nils-herrmann/clean_scirepeval huggingface dataset:
A novel VLSI architecture of motion compensation for multiple standards

Motion compensation (MC) is one of the most important technologies capable of removing the temporal redundancy and widely adopted by the main video standards. From the older MPEG-2 to the latest H.264 and the Chinese AVS, many efficient coding tools have been introduced into MC, such as new motion vector prediction, bi-directional matching, quarter precision interpolation, etc. However, these new features enormously increase the computational complexity and the memory bandwidth consumption. In this paper, we introduce a novel architecture design of motion compensation (MC) for multiple video standards including MPEG-2, H.264, and AVS. The proposed design has a macroblock- level pipelined structure which consists of MV predictor, cache-based Fetch, and pixel interpolation unit. The proposed architecture exploits the parallelism in MC algorithm to accelerate the processing speed and uses the dedicated design to optimize the memory access. MV predictor unit can cover all MV prediction algorithms for the three standards and provide a simple error concealment scheme. Cache-based Fetch unit can save 25% memory bandwidth of MC in average and doesn't impact the performance in the worst case. Pixel interpolation unit adopts fully separate 1D filtering structure which is designed to effectively avoid the redundant calculations. The architecture can achieve the real-time multiple-standard decoding for HDTV 1080i (1920*1088 4:2:0 60 field/s) video. The efficient design can work at the frequency of148.5MHz and the total gate count for logic circuit s is about 56K.
Compressive-Sensed Image Coding via Stripe-based DPCM

These years have seen the advances of compressive sensing (CS), but efficient coding of sensed measurements is still an issue. In this paper, we propose an image coding system based on the compressive sensing paradigm via stripe-based differential pulse-code modulation (DPCM). In the system, we sample and encode an image in a unit of multiple rows, which we call a stripe. Through extensive experiments, we observe that the correlation between measurements of adjacent stripes are much higher than that of the neighboring blocks. Based on this, we combine the stripe-based CS acquisition with the DPCM framework and design a mechanism that predicts a stripe of measurements from its preceding stripe of measurements. The produced measurement residuals are then quantized and entropy-encoded into binary coding bits, which are tremendously reduced compared to the traditional block-based framework. Furthermore, we provide an image CS reconstruction algorithm corresponding to the stripe-based acquisition. Experiments verify that the reconstruction quality is no worse or even better than the block-based case when much lower bitrate is consumed. In a rate-distortion point of view, the proposed system also outperforms the methods using block-based sampling and achieves the state-of-the-art performance for compressive-sensed image coding.
Illumination transfer using homomorphic wavelet filtering and its application to light-insensitive face recognition

In this paper, we propose a novel homomorphic wavelet filtering based illumination transfer technique to change the dominant lighting of one face image (source face image) to another face image (reference face image ). Specifically, in the proposed method, based on the ldquoreflectance-illuminationrdquo imaging model, we first obtain an approximate estimate of the illumination component of the face image through a wavelet-based Multiresolution Analysis (MRA) in the logarithm domain of the input image. Then, a homomorphic filtering procedure is applied to improve the accuracy of the illumination component estimation. Finally, the source face image is re-lighted by substituting the estimated illumination component of the reference image for that of the source image. The proposed method is entirely an image processing based method without any 3D geometry modeling steps, so it is simple but effective. The method is also applied easily to illumination invariant face recognition by transferring a standard illumination to all the face images. Experimental results show that our method is quite effective for both illumination transfer and illumination-insensitive face recognition.
Condition assessment of power transformer using fuzzy and evidential theory

To meet the needs of risk assessment of power transformer in power system, a condition assessment model of power transformer based on fuzzy evaluation method and information fusion technology is proposed in this paper. Typical parameters from preventative test and on-line monitoring are selected to evaluate operational condition of power transformer. The selected condition parameters are normalized at first and then membership to different condition level are calculated. Assessment model with multi-information fusion method based on Dempster-Shafer evidential theory is employed. Moreover, data from routine tests and on-line monitoring of a 220kV power transformer are analyzed and the results verified the validity of the proposed model. It is indicated that the proposed model is helpful to improving the accuracy and timeliness of transformer condition evaluation by combining on-line monitoring parameters.
Inter-dependent rate-distortion modeling for video coding and its application to rate control

Rate control scheme using independent rate-distortion (R-D) model at the minimum coding unit (macroblock) level has been widely discussed in the literature where R-D optimization is performed without consideration of the inter-dependencies of different coding units. In this paper, we extend these techniques to the more general situations - rate control for inter-dependent video coding units. The interdependent distortion-quantization (D-Q) model and rate-quantization (R-Q) model are formulated separately based on the analysis of the relationship between the spatial-domain residual and the transform-domain residual. Then a window-based rate control scheme with frame bit allocation and video quality optimization is proposed, which uses the approximated R-D model to reduce the computational complexity. Simulation results demonstrate that the proposed algorithm shows excellent peak signal-to-noise ratio (PSNR) performance under the bit rate constraint. This one-pass rate control scheme is highly practical for the realtime video coding application.
Hierarchical dependency context model based arithmetic coding for DCT video compression

This paper presents an arithmetic coding scheme for DCT coefficients in video compression, in which the number of non-zero coefficients, significant map and level information for a DCT block are used as coding elements. To exploit the statistical correlations, an hierarchical dependency context model (HDCM) is proposed, where the number of non-zero coefficients and scanned position are used to capture the magnitude varying tendency of DCT coefficients. Then a new binary arithmetic coding using HDCM (HDCMBAC) is proposed to code the coding elements. Experimental results demonstrate that HDCMBAC can achieve the similar coding performance as CABAC at low and high QPs. Meanwhile the context modeling and arithmetic decoding in HDCMBAC can be carried out in parallel, since the context dependency only exists among different parts of basic coding elements in HDCM.
An Effective Mode Decision Scheme in Macroblock-Based PFGS

The progressive fine granularity scalable (PFGS) coding is a promising technique for streaming video applications. The macroblock-based PFGS coding proposes several INTER modes for the enhancement macroblock coding and provides a simple method to determine a coding mode for each macroblock. Because it does not consider the drifting error possibly occurs in decoder, the mode decision method is not effective. In this paper, we present a mode decision scheme to make a trade-off between the low drifting error and high coding efficiency, by adding a middle bit-rate loop in encoder to estimate the possible drifting error in decoder and using an R-D model to estimate the improvement of coding efficiency by selecting high quality reference. Then a nearly optimal coding mode for each enhancement macroblock can be easily determined. The experimental results show that the proposed scheme can effectively reduce the drifting error at low bit rates, while maintaining improved coding efficiency at high bit rates
High performance arithmetic coding for small alphabets

Generally, there are two main obstacles in the application of arithmetic coding. One is the relatively heavy computational burden in the coding part, since at least two multiplications are needed for each symbol. The other is that a highly efficient statistical model is hard to implement. We observe that under some important circumstances the number of different symbols in the data stream is definitely small. We specially design both the coding part and the modeling part to get a high performance arithmetic coder for the case of small alphabets. Our method is based on the improved arithmetic coding algorithm. We further improve it to be multiplication-free.
Dynamic MB-level Scheduling for parallel video coding

MB-level parallelism is widely used in parallel video coding thanks to its merits of low latency, no performance loss and high degree of parallelism. Most of video encoders with MB-level parallelism employ MB Row Scheduling (MRS) scheme. In software video encoder, early terminate algorithms tend to cause significant difference in coding time of different MBs. Consequently, the running speeds of multiple threads are unbalanced. When the number of threads is more than that of physical cores, the running speed unbalance is further worsened by computation resources competition among multiple threads. The computation resources of multiple cores can't be fully utilized without careful handling of the above running speed unbalance. Additionally, synchronization of multiple threads can also penalize the running speed of the whole video encoder. In this paper, we analyze the running speed unbalance of multiple threads in MRS scheme, and propose a new Dynamic MB-level Scheduling (DMS) scheme for parallel video coding. DMS alleviates both the running speed unbalance and synchronization delay among multiple threads on multi-core platform. Experiment results verified that video encoder with MRS can be accelerated in average 9% by our proposed DMS, when processors are fully utilized.
Neural-Network Aided Dynamic Control for Delivering Media Streams in Selfish Wireless Networks With Unknown Node-Selfishness

In this paper, we investigate the effect of selfish behaviors on the media stream delivery in selfish wireless networks with selfish relay nodes (RN). The RN’s selfish behavior of forwarding media streams, depicted as its degree of node-selfishness (DeNS), is affected by both its available resources and the received incentives, while an incentive mechanism is employed to depress the selfish behaviors of the RNs having limited available resources. Under the RN’s node-selfishness, the optimal incentives are controlled by the sources delivering media streams to maximize the finite-horizon utility, namely, the tradeoff between the path reliability and the incentive cost for delivering media streams during a finite time period. With the unknown node-selfishness owing to the dynamic information about the RNs’ available resources and their received incentives being private, we provide a dynamic node-selfishness model to formulate the dynamic of the RN’s DeNS, and further design an online neural-networks aided approximation scheme to identify the node-selfishness dynamics and to determine the optimal incentives. The convergence of this scheme is analyzed by Lyapunov techniques. Simulation results demonstrate the effectiveness of delivering media streams.
Adaptive multi-resolution motion estimation using texture-based search strategies

Motion estimation is the most complex module which contributes nearly 70% of computation resources in a hardware-based video encoder. This huge computational complexity limits the performance of HD video encoders in terms of encoding speed and power consumption. This paper presents a hardware oriented multi-resolution motion estimation algorithm using adaptive search strategies to reduce computational complexity. The spatial homogeneity and the temporal stationarity characteristics of video sequences are adaptively detected to determine search range and down-sampling rate. Homogeneous regions are detected by using Sobel edge operators and stationary regions are detected by using temporal information. These texture-based search strategies make motion estimation more concise under fixed computational complexity constraint. Additional computational cost originated by determining the search strategies can be neglected due to the simple addition and shift operations. Experimental results show that the proposed algorithm achieves better performance and reduces computation cost by 40% compared with previous works.
Multiscale Deep Alternative Neural Network for Large-Scale Video Classification

With the rapid increase in the amount of multimedia data, video classification has become a demanding and challenging research topic. Compared with image classification, video classification requires mapping a video that contains hundreds of frames to semantic tags, which poses many challenges to the direct use of advanced models originally designed for image-oriented tasks. On the other hand, continuous frames in a video also give us more visual clues that we can leverage to achieve better classification. One of the most important clues is the context in the spatiotemporal domain. In this paper, we introduce the multiscale deep alternative neural network (DANN), a novel architecture combining the strengths of both convolutional neural network and recurrent neural networks to achieve a deep network that can collect rich context hierarchies for video classification. In particular, the DANN is stacked with alternative layers, each of which consists of a volumetric convolutional layer followed by a recurrent layer. The former acts as a local feature learner, whereas the latter is used to collect contexts. Compared with popular deep feed-forward neural networks, the DANN learns local features and their contexts from the very beginning. This setting enables preserving context evolutions, which we show to be essential for improving the accuracy of video classification. To release the full potential of the DANN, we develop a deeper version with stochastic-layer skip-connections and construct a multiscale DANN to incorporate contexts at different scales. We show how to apply the multiscale DANN for video classification with carefully designed configurations in terms of both input–output settings and training–testing methods. The DANN is shown to be robust to not only human-centric videos, but also natural videos. As there are few large-scale natural disaster video datasets, we construct a new large-scale one and make it publicly available. Experiments on four datasets show the effectiveness of our method for both human actions and natural events.
Fault diagnosis of power transformer using feature selection techniques and KNN

Effective and correct fault diagnosis of power transformer is essential prerequisite for reliable power delivery. This paper presents a novel approach for power transformer incipient fault diagnosis based on feature selection techniques and K nearest neighbor algorithm. ReliefF, Laplacian Score and Fisher Score are employed to rank and select the most discriminative feature from 22 different features. 268 DGA samples are used to establish fault diagnosis model with the selected features. Fault diagnosis performance is evaluated with 5-fold cross validation methods. Fault diagnosis accuracy of the proposed methods achieves 72.5%. Results and comparisons against other conventional methods show relative superiority of feature selection based DGA interpretation approach in term of fault diagnosis accuracy.
Performance optimization and evaluation of V2G in regulated and deregulated microgrid

In this paper, an economic dispatch model with probabilistic modeling is developed for regulated microgrid. Due to the fluctuation of solar and wind plants' output, an empirical probabilistic model is applied to predict their hourly output. According to distinctive characteristics of wind and solar plants, the parameters for probabilistic distribution are further adjusted individually for both type of power plants. On the other hand, with the growing trend of Plug-in Electric Vehicle (PEV), an integrated microgrid system must also consider the impact of PEVs. Not only the charging loads from PEVs, but also the discharging output via Vehicle to Grid (V2G) method can greatly affect the economic dispatch for all the micro energy sources in microgrid. Firstly, this paper presents an optimization method for economic dispatch in regulated microgrid. Simulation is also performed to evaluate the performance of V2G in deregulated energy trading market. In this case, the trading market is considered as a noncooperative game. The simulation results in both cases reveal that PEVs with V2G capability can be an indispensable supplement in modern microgrid.
Mean-Shift Blob Tracking with Adaptive Feature Selection and Scale Adaptation

When the appearances of the tracked object and surrounding background change during tracking, fixed feature space tends to cause tracking failure. To address this problem, we propose a method to embed adaptive feature selection into mean shift tracking framework. From a feature set, the most discriminative features are selected after ranking these features based on their Bayes error rates, which are estimated from object and background samples. For the selected features, a criterion is proposed to evaluate their stability for tracking and to guide feature reselection. The selected features are used to generate a weight image, in which mean shift is employed to locate the object. Moreover, a simple yet effective scale adaptation method is proposed to deal with object changing in size. Experiments on several video sequences show the effectiveness of the proposed method.
Hybrid Electric Vehicle Modeling and Analysis in Generic Modeling Environment

This paper introduces a new modeling methodology based on generic modeling environment (GME), a software suitable for modeling and analysis of physical systems. Once a metamodel is configured in GME using unified modeling language (UML) and object constraint language (OCL) for a specific engineering domain, application level system model can be constructed and analyzed. UML and OCL can facilitate domain experts' knowledge to be represented and enforced during design process. These concepts are illustrated through a domain-specific modeling environment for electric and hybrid electric vehicles. In particular, this environment is used to evaluate the dynamic performance of a battery electric vehicle design, as shown by some preliminary simulation results
Rotated face detection in color images using radial template (RT)

In this paper, we propose a face detection algorithm to locate faces rotated in any orientation. Detecting rotated faces is important for a face detection system. First, we present a novel model named radial template (RT) to detect rotated faces. This template is designed to find stable features of center-rotated objects in edge maps. Based on skin detection and edge extraction, our method searches for face-like areas and gets their orientations by RT searching. Then the candidates are rotated upright and a frontal face detector is used to determine existence of faces. A system integrating these techniques is presented. Experimental results show that our algorithm is effective to detect human faces rotated in any angle with different sizes, lighting conditions and backgrounds.
Facetracker: a human face tracking and facial organ localizing system

The system consists of two stages (Fig.1). Input with an original video, the system will output an object video with face and facial organ areas' locations. -Face and Facial Organ A r e a Locations I I I Next F r a m e of Video Stream 1 Fig. 1. System structure The first module(Fig.2 and Fig.3) produce initial location information about faces and facial organs such as eyebrows, eyes, nose and mouth. The second module is composed of 4 parts(Fig. 5), which output the final exact location information. The six steps in Fig.4 and 2 steps in Fig.6 illustrate the procedure for faces and their organs' detection.
Macroblock-based progressive fine granularity scalable video coding

The Progressive Fine Granularity Scalable (PFGS) coding is a promising technique for streaming video applications. However, since the original PFGS only chooses its references as framebased, it is very difficult to achieve a good trade-off between high coding efficiency and low drifting errors. In this paper, we present a flexible and effective scheme to control the PFGS coding at the macroblock level. Three INTER modes are first proposed for the enhancement macroblock coding. One of these modes provides a novel method to effectively reduce the drifting errors at low bit rates. Then, a decision-making mechanism based on temporal prediction is developed to choose the optimal coding mode for each enhancement macroblock, which offers a significant coding efficiency improvement for the fine granularity scalable coding scheme. Moreover, the proposed control mechanism can be easily implemented without any additional computation. The experimental results show that the proposed macroblock-based PFGS coding scheme can effectively reduce the drifting errors at low bit rates, while providing further coding efficiency improvement over the FGS and the original PFGS scheme at moderate or high bit rates.
Effective scene matching with local feature representatives

Scene matching measures the similarity of scenes in photos and is of central importance in applications where we have to properly organize large amount of digital photos by scene categories. In this paper, we present a novel scene matching method using local features representatives. For a given image, its scene is compactly represented as a set of cluster centers, called local feature representatives, where the clusters are obtained using the affinity propagation (AP) algorithm to aggregate local features according to their spatial closeness and appearance similarity. The similarity of scenes in two images is then measured by a modified Earth Mover Distance (EMD) between their corresponding sets of local feature representatives. Empirical experiments on real world photos shows that our method is comparable to the state-of-the-arts.
CSPS: An Adaptive Pooling Method for Image Classification

This paper proposes an adaptive approach to learn class-specific pooling shapes (CSPS) for image classification. Prevalent methods for spatial pooling are often conducted on predefined grids of images, which is an ad-hoc method and, thus, lacks generalization power across different categories. In contrast, our CSPS is designed in a data-driven fashion by generating plenty of candidates and selecting the optimal subset for each class. Specifically, we establish an overcomplete spatial shape set that preserves as many geometric patterns as possible. Then, the class-specific subset is selected by training a linear classifier with structured sparsity constraints and color distribution cues. To address the high computational cost and the risk of overfitting due to the overcomplete scheme, the image representations for CSPS are first compressed according to dictionary sensitivity and shape importance. These representations are finally fed to SVMs for the classification task. We demonstrate that CSPS can learn compact yet discriminative geometric information for different classes that carries more semantic meaning than other methods. Experimental results on four datasets demonstrate the benefits of the proposed method compared with other pooling schemes and illustrate its effectiveness on both object and scene images.