nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive rate control for High Efficiency Video Coding

A frame level adaptive rate control scheme for the emerging High Efficiency Video Coding (HEVC) standard is proposed in this paper, where both rate model and distortion model are provided. For rate modeling, a new rate model is proposed based on the weighted complexity estimation of the previously encoded frames. For distortion modeling, the distortion is modeled as an exponential function of the sum of absolute transformed difference (SATD) and the quantization parameter of the current frame. Moreover, a quality smoothing method based on the distortion model is proposed to reduce the quality fluctuation. The proposed rate control algorithm is implemented into HM5.0. The proposed scheme can control the bitrate accurately with smoothing quality, and the coding gain compared with state-of-the-art technique is up to 0.64dB for LB HE & LB LC, 0.33dB, 0.31dB, 0.42dB, 0.44dB for LP HE, LP LC, RA HE and RA LC respectively.
Classifying traditional Chinese painting images

More and more traditional Chinese painting art images are digitalized and exhibited on the Internet. Effective browsing and retrieving them is an imperative problem need to be addressed. This paper proposes a scheme to classify traditional Chinese paintings. The algorithm uses three low-level features to achieve such a high-level classification: Ohta histogram, color coherence vector and auto-correlation. An accuracy of 97.21% was achieved on the database of 1254 Chinese painting database.
On-line sprite encoding with large global motion estimation

A sprite which is an image composed of pixels belonging to a video object visible throughout a video segment is a very important concept proposed by MPEG4. Because of the search region limitation in the global motion estimation, the performance of traditional sprite coding technology is not satisfactory in the case of fast camera motion. Only enlarging the search region is difficult to ensure the right motion estimation. An improved algorithm is proposed with enlarging the search region, predicting the motion of the current VOP (video object plane) and shortening the iterative time. Three main techniques are adopted in the new algorithm. They are: (1) enlarging search region; (2) weighting sum of absolute difference (SAD); and (3) shortening iteration. Two group experiments present a comparison between the original algorithm and the improved algorithm. The first group experiment shows the improved algorithm gets the same performance as the original algorithm in coding the general motion sequences. In the second group experiment, the Stefan background sequences which frame rates of 15 Hz, 10 Hz, 7.5 Hz, and 6 Hz are encoded in various transformations. The results given in a table show the coding performances of the algorithm are significantly improved.
Review the strength of Gabor features for face recognition from the angle of its robustness to mis-alignment

Gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. In this paper, we review the strength of Gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. Our experiments show that, compared with the gray-level intensity, Gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of Gabor representation.
Study of Power System load shedding scheme based on dynamic simulation

An adaptive load shedding scheme is designed, modeled and simulated in Power System Simulator for Engineers (PSS/E) and compared with conventional under-frequency load shedding scheme (UFLS). In this paper a new distributed load shedding scheme based on real time synchronized frequency measurement is proposed. This scheme improves the load shedding operation and sheds optimal amount of load taking into account simultaneous frequency measurements from various buses along with operating conditions and system topology. Modified New England 39 bus system is used for evaluating the results. The simulation results show that the adaptive scheme has improved the system performance under disturbance. The new distributed load shedding scheme sheds less amount of load as compared to adaptive load shedding.
A low-noise HV interface circuit for MEMS vibratory gyroscope

The paper presents a low-noise high voltage (HV) CMOS Interface ASIC designed for MEMS vibratory gyroscopes. A closed-loop control is realized in the driving mode. An in-chip level shifter is designed in the loop to achieve a high DC voltage level of 5V which can excite the gyroscope. A DC biasing method is adopted in the interface circuit to convert the amplitude-modulated capacitive signal into voltage. The chip occupies 2.5 × 2.0mm2 in a 0.35 µm 2P3M BCD HV process, which offers buried layer and high voltage N-well isolation to block out the potential coupling noise. Simulation results show that the drive axis can accomplish a closed-loop self-oscillation of the MEMS gyroscope.
Matrix-Structural Learning (MSL) of Cascaded Classifier from Enormous Training Set

Aiming at the problem when both positive and negative training set are enormous, this paper proposes a novel matrix-structural learning (MSL) method, as an extension to Viola and Jones' cascade learning method for object detection. Briefly speaking, unlike Viola and Jones' method that learn linearly by bootstrapping only negative samples, the proposed MSL method bootstraps both positive and negative samples in a matrix-like structure. Moreover, an accumulative way is further presented to improve the training efficiency of MSL by inheriting features learned previously during training procedure. The proposed method is evaluated on face detection problem. On a positive set containing 230000 face samples, only 12 hours are needed on a common PC with a 3.20 GHz Pentium IV processor to learn a classifier with false alarm rate less than 1/1000000. What's more, the accuracy of the learned detector exceeds the state-of-the-art results on the CMU+MIT frontal face test set.
An intra prediction pipeline architecture design for AVS encoder

In this paper, an efficient pipelining method to reduce the data dependence for intra prediction in AVS high-definition real-time encoder is proposed. Taking advantage of different data dependences of different locations and prediction modes of sub-blocks within a MB, a new processing order for sub-blocks and their prediction modes is applied in intra prediction pipelining method. The proposed method was implemented in Verilog and synthesized on Xilinx LX330. The simulation result shows that the design is capable of achieving real-time encoding 720p high-definition video sequences at 30 frames per second.
Resource allocation for MIMO orthogonal relay channels with finite-rate feedback

Multiple antennas and relay can improve spectral efficiency of a network. Half duplex relay channels employing decode-and-forward and total power constraint are considered in this work. The resource includes power and time for the receive and transmit stages of the relay. Resource allocation algorithm is designed in order to maximize an achievable rate. The source and the relay nodes are assumed to have partial channel state information (CSI) obtained through finite-rate feedback from relay and/or destination. The full-blown problem is stated and the result of a special case of orthogonal transmission is presented. The transmitters use the simple power on/off precoding with constant number of on-beams while the precoding matrices are chosen according to quantization on Grassmann manifolds. An earlier analytical result on finite rate feedback is employed in the resource allocation algorithm. The complicated expressions of the achievable rates and the non-convexity of the problem make the optimal resource allocations hard to obtain and analyze. Iterative and near optimal resource allocation algorithm is presented. Numerical results are provided to show the benefits of finiterate feedback by comparison of the achievable rates of the relay channel, two-hop transmission and the direct transmission.
Predicting the effectiveness of queries for visual search

Poor retrieval performance significantly degenerates users' experience of visual search, especially in mobile search. Ideally, users would like to be alerted when bad queries are present, which helps eliminate latency as well as waste of bandwidth, especially in 3G wireless environment. In this paper, we propose a visual query performance prediction (v-QPP) approach to predict the retrieval effectiveness. We employ latent dirichlet allocation (LDA)to derive latent topics from image database. From the collection statistics, we model the query's specificity based on topics. High specificity helps a retrieval system to derive user's search intent exactly. Moreover, as low discriminative content is difficult to search in terms of distinguishing relevant images from irrelevant one, we propose a topics based inverse concept frequency (t-ICF) model to deal with specific queries but difficult to discriminate in the reference database. Comparison experiments over MPEG CDVS benchmarking datasets have shown our method significantly outperforms existing approaches in document retrieval.
Color image segmentation using density-based clustering

Color image segmentation is an important but still open problem in image processing. We propose a method for this problem by integrating the spatial connectivity and color features of the pixels. Considering that an image can be regarded as a dataset in which each pixel has a spatial location and a color value, color image segmentation can be obtained by clustering these pixels into different groups of coherent spatial connectivity and color. To discover the spatial connectivity of the pixels, density-based clustering is employed, which is an effective clustering method used in data mining for discovering spatial databases. The color similarity of the pixels is measured in Munsell (HVC) color space whose perceptual uniformity ensures the color change in the segmented regions is smooth in terms of human perception. Experimental results using the proposed method demonstrate encouraging performance.
Statistical Analysis and Modeling of Rate-Distortion Function in SVC Fine-Granular SNR Scalable Videos

In this paper, we propose a simple and effective rate-distortion model of fine-granular SNR scalable (FGS) layer in JVT scalable video coding (SVC). First, we introduce generalized Gaussian distributions (GGD) to model the distributions of the 16 (4*4) integer transform coefficients of the SVC FGS residue picture. Then we analyze the quantization scheme of SVC FGS coding, under which we analyze the distortion-rate function of generalized Gaussian model and conclude that the derivative of the distortion-rate function usually decreases to the traditional number 6.02 dB/bit as the coding rate increases. For some special cases where the shape of GGD is smooth, the derivative of the distortion-rate function increases to 6.02 dB/bit as the rate increases in the actual coding rate range. Guided by the observations, an effective and flexible rate-distortion model is proposed to approximate the actual rate-distortion function of SVC FGS layer. The average estimation error is only 0.07 dB in our extensive experiments. And our analysis and models bring us much insight into the SVC FGS coding and its rate-distortion function.
Human Behavior Analysis for Highlight Ranking in Broadcast Racket Sports Video

The majority of existing work on sports video analysis concentrates on highlight extraction. Little work focuses on the important issue as how the extracted highlights should be organized. In this paper, we present a multimodal approach to organize the highlights extracted from racket sports video grounded on human behavior analysis using a nonlinear affective ranking model. Two research challenges of highlight ranking are addressed, namely affective feature extraction and ranking model construction. The basic principle of affective feature extraction in our work is to extract sensitive features which can stimulate user's emotion. Since the users pay most attention to player behavior and audience response in racket sport highlights, we extract affective features from player behavior including action and trajectory, and game-specific audio keywords. We propose a novel motion analysis method to recognize the player actions. We employ support vector regression to construct the nonlinear highlight ranking model from affective features. A new subjective evaluation criterion is proposed to guide the model construction. To evaluate the performance of the proposed approaches, we have tested them on more than ten-hour broadcast tennis and badminton videos. The experimental results demonstrate that our action recognition approach significantly outperforms the existing appearance-based method. Moreover, our user study shows that the affective highlight ranking approach is effective.
Estimation of end-to-end distortion of virtual view for error-resilient depth map coding

In this paper, we propose to estimate the end-to-end distortion of virtual view for error-resilient depth map coding by analyzing the view-rendering process. The end-to-end distortion is estimated according to the expectation of depth distortion. The expectation of depth distortion is estimated based on the error-propagated distortion from the reference frame, which is calculated recursively for each pixel by considering the source characteristics, channel condition and error concealment method. The error-propagated distortion in terms of each frame is updated after the frame is encoded, which is used for the subsequent frames. Experimental results demonstrate that the proposed algorithm achieves substantial and consistent gains in comparison with the random intra updating method and the conventional rate-distortion method for error-resilient depth map coding.
Hierarchical-and-Adaptive Bit-Allocation with Selective Background Prediction for High Efficiency Video Coding (HEVC)

Recently, a low-delay and high-efficiency hierarchical prediction structure (HPS) has been proposed for the forthcoming HEVC. Actually, frames and coding units (CUs) at different HPS positions have different importance to predict following frames and CUs. This paper firstly analyzes what frames and CUs should be quantified less. Based on the analysis, we propose a Hierarchical-and-Adaptive BIT-allocation method with Selective background prediction (HABITS) to optimize the video performance of HEVC. Extensive experiments on HM8.0 show that, HABITS saves 13.3% and 35.5% of the total bit rate for eight HEVC conference videos and eight common used surveillance videos. Even for the normal videos in HEVC's Class B and C, there is still 2.2% bit-saving.
Fast Identification of Error-Prone Patterns for LDPC Codes under Message Passing Decoding

Although density evolution accurately predicts the asymptotic performance of LDPC codes, it provides limited information for codes of short or moderate block length. For this reason, combinatorial methods have been employed to analyze the performance of finite-length LDPC codes under message passing decoding. The error floor phenomenon is a key issue of finite-length effects, which is caused largely by poorly connected subgraphs residing in the LPDC codes structure. Because of the unequal noise immunity of different bit levels, error floors become more pronounced for LDPC-coded high-order modulation. Nevertheless, the enumeration of error-prone patterns that lead to error floors has been proven to be NP-hard, which motivates us to find substitute metrics with manageable complexity. Considering trapping sets always comprise loops of low extrinsic message degree (EMD) and the connectivity of a loop with the residual graph can be characterized by its approximate EMD (ACE), in this paper we propose a fast method to identify loops with low ACE for a given code. In light of this, the vulnerable variable nodes residing on low-ACE loops are judiciously mapped to bit levels benefitting from stronger error protection. As a result, the error floor can be significantly improved at the cost of a minor increase of hardware complexity.
General blind watermark schemes

Digital watermarking is an active research area that has received a considerable attention in multimedia applications. For most watermark applications, it is often desired to retrieve the embedded information without access to the host data, which is known as blind watermark. Most previous blind watermark schemes either suffer significantly from host data interference or require expense of storage. Therefore, a simple and effective blind watermark scheme is needed urgently. In this paper, we attempt to resolve the problems. Here, we present two general blind watermark schemes, based on the lattice principle and property of periodic function, respectively. By using these schemes, the host data interference is eliminated entirely and only a little storage cost is needed. Thus, they have considerable advantages over previously proposed schemes.
Probability-based method for power capacity specification of wind energy storage systems

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 is proposed in this paper. Through this method the capacities of BESS and super-capacitor are properly designed to bridge the characteristics of high energy density of BESS and the characteristics of high power density of super-capacitor. Comparing to BESS for accommodating wind power fluctuations, HESS is more cost-effective due to the extension of BESS lifetime. 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.
A hybrid pixel-block based view synthesis for multiviewpoint 3D video

View synthesis technique is important for multi-viewpoint 3D video applications. In our technical investigation, we find that some of the current view synthesis methods perform better in boundary areas while some others in non-boundary areas. For example, MPEG VSRS is the most popular view synthesis software, which integrates a series of advanced tools. Recently, Interval-Based Image Synthesis (IBIS) algorithm is proposed, which shows advantage over MPEG VSRS in non-boundary areas but still has some unpleasant artifacts in boundary areas. To take advantage of the merits of the above methods and overcome their shortcomings, we propose a Hybrid Pixel-block Based View Synthesis(HPBVS) method. Experimental results testify that our approach outperforms the state-of-the-art view synthesis methods from both the subjective and objective perspectives.
Discrete wavelet transform-based structural similarity for image quality assessment

Image quality assessment method plays a major role in image processing. Structural similarity (SSIM) is a novel image quality assessment method, and attracts a lot of attentions for its good performance and simple calculation. But it is proposed in pixel domain, a large computation load is introduced when using it to guide image processing algorithm in discrete wavelet transform (DWT) domain. And image processing in DWT domain has been very common. This paper proposes a discrete wavelet transform- based structural similarity (DWT-SSIM) method for image quality assessment. As it highlights human eyes' sensitive frequency bands, the proposed measure has a better correlation with the judgment of human observers than SSIM. Furthermore, the method is easy to implement and embed in DWT domain processing algorithms.
Entropy of Primitive: From Sparse Representation to Visual Information Evaluation

In this paper, we propose a novel concept in evaluating the visual information when perceiving natural images—the entropy of primitive (EoP). Sparse representation has been successfully applied in a wide variety of signal processing and analysis applications due to its high efficiency in dealing with rich varied and directional information contained in natural scenes. Inspired by this observation, in this paper, the visual signal can be decomposed into structural and nonstructural layers according to the visual importance of sparse primitives. Accordingly, the EoP is developed in measuring the visual information. It has been found that the EoP changing tendency in image sparse representation is highly relevant with the hierarchical perceptual cognitive process of human eyes. Extensive mathematical explanations as well as experimental verifications have been presented in order to support the hypothesis. The robustness of the EoP is evaluated in terms of varied block sizes. The dictionary universality is also studied by employing both universal and adaptive dictionaries. With the convergence characteristics of the EoP, a novel top-down just-noticeable difference (JND) profile is proposed. The simulation results have shown that the EoP-based JND outperforms the state-of-the-art JND models according to the subjective evaluation.
Shape-Based Depth Map Coding

Three-dimensional Television (3DTV) system based on Depth-Image-Based Rendering(DIBR) has won popular recognition of researchers. Since the depth information is closely related with the rendering of viewed pixels, the quality of coding has a direct impact on the later effect of the three-dimensional(3D) video, of which the existence of cloud noise and the self-adaption adjustment of depth have become the major problem of the current 3DTV system. By deeply understanding the characteristics of depth information’s object commonality and effective coding of depth information according to multi-objects, this paper proposes a new three-dimensional data representation “Two-dimensional(2D) texture + Depth map+ Shape masks”. The proposed algorithm establishes its model according to the similarity of different objects’ depth information. This algorithm can not only effectively erase the cloud noise caused by the latter synthesizing of the uncertain boundaries, but also conduct the self-adaption adjustment of the depth to the practical needs. It exhibits great advantage in performance compared with such coding depth maps as MPEG-4.