyner–Ziv Switching Scheme for Multiple Bit-Rate Video Streaming

This paper proposes a Wyner-Ziv (WZ) switching scheme for multiple bit-rate (MBR) video streaming over networks. Identical video content is encoded into a set of normal streams, which are generated by conventional hybrid video coding with multiple bit rates, so that streaming can dynamically switch among these normal streams according to available bandwidth. At encoder side, the WZ codec generates a switching stream by compressing the reconstructed frames of a certain normal stream that will be switched to, no matter which normal stream it switches from. At decoder side, for switching to the same frame, the same WZ switching stream is used to reconstruct the switching-to frame by taking the switching-from frame as the side information. The number of required WZ bits depends on the inherent mutual correlation between two frames switching to and from. Since the WZ switching streams are generated independently of the normal switching-from streams, given normal streams that can switch from any one to another, the proposed scheme reduces the number of switching streams from to . Furthermore, switching streams do not deteriorate the coding efficiency of normal streams when no switching occurs. However a big problem here, similar to requesting bits in distributed video coding, is how many WZ bits should be transmitted when a switching happens because the streaming scenario does not tolerate too much extra delay caused by the requests back and forth. Therefore, a Laplacian model, which is proved in the simplified case, is proposed to characterize the correlation between switching-to and switching-from frames. It can be used to accurately estimate the number of WZ bits at the server side.
An Optimized Hardware Video Encoder for AVS with Level C+ Data Reuse Scheme for Motion Estimation

In a hardware video encoder, Level C+ data reuse for motion estimation can reuse two-dimensional overlapped search window (SW) and thus is a good choice to trade off the memory bandwidth with the on-chip buffer size. However, the irregular zigzag coding order brings some other troubles to the encoder implementation. This paper mainly focuses on the special considerations for a Level C+ zigzag encoder. First we present a guideline about how to select the Level C+ zigzag HFmVn scan for the adopted encoder pipeline. Second, according to the guideline, zigzag HF5V3 coding order is applied into our Level C+ encoder in which a new function is added to alter zigzag bit-stream into standard raster order and exact motion vector predictor (MVP) can be used for most macro blocks (MBs) except some corner MBs to increase the coding performance. Third, zigzag-aware scheduling for prefetching the SW is proposed so that the pipeline will never be disturbed by this irregular coding order and can smoothly run MB by MB. In addition, balancing the bandwidth into each MB processing period can improve the bandwidth utilization. With these techniques, a real-time high-definition (HD) 1080P AVS encoder is successfully implemented on FPGA verification board with search range [-128, 128]×[-96, 96] and two reference frames at an operating frequency of 160 MHz.
An adaptive skin color detection algorithm with confusing backgrounds elimination

In this paper a new skin detection method based on adaptive thresholds is proposed. Compared with the fixed threshold histogram method used widely, ours can find optimal thresholds to the different complex backgrounds. Four clues are summarized from the skin probability distribution histogram (SPDH) to help search candidates of optimum thresholds, and an ANN classifier is trained to select the final optimum threshold. A color deviation histogram (CDH) is also proposed to eliminate confusing backgrounds and refine optimal thresholds. The selection process of optimal thresholds is fast thus appropriate for real-time applications since no iterative operation is involved. Experimental results show that the proposed method can achieve better performance than the fixed threshold histogram method.
Frame layer rate control for dual frame motion compensation

Rate control plays an important role in video coding. When jump update dual frame motion compensation (JU-DFMC) is utilized in video coding, two kinds of frames are existed according to bit allocation, one is low quality frame (LQF), the other is high quality frame (HQF). For each current frame (LQF or HQF), one short term reference frame (STR) and one long term reference frame (LTR) are utilized for motion compensation. Owning to this kind of coding structure, rate control for JU-DFMC is different from traditional methods. In this paper, a rate control scheme for JU-DFMC is proposed. Firstly, a linear bit allocation method is proposed for LOFs to obtain smooth quality. Secondly, different source rate and quantization stepsize (Qstep) models are proposed to different frames (LQFs or HQFs). Thirdly, overhead prediction and MAD prediction are presented. Experimental results show that the proposed method can bring accurate rate control for JU-DFMC, at the same time the PSNR is better than the other methods.
A novel integer-pixel motion estimation algorithm based on quadratic prediction

This paper presents a fast integer-pixel motion estimation (ME) algorithm for High Efficiency Video Coding (HEVC), which provides a strategy to speed up the search process significantly, while yielding the same quality performance as the Test Zone Search (TZSearch) scheme in HEVC Test Model (HM). Compared with the H.264/AVC, the ME process employs a more complex hybrid coding architecture and a larger size search window in HEVC, leading to great computational complexity. We utilize limited pixels of certain position in the current search range to build a quadratic model, and then shrink the search range repeatedly by analyzing the sum of absolute difference (SAD) distribution until the best motion vector (MV) is obtained. The proposed algorithm can be applied to various encoding conditions. Experimental results show that our method can save 56% of computations compared with the TZSearch scheme, with negligible decrease of coding quality.
Reconstruction problems in 3D for viral cryo electron microscopy

Cryo electron microscopy of viruses provides 2D projections of the scattering intensity of the viral particle but the orientation of the projections is not known. We describe an approach to reconstructing the 3D scattering intensity in spite of the unknown projection orientations using nonlinear least squares ideas where the reconstruction is guaranteed to have the icosahedral symmetry known to be present in the viral particle.
Robust Estimation of 3D Human Poses from a Single Image

Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose after projection due to the lack of depth information. Moreover, current 2D pose estimators are usually inaccurate which may cause errors in the 3D estimation. We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons. (ii) We enforce limb length constraints to eliminate anthropomorphically implausible skeletons. (iii) We estimate a 3D pose by minimizing the 1-norm error between the projection of the 3D pose and the corresponding 2D detection. The 1-norm loss term is robust to inaccurate 2D joint estimations. We use the alternating direction method (ADM) to solve the optimization problem efficiently. Our approach outperforms the state-of-the-arts on three benchmark datasets.
Context-adaptive pixel based prediction for intra frame encoding

Intra prediction is one effective method to remove the spatial redundancies in intra frame coding. Better intra prediction will result in the residual with less energy, which will decrease the number of bits needed to reconstruct the signal at decoder. To improve the accuracy of intra prediction, a context-adaptive pixel based prediction (CAPBP) algorithm is proposed in this paper. For each pixel within the target block to be encoded, the prediction is calculated as the linear weighted summation of the reconstructed pixels within the left column and the above row. Based on the assumption that pixels having the same coordinates within one block own the same prediction weights, we calculate the corresponding weights for each pixel within the target block by the least square method. The same processing is also performed at decoder; hence the prediction weights do not need to be sent to the decoder. Experimental results verify that the proposed algorithm is able to improve the efficiency of intra frame coding up to
Rate-adaptive Compact Fisher Codes for Mobile Visual Search

Extraction and transmission of compact descriptors are of great importance for next-generation mobile visual search applications. Existing visual descriptor techniques mainly compress visual features into compact codes of fixed bit rate, which is not adaptive to the bandwidth fluctuation in wireless environment. In this letter, we propose a Rate-adaptive Compact Fisher Codes (RCFC) to produce a bit rate scalable image signature. In particular, RCFC supports fast matching of descriptors based on Hamming distance; meanwhile, low memory footprint is offered. Extensive evaluation over benchmark databases shows that RCFC significantly outperforms the state-of-the-art and provides a promising descriptor scalability in terms of bit rates versus desired search performance.
A Biologically Inspired Energy-Aware Routing Algorithm for Communications in Cyberworlds

This paper presents a new version of swarm-intelligence inspired ad hoc routing algorithm for communications in Cyberworlds, based on evolutionary cooperation in a biological swarm. We use the principle of swarm intelligence to reinforce good quality routes with only local communication. The data traffic is influenced at each node, and the communicating nodes observe this influence to update their tables. By locally monitoring the network transmission queue length and other MAC layer information, this algorithm can forward data traffic through paths that avoid network congestion areas. In order to conserve energy, this algorithm intelligently power off the network interfaces of the nodes that are either not currently involved in any communications or in the area where congestion occurs. We also include an evaluation methodology to simulate ad hoc networks, and the simulation results show that this novel routing algorithm performs well for a variety of network conditions.
FACOLA-face coder based on location and attention

(face coder based on location and attention) is proposed for potential applications such as compression of face pictures used in IC and ID cards. The face locator locates a face in an image using template matching and eigenface techniques. The attention detector detects high attention and low attention in the located face according to their different frequency characteristics. For high attention, usually high quality or lossless coding is required. The DCT is not suitable for such a case because it will not lead to an efficient compaction of the image energy and variable length coding (VLC) cannot be done efficiently. So a DPCM coder with three directional predictors is presented instead of the DCT. The prediction difference is quantized and the entropy coded DPCM coder supports lossless and lossy compression. The DCT coder is adopted for low attention and non-face area compression using different quantization factors.
Hierarchical ensemble of Gabor Fisher classifier for face recognition

Gabor feature has been widely recognized as one of the best representations for face recognition. However, traditionally, it has to be reduced in dimension due to curse of dimensionality. In this paper, an ensemble based Gabor Fisher classifier (EGFC) method is proposed, which is an ensemble classifier combining multiple Fisher discriminant analysis (FDA)-based component classifiers learnt using different segments of the entire Gabor feature. Since every dimension of the entire Gabor feature is exploited by one component FDA classifier, we argue that EGFC makes better use of the discriminability implied in all the Gabor features by avoiding the dimension reduction procedure. In addition, by carefully controlling the dimension of each feature segment, small sample size (3S) problem commonly confronting FDA is artfully avoided. Experimental results on FERET show that the proposed EGFC significantly outperforms the known best results so far. Furthermore, to speed up, hierarchical EGFC (HEGFC) is proposed based on pyramid-based Gabor representation. Our experiments show that, by using the hierarchical method, the time cost of the HEGFC can be dramatically reduced without much accuracy lost
Classifiability-Based Discriminatory Projection Pursuit

Fisher's linear discriminant (FLD) is one of the most widely used linear feature extraction method, especially in many visual computation tasks. Based on the analysis on several limitations of the traditional FLD, this paper attempts to propose a new computational paradigm for discriminative linear feature extraction, named “classifiability-based discriminatory projection pursuit” (CDPP), which is different from the traditional FLD and its variants. There are two steps in the proposed CDPP: one is the construction of a candidate projection set (CPS), and the other is the pursuit of discriminatory projections. Specifically, in the former step, candidate projections are generated by using the nearest between-class boundary samples, while the latter is efficiently achieved by classifiability-based AdaBoost learning from the CPS. We show that the new “projection pursuit” paradigm not only does not suffer from the limitations of the traditional FLD but also inherits good generalizability from the boundary attribute of candidate projections. Extensive experiments on both synthetic and real datasets validate the effectiveness of CDPP for discriminative linear feature extraction.
Joint learning for side information and correlation model based on linear regression model in distributed video coding

The coding efficiency of distributed video coding system is significantly determined by the side information quality and correlation model. Motivated by theoretical analysis of the maximum likelihood treatment for linear regression model, we propose a novel joint online learning model for side information generation and correlation model estimation in this paper. In our proposed scheme, each pixel in the side information is approximated as the linear weighted combination of samples within a local spatio-temporal neighboring space. Weights are trained in a self-feedback fashion, during which the correlation model parameters can also be achieved. The efficiency of the proposed joint learning model is confirmed experimentally.
A prototype of ROIC for pyroelectric uncooled IRFPA with colume-level ADC

To meet the interest in low power, low cost and small volume, uncooled Infrared Focal Plane Array(IRFPA) and Readout Integrated Circuit(ROIC) with ADC are both emphases for the third generation IRFPA. This paper presents a prototype verification of ROIC with a 14-bit column-level ADC for pyroelectric array. The architecture of ADC is not sensitive to comparator's delay. Therefore, low power and low noise design can be achieved. A prototype of ROIC with column-level ADC is implemented with a 0.5μm DPTM process. And the circuit can be expended to 384×288 array operating at 60Hz. The current of each column ADC is about 6μA.
Decision on maintenance period for power transformer based on Monte Carlo simulation of reliability

The problem of selecting a suitable maintenance period is vital for preventive maintenance of power transformer. Traditionally, maintenance period can be calculated by model with an objective function, such as availability, maintenance cost, or reliability of the equipment. However, it is difficult to directly apply these models for power transformer, because its reliability is high, and there is no enough reliability data to calculate the objective functions. Therefore, a simulated maintenance period model is developed in this paper. Firstly, Monte Carlo method and Fault Tree method are applied to generate simulated fault times of the transformer system. Secondly, Mission Reliability is calculated based on these simulated data and acts as the objective function in the model. Finally, this model is applied to investigate the maintenance period of power transformer.
Maximal Linear Embedding for Dimensionality Reduction

Over the past few decades, dimensionality reduction has been widely exploited in computer vision and pattern analysis. This paper proposes a simple but effective nonlinear dimensionality reduction algorithm, named Maximal Linear Embedding (MLE). MLE learns a parametric mapping to recover a single global low-dimensional coordinate space and yields an isometric embedding for the manifold. Inspired by geometric intuition, we introduce a reasonable definition of locally linear patch, Maximal Linear Patch (MLP), which seeks to maximize the local neighborhood in which linearity holds. The input data are first decomposed into a collection of local linear models, each depicting an MLP. These local models are then aligned into a global coordinate space, which is achieved by applying MDS to some randomly selected landmarks. The proposed alignment method, called Landmarks-based Global Alignment (LGA), can efficiently produce a closed-form solution with no risk of local optima. It just involves some small-scale eigenvalue problems, while most previous aligning techniques employ time-consuming iterative optimization. Compared with traditional methods such as ISOMAP and LLE, our MLE yields an explicit modeling of the intrinsic variation modes of the observation data. Extensive experiments on both synthetic and real data indicate the effectivity and efficiency of the proposed algorithm.
Enhanced direct mode coding for bi-predictive pictures

The direct mode coding in the bi-predictive pictures (B-pictures) can efficiently improve the performance of bi-predictive coding, because it exploits the temporal correlation by bi-directional prediction from both forward and backward reference pictures, and meanwhile it does not require any bits for coding the motion vectors. Accordingly, how to accurately calculate the motion vectors in terms of direct mode coding is very important to obtain better prediction values. In other words, it is more desirable to obtain the true motion vectors, because the derived motion vectors need not be coded. The traditional direct mode coding usually derives the motion vector of the current block by scaling the motion vector of the co-located block in the subsequent reference pictures. It is based on the assumption that the near blocks would have the same motion trajectory. In this paper, we present an improved method based on combining motion vector tracking technique and spatial motion vector prediction technique. The proposed method can accurately calculate the motion vectors for the direct mode coding.
Power capacity specification for energy storage in wind application using probability- based method

To accommodate the wind power fluctuation, a hybrid energy storage system (HESS) consisting of battery energy system (BESS) and super-capacitor is adopted in this paper. A probability-based power capacity specification approach for the BESS and super-capacitors is proposed. Through this method the capacities of BESS and super-capacitor are properly designed to combine the characteristics of high energy density of BESS and the characteristics of high power density of super-capacitor. It turns out that the super-capacitor within HESS deals with the high frequency fluctuations, which contributes to the extension of BESS lifetime, and the super-capacitor can handle the peaks in wind power fluctuations without the severe penalty of round trip losses associated with a BESS. The proposed approach has been verified based on the real wind data from an existing wind power plant in Iowa.
Empirical comparisons of several preprocessing methods for illumination insensitive face recognition

Illumination variation is one of the bottlenecks of face recognition systems. Many approaches to coping with illumination variations have been proposed. They can be categorized into model-based and preprocessing-based. Although the model-based approaches seem better in theory, they commonly introduce more constraints, which make them not practical enough for real applications. On the other hand, the preprocessing approaches commonly exploit simple and efficient image processing techniques. Typical approaches based on image processing include histogram equalization (HE), histogram specification (HS), logarithm transform (Log), gamma intensity correction (GIC), and self-quotient image (SQI). We have performed extensive experiments to analyze and compare these methods empirically by evaluating them on three large-scale face databases: CMU-PIE database, FERET database; CAS-PEAL database. Our experimental results show that HE, HS and GIC can improve recognition performance for images both with and without illumination variation, while Log and SQI may decrease the recognition rate for face images without much illumination variation, though they may facilitate the recognition of face images with illumination variation.
Layer-based image completion by poisson surface reconstruction

Image completion has been widely used to repair damaged regions of a given digital image in a visually plausible way. However, it is difficult to infer appropriate information, meanwhile keep globally coherent just from the origin image when its critical parts are missing. To address this problem, we propose a novel layer-divided image completion scheme, which contains two major steps. First, we extract foregrounds of both target image and source image, and then we apply a guided Poisson surface reconstruction technique to complete the target foreground according to parameters obtained from optimal-matching calculation. Second, to fill the remaining damaged part, a related exemplar-based image completion algorithm is further devised. Several experiments and comparisons show the effectiveness and robustness of our proposed algorithm.
A Proposed Intelligent Vehicle Telematics Service System (IVTS)

Location-based vehicle navigation services, monitoring services and traffic information services are three categories of transportation-related applications, and tend to converge at a new domain now, i.e., vehicle telematics. This paper addresses a uniform system, so-called intelligent vehicle telematics service system (IVTS). Firstly, the four-part structure is put forward, including traffic information collection sub-system, service centers (inc. traffic information center, map server and monitoring center), communication infrastructures and in-vehicle telematics terminal. Secondly, five related issues are discussed in detail respectively, that is, traffic information encoding, traffic-data-based dynamic route calculation, dynamic updating of navigable digital map, server-side multi-user response, and hardware terminal incorporation. Lastly, experimental systems are illustratively exhibited