nils-herrmann/clean_scirepeval huggingface dataset:
Reduced-Reference Quality Assessment of Screen Content Images

The screen content images (SCIs) quality influences the user experience and the interactive performance of remote computing systems. With numerous approaches proposed to evaluate the quality of natural images, much less work has been dedicated to reduced-reference image quality assessment (RR-IQA) of SCIs. Here, we propose an RR-IQA method from the perspective of SCI visual perception. In particular, the quality of the distorted SCI is evaluated by comparing a set of extracted statistical features that consider both primary visual information and unpredictable uncertainty. A unique property that differentiates the proposed method from previous RR-IQA methods for natural images is the consideration of behaviors when human subjects view the screen content, which motivates us to establish the perceptual model according to the distinct properties of SCIs. Validations based on the screen content IQA database show that the proposed algorithm provides accurate predictions across a wide range of SCI distortions with negligible transmission overhead.
Fast weighted algorithms for bitstream extraction of SVC Medium-Grain scalable video coding

The Medium-Granular scalable (MGS) technologies in H.264/AVC-based scalable video coding (SVC) provide a flexible foundation to accommodate different network capacities. In order to make use of MGS in multiple environments conveniently, we need to obtain the Rate Distortion (R-D) function of SVC and design efficient bitstream extractions. In this paper, we proposed a simple and effective distortion model to estimate the reconstruction distortion with drift error. Based on the model, a simple and fast weight-based priority setting algorithm is designed to achieve optimal R-D performance in MGS bitstream extractions. A smooth extraction algorithm is also provided for smooth quality substream extraction. Extensive experiments show the accuracy of the new R-D models and the effectiveness of the proposed extraction algorithms.
Design and process development of a stacked SRAM memory chip module with TSV interconnection

In this paper, a stacked SRAM chip module is presented and simulation results are demonstrated. A novel 3D integration process is presented and challenging issues are addressed. With this novel process, there's no need to do grinding/polishing of copper overburden after filling of TSV by copper electroplating. Copper microbumps will be formed directly on the active side in the filling of TSV by copper electroplating while the ones on the backside will be formed with backside releasing process. A test run is carried out with this novel process and a 4-layer stacked chip module is successfully fabricated.
Morphological representation of DCT coefficients for image compression

Recent success in discrete cosine transform (DCT) image coding is mainly attributed to recognition of the importance of data organization and representation. Currently, there are several competitive DCT-based coders such as DCT-based embedded image coding (EZDCT) (see Xiong et al., Z., 1996) and significance tree quantization (STQ) (see Davis, G. and Chawla, S., 1997). In the wavelet context, morphological representation of wavelet data has achieved the best compression performance. The representatives are morphological representation of wavelet data (MRWD) (see Servetto, S. et al., 1999) and significance-linked connected component analysis (see Chai, B.-B. et al., 1999). We show that, by proper reorganization of its coefficients, the block-based DCT can have similar characteristics, such as energy compaction, cross-subband similarity, decay of magnitude across subband, etc., to the wavelet transform. These characteristics can widen DCT applications relevant to image compression, image retrieval, pattern recognition, etc. We then present an image coder utilizing these characteristics by morphological representation of DCT coefficients (MRDCT). The experiments show that MRDCT is among the state-of-the-art DCT-based image coders reported in the literature. For example, for the Lena image at 0.25 bpp, MRDCT outperforms JPEG, STQ and EZDCT in peak signal-to-noise ratio by 1.0, 1.0, and 0.3 dB, respectively.
A new texture-insensitive edge detection method

In this paper, a novel edge detection method is proposed which can be used to find main edges of objects meanwhile filter edges within texture regions. We first introduce three kinds of edge models and three kinds of nonedge models to represent all the possible condition in the edge detection process. Then, pixel's window intensity regularity (WIR) is defined which denotes the texture similarity degree around the pixel. And then based on pixel's WIR a new computation method for image gradient is brought forward, according to which, gradient value within texture regions will be almost eliminated while the reasonable gradient will not be weakened obviously. Finally, Canny-like operator is employed to detect and locate edges. Experimental results demonstrate encouraging performance.
Sequence Multi-Labeling: A Unified Video Annotation Scheme With Spatial and Temporal Context

Automatic video annotation is a challenging yet important problem for content-based video indexing and retrieval. In most existing works, annotation is formulated as a multi-labeling problem over individual shots. However, video is by nature informative in spatial and temporal context of semantic concepts. In this paper, we formulate video annotation as a sequence multi-labeling (SML) problem over a shot sequence. Different from many video annotation paradigms working on individual shots, SML aims to predict a multi-label sequence for consecutive shots in a global optimization manner by incorporating spatial and temporal context into a unified learning framework. A novel discriminative method, called sequence multi-label support vector machine (SVMSML), is accordingly proposed to infer the multi-label sequence for a given shot sequence. In SVMSML, a joint kernel is employed to model the feature-level and concept-level context relationships (i.e., the dependencies of concepts on the low-level features, spatial and temporal correlations of concepts). A multiple-kernel learning (MKL) algorithm is developed to optimize the kernel weights of the joint kernel as well as the SML score function. To efficiently search the desirable multi-label sequence over the large output space in both training and test phases, we adopt an approximate method to maximize the energy of a binary Markov random field (BMRF). Extensive experiments on TRECVID'05 and TRECVID'07 datasets have shown that our proposed SVMSML gains superior performance over the state-of-the-art.
Individual 3D face synthesis based on orthogonal photos and speech-driven facial animation

In the paper, a methodology for individual face synthesis using given orthogonal photos is proposed. An integrated speech-driven facial animation system is presented. Firstly, in order to capture a given subject's personal facial configuration, a novel coarse-to-fine strategy based on facial texture and deformable template is proposed to localize some facial feature points in the image of frontal view. The corresponding feature points in the profile are extracted by using the polygonal approximation. Secondly, all these feature points are aligned to fit the generic 3D face model to a specialized one to reflect the given person's facial configuration. Then a multi-direction texture-mapping technique is presented to synthesize a lifelike personal face. Finally, muscle-based expression and lip-motion models are built up. All the above technologies are integrated into a speech-driven face animation system. We are aiming at a MPEG-4 compatible video-driven face animation system.
Image Super-Resolution via Hierarchical and Collaborative Sparse Representation

In this paper, we propose an efficient image super-resolution algorithm based on hierarchical and collaborative sparse representation (HCSR). Motivated by the observation that natural images typically exhibit multi-modal statistics, we propose a hierarchical sparse coding model which includes two layers: the first layer encodes individual patches, and the second layer jointly encodes the set of patches that belong to the same homogeneous subset of image space. We further present a simple alternative to achieve such target by identifying optimal sparse representation that is adaptive to specific statistics of images. Specially, we cluster images from the offline training set into regions of similar geometric structure, and model each region (cluster) by learning adaptive bases describing the patches within that cluster using principal component analysis (PCA). This cluster-specific dictionary is then exploited to optimally estimate the underlying HR pixel values using the idea of collaborative sparse coding, in which the similarity between patches in the same cluster is further considered. It conceptually and computationally remedies the limitation of many existing algorithms based on standard sparse coding, in which patches are independently encoded. Experimental results demonstrate the proposed method appears to be competitive with state-of-the-art algorithms.
New distortion model for depth coding in 3DVC

Video plus the corresponding depth maps is now the most popular data format for 3D video coding, as it is convenient to synthesize an arbitrary intermediate virtual view without the need to display the depth map for end users. How to compress the depth map efficiently is one of the key problems in 3DVC. In this paper we propose a new distortion model for depth map coding by considering the view synthesis distortion caused by depth distortion and the disparity rounding problem. Simulation results show that the proposed scheme can achieve 34% bit-rate saving for depth map coding, or 1.28 dB gain in terms of PSNR on average for synthesized virtual views.
Spectrum Sensing for DMB-T Systems Using PN Frame Headers

Spectrum sensing for the China digital television terrestrial broadcasting system is considered in this paper. The spectrum sensing algorithms utilize PN sequences embedded in the frame headers to perform spectrum sensing. A theoretical lower bound on the misdetection probability for each spectrum sensor presented in this paper is derived. The performances of the proposed spectrum sensing algorithms are demonstrated by computer simulation for the multipath Rayleigh fading and steady state multipath Rayleigh fading channels. Simulation results show that the misdetection probability evaluated by computer simulations is close to the lower bound on the misdetection probability for a steady state multipath Rayleigh channel. When the probability of false alarm is 0.01 and a 50 ms of sensing time is used, a misdetection probability equal to 0.1 is achieved when the signal to noise power ratio is -16 dB, -18.8 dB, and -18 dB for modes 1, 2, and 3 in the multipath Rayleigh fading channel, respectively.
Discriminative learning of additive noise and channel distortions for robust speech recognition

Learning the influence of additive noise and channel distortions from training data is an effective approach for robust speech recognition. Most of the previous methods are based on maximum likelihood estimation criterion. We propose a new method of discriminative learning environmental parameters, which is based on the minimum classification error (MCE) criterion. By using a simple classifier defined by ourselves and the generalized probabilistic descent (GPD) algorithm, we iteratively learn environmental parameters. After getting the parameters, we estimate the clean speech features from the observed speech features and then use the estimation of the clean speech features to train or test the back-end HMM classifier. The best error rate reduction of 32.1% is obtained, tested on a Korean 18 isolated confusion words task, relative to the conventional HMM system.
Rateless-coded hybrid ARQ

Recently, hybrid-ARQ attracts significant research efforts due to the emergence of high speed data packet access (HSDPA) standard. Turbo-coded hybrid-ARQ was reported to have very good performance under block fading channels. In this paper, we investigate new hybrid ARQ schemes using rateless codes. The simulation results show that at low SNR the rateless-coded hybrid-ARQ outperforms Turbo-coded hybrid-ARQ in terms of bit error rate in block fading channel under QPSK and 16-QAM modulation. We also propose schemes applying length estimation on rateless-coded hybrid-ARQ to improve the data transmission rate.
Group-Sensitive Multiple Kernel Learning for Object Recognition

In this paper, a group-sensitive multiple kernel learning (GS-MKL) method is proposed for object recognition to accommodate the intraclass diversity and the interclass correlation. By introducing the “group” between the object category and individual images as an intermediate representation, GS-MKL attempts to learn group-sensitive multikernel combinations together with the associated classifier. For each object category, the image corpus from the same category is partitioned into groups. Images with similar appearance are partitioned into the same group, which corresponds to the subcategory of the object category. Accordingly, intraclass diversity can be represented by the set of groups from the same category but with diverse appearances; interclass correlation can be represented by the correlation between groups from different categories. GS-MKL provides a tractable solution to adapt multikernel combination to local data distribution and to seek a tradeoff between capturing the diversity and keeping the invariance for each object category. Different from the simple hybrid grouping strategy that solves sample grouping and GS-MKL training independently, two sample grouping strategies are proposed to integrate sample grouping and GS-MKL training. The first one is a looping hybrid grouping method, where a global kernel clustering method and GS-MKL interact with each other by sharing group-sensitive multikernel combination. The second one is a dynamic divisive grouping method, where a hierarchical kernel-based grouping process interacts with GS-MKL. Experimental results show that performance of GS-MKL does not significantly vary with different grouping strategies, but the looping hybrid grouping method produces slightly better results. On four challenging data sets, our proposed method has achieved encouraging performance comparable to the state-of-the-art and outperformed several existing MKL methods.
Online selecting discriminative tracking features using particle filter

The paper proposes a method to keep the tracker robust to background clutters by online selecting discriminative features from a large feature space. Furthermore, the feature selection procedure is embedded into the particle filtering process with the aid of existed "background" particles. Feature values from background patches and object observations are sampled during tracking and Fisher discriminant is employed to rank the classification capacity of each feature based on sampled values. Top-ranked discriminative features are selected into the appearance model and simultaneously invalid features are removed out to adjust the object representation adaptively. The implemented tracker with online discriminative feature selection module embedded shows promising results on experimental video sequences.
Adaptive Spatial and Transform Domain FGS Coding

In inter-picture coding, block-based frequency transform is usually carried out on the predicted errors for each interblock to remove the spatial correlation among them. However, it can not always do well since the predicted errors in some inter-blocks have marginal or diagonal correlation. A good solution is to omit transform operations for the predicted errors of those inter-blocks with low correlation before quantization operation. The same phenomenon also can be observed in fine grain scalability (FGS) layer coding. In this paper, an adaptive prediction error coding method in spatial and frequency domain with lower complexity is considered for FGS layer coding. Transform operation is only needed when there are non-zero reconstructed coefficients in spatially co-located block in base layer. The experimental results show that compared with FGS coding in JSVM, higher coding efficiency can be achieved with lower computational complexity at decoder since inverse transform is no longer needed for those predicted errors coded in spatial domain at encoder.
Mitigation of disturbances in DFIG-based wind farm connected to weak distribution system using STATCOM

DFIG (Doubly Fed Induction Generator) based wind farm is gaining popularity these days because of its inherent advantages like variable speed operation and independent controllability of active and reactive power over conventional Induction Generators. When interconnected into power grid, it brings voltage stability problems during grid-side disturbances. So integration of DFIG-based wind farm to power grid, especially to weak distribution system is major concern for power system engineers today with the recent grid code requirements. This paper examines the use of Static Synchronous Compensator (STATCOM) as a dynamic reactive power compensator at the point of common coupling to maintain stable voltage by protecting DFIG-based wind farm interconnected to weak distribution system from going offline during and after the disturbances. The developed system is simulated in MATLAB/Simulink and the results show that STATCOM mitigates the effects of transient disturbances viz., three phase fault, step load change, voltage swelling and sagging in the system and hence improves the stability and performance of wind farm.
An offset-compensated switched-capacitor interface circuit for closed-loop MEMS capacitive accelerometer

This paper presents a switched-capacitor readout circuit designed for the closed-loop MEMS capacitive accelerometer with a full scale of ±2g. The offset voltage of amplifiers is compensated and the low-frequency noise is reduced by using CDS technique, thus the precision of the whole system is significantly improved. The chip has been fabricated in a 0.35μm 15V CMOS technology and the equivalent noise acceleration (ENA) of the whole accelerometer system is tested to be 950ng/VHz at 120Hz in atmosphere.
A Frame Level Rate Control Algorithm For Screen Content Coding

In this paper, we propose a frame level rate control scheme for screen content video (SCV) coding. Firstly, inter frame correlation (IFC) is computed to describe the similarity of successive frames for SCV. Based on the IFC, the frames in SCV are further classified into key frames (KFs) and non-key frames (NKFs). Secondly, an efficient bit rate allocation scheme is proposed for KFs and NKFs based on the IFC and Hypothetical Reference Decoder (HRD), which can well ensure the R-D performance. Finally, rate quantization (R-Q) models for KFs and NKFs are established by considering the characteristics of the two types of frames, respectively. As such, accurate coding bits allocation at frame level can be achieved by the proposed rate control algorithm. The proposed rate control scheme introduces low coding complexity burden as well as low coding delay, enabling its application in real screen sharing scenarios. Compared with the recommended rate control scheme in HEVC, experimental results demonstrate that the proposed rate control scheme can achieve better performance in terms of both rate control accuracy and R-D performance. In particular, the average bit rate mismatch is within 1.4%, and the average R-D performance improvements are over 19%.
Robust fisher codes for large scale image retrieval

Fisher vectors (FV) have shown great advantages in large scale visual search. However, traditional FV suffers from noisy local descriptors, which may deteriorate the FV discriminative power. In this paper, we propose a robust Fisher vectors (RFV). To fulfill fast search and light storage over a large scale image dataset, we employ a simple binarization method to compress RFV to generate compact robust Fisher codes (RFC). Extensive comparison experiments on benchmark datasets have shown that both RFV and RFC outperforms the state-of-the-art performance. The scalability of RFC has been validated on a dataset of over 1 million images as well.
Structural Group Sparse Representation for Image Compressive Sensing Recovery

Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some domain. Most of conventional CS recovery approaches, however, exploited a set of fixed bases (e.g. DCT, wavelet, contour let and gradient domain) for the entirety of a signal, which are irrespective of the nonstationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor rate-distortion performance. In this paper, we propose a new framework for image compressive sensing recovery via structural group sparse representation (SGSR) modeling, which enforces image sparsity and self-similarity simultaneously under a unified framework in an adaptive group domain, thus greatly confining the CS solution space. In addition, an efficient iterative shrinkage/thresholding algorithm based technique is developed to solve the above optimization problem. Experimental results demonstrate that the novel CS recovery strategy achieves significant performance improvements over the current state-of-the-art schemes and exhibits nice convergence.
Generating vocabulary for global feature representation towards commerce image retrieval

This paper studies the problem of retrieving images by color, texture and shape in the context of visual assisted product recommendation in E-commerce sites. Different from general CBIR applications, commerce image retrieval puts more emphasis on outlier-free ranking (top N) to gain perfect user experience. We suggest to extend the bag-of-words (BoW) model to global feature characterization rather than commonly used histogram based low-level feature representation. Although BoW is a common practice in object recognition, we argue generating feature vocabulary is useful to address the global feature characterization that could be elegantly adapted to domain specific commerce image search. The representation is compact and discriminative, which may adapt with individual websites. Quantitative as well as subjective evaluation demonstrates the functionality of the proposed method. In practice, the vocabulary based global features greatly reduce outliers in top rank images, so that desirable user experience can be obtained in E-Commerce applications.
An edge-based admission control for differentiated services network

This paper presents an edge-based admission control scheme for Differentiated Services (DiffServ) networks. In our scheme, admission control decisions are performed at the edge routers according to the core router's available resource information obtained by sending probing packets along the selected routing path. The scheme consists of four elements: (1) local measurement, (2) available bandwidth estimation based on the second moment property of the aggregate traffic and QoS constraints, (3) admission control decisions, and (4) a stateless resource probing protocol to collect the network status. The experimental results show that the proposed scheme can effectively improve the utilization of network resources and provide QoS guarantees for an aggregate.