nils-herrmann/clean_scirepeval huggingface dataset:
Algorithmic and architectural co-design for integer motion estimation of AVS

The video part of A VS has been finalized, in order to enhance coding performance. AVS video standard adopts some new features for motion estimation, such as variable block size search, multiple reference frames, and motion vector prediction. However, the better performance comes at the price of high computational complexity, data dependence and memory access requirement. These new features also make the hardware implementation more difficult, especially for real-time applications. In this paper, we firstly propose an integer motion estimation algorithm from hardware-oriented viewpoint. Experimental results show that the proposed algorithm has almost the same performance as the reference software of AVS in SDTV applications. Then the corresponding VLSI architecture is presented. The VLSI architecture has been described in Verilog HDL and synthesized using 0.18 mum Artisan CMOS cells library. The circuit totally costs about 400 K equivalent logic gates. At 108 MHz working frequency, the circuit can meet the real-time requirement for SDTV (720times576, 25 fps) applications, with the search area of 192times192. The co-design obtains better tradeoff between performance and gate-count
Social Image Tagging by Mining Sparse Tag Patterns from Auxiliary Data

User-given tags associated with social images from photosharing websites (e.g., Flickr) are valuable auxiliary resources for the image tagging task. However, social images often suffer from noisy and incomplete tags, heavily degrading the effectiveness of previous image tagging approaches. To alleviate the problem, we introduce a Sparse Tag Patterns (STP) model to discover noiseless and complementary cooccurrence tag patterns from large scale user contributed tags among auxiliary web data. To fulfill the compactness and discriminability, we formulate the STP model as a problem of minimizing quadratic loss function regularized by bi-layer ℓ1 norm. We treat the learned STP as a universal knowledge base and verify its superiority within a data-driven image tagging framework. Experimental results over 1 million auxiliary data demonstrate superior performance of the proposed method compared to the state-of-the-art.
Spectrum Sensing for TV White Space in North America

Spectrum sensing for the use of TV spectrum in North America is addressed in this paper. A unified signature-based spectrum sensing algorithm is designed for the primary licensed signals including NTSC analog TV and ATSC digital TV signals. In addition, an autocorrelation-based spectrum sensing algorithm is proposed for the secondary licensed signals such as wireless microphone signals. To our knowledge, the spectrum sensing algorithms presented in this paper are the first one which can reliably determine the availability of a TV channel for unlicensed radio transmissions in the presence of a strong adjacent channel interferer and the target signal strength is as low as -114 dBm.
A dual round-robin algorithm for combined input-crosspoint-queued switches

Compared with a bufferless crossbar switch, a combined input-crosspoint-queued (CICQ) switch has better scalability owing to its distributed scheduling. Although the previously proposed round-robin algorithms achieve 100% throughput asymptotically under uniform traffic, these algorithms do not provide a satisfactory performance under nonuniform traffic. In this paper, we propose an efficient round-robin algorithm for a CICQ switch with one-cell cross point buffers. With our algorithm, each input arbiter is associated with dual round-robin pointers. Unlike the existing round-robin algorithms, our algorithm has distinctive round-robin pointer updating rules which are powerful to cope with nonuniform traffic patterns. Extensive simulation results show that our algorithm achieves a satisfactory performance under both uniform and a broad class of nonuniform traffic patterns.
Packet Video Error Concealment With Auto Regressive Model

In this paper, auto regressive (AR) model is applied to error concealment for block-based packet video coding. In the proposed error concealment scheme, the motion vector for each corrupted block is first derived by any kind of recovery algorithms. Then each pixel within the corrupted block is replenished as the weighted summation of pixels within a square centered at the pixel indicated by the derived motion vector in a regression manner. Two block-dependent AR coefficient derivation algorithms under spatial and temporal continuity constraints are proposed respectively. The first one derives the AR coefficients via minimizing the summation of the weighted square errors within all the available neighboring blocks under the spatial continuity constraint. The confidence weight of each pixel sample within the available neighboring blocks is inversely proportional to the distance between the sample and the corrupted block. The second one derives the AR coefficients by minimizing the summation of the weighted square errors within an extended block in the previous frame along the motion trajectory under the temporal continuity constraint. The confidence weight of each extended sample is inversely proportional to the distance toward the corresponding motion aligned block whereas the confidence weight of each sample within the motion aligned block is set to be one. The regression results generated by the two algorithms are then merged to form the ultimate restorations. Various experimental results demonstrate that the proposed error concealment strategy is able to improve both the objective and subjective quality of the replenished blocks compared to other methods.
HEVC decoder acceleration on multi-core X86 platform

In this paper, we propose a hybrid parallel decoding strategy for HEVC which combines task-level parallelism and datalevel parallelism based on CTUs. The data-level parallelism makes the execution time distribution of different decoding stages more balanced, and makes the task-level parallelism more efficient. Our approach imposes no constraint on bit streams that they shall be generated by optional parallel coding tools such as tiles or WPP, so it can be applied for all kinds of HEVC bit streams. Furthermore, SSE, a typical SIMD instruction set on X86 platform, is utilized to accelerate time-consuming modules, which shortens the execution time gaps between different stages and make them in favor of parallel processing. We have implemented these acceleration strategies on HM-10.0 decoder, and a great speed-up ratio is achieved.
Head Yaw Estimation From Asymmetry of Facial Appearance

This paper proposes a novel method to estimate the head yaw rotations based on the asymmetry of 2-D facial appearance. In traditional appearance-based pose estimation methods, features are typically extracted holistically by subspace analysis such as principal component analysis, linear discriminant analysis (LDA), etc., which are not designed to directly model the pose variations. In this paper, we argue and reveal that the asymmetry in the intensities of each row of the face image is closely relevant to the yaw rotation of the head and, at the same time, evidently insensitive to the identity of the input face. Specifically, to extract the asymmetry information, 1-D Gabor filters and Fourier transform are exploited. LDA is further applied to the asymmetry features to enhance the discrimination ability. By using the simple nearest centroid classifier, experimental results on two multipose databases show that the proposed features outperform other features. In particular, the generalization of the proposed asymmetry features is verified by the impressive performance when the training and the testing data sets are heterogeneous.
Overvoltages of 40.5 kV vacuum circuit breaker switching off shunt reactors

To investigate the reason of the severe overvoltage failures of 40.5 kV vacuum circuit breakers during switching off shunt reactors, a series of field tests were carried out. The transient process is analyzed, and the magnitude and the characteristics of the transient recovery voltages (TRV) are analyzed. The experiment results indicate that multi-reignition in 35 kV system is much more serious than that in 10 kV system, and the inter-phase overvoltage is especially critical. Additionally, the relationship between the breakdown voltage and the operating moment is exponentially fitted. The result could be used to simulate the overvoltage process when vacuum circuit breakers switch off shunt reactors.
Parzen Discriminant Analysis

In this paper, we propose a non-parametric discriminant analysis method (no assumption on the distributions of classes), called Parzen discriminant analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that minimizing/maximizing the distances between each data sample and its nearby similar/dissimilar samples is equivalent to minimizing an upper bound of the Bayesian error rate. Based on this theoretical analysis, we define our criterion as maximizing the average local dissimilarity scatter with respect to a fixed average local similarity scatter. All local scatters are calculated in fixed size local regions, resembling the idea of Parzen estimation. Experiments in UCI machine learning database show that our method impressively outperforms other related neighbor based non-parametric methods.
Rate Control for Hierarchical B-picture Coding with Scaling-factors

The coding performance can be further improved when the hierarchical B-picture coding is introduced into H.264/AVC. However, the existing rate control schemes can not work efficiently in such new coding framework. This paper proposes a novel rate control algorithm when hierarchical B-picture coding is used in H.264/AVC. Firstly, a set of scaling-factors applied in designing cascaded quantizer for the B frames at different temporal levels is introduced. Based on the designed scaling-factors, an efficient bit-allocation strategy for hierarchical B-picture coding is presented. The experiments show that the proposed rate control algorithm can further improve PSNR up to 0.7dB compared to the existing hierarchical B-picture coding in H.264/AVC, while the mismatch of target bit rate and real bit rate does not exceed 2%.
Statistical model, analysis and approximation of rate-distortion function in MPEG-4 FGS videos

In this paper, the generalized Gaussian distribution is employed first to model the DCT coefficients of image data from MPEG-4 fine-granularity scalability (FGS) frame. Then, according to the quantization theory, the distortion-rate function of the generalized Gaussian model is analyzed and it is concluded that the derivative of the distortion-rate function first decreases, and then increases up to the boundary of 6.02 as the bit rate increases. For actual FGS coding, the derivative of actual distortion-rate function usually decreases as the rate increases, and then begins to increase slowly at a comparatively high bit rate. Finally, based on above observations, a rate-distortion (R-D) model is proposed to approximate the actual distortion-rate function. Experiments show that the proposed R-D model is accurate and flexible.
Channel compensation for robust telephone speech recognition

Channel compensation has been proved to be an effective approach for robust speech recognition. We first use two level cepstral mean substraction (CMS) for channel compensation in telephone speech recognition, and then propose a new channel compensation method, RMFCC. For all experiments, a Korean isolated 84 words database consisting of 80 speakers collected from a local telephone line is adopted. Using the RMFCC, a 39.8% reduction in word error rate is obtained relative to the conventional HMM system. It is shown from the experiments that the RMFCC, compared with RASTA, reduces the computational complexity without losing accuracy, and is also better than the CMS and two level CMS in performance.
AVS2 ? Making Video Coding Smarter [Standards in a Nutshell]

AVS2 is a new generation of video coding standard developed by the IEEE 1857 Working Group under project 1857.4. AVS2 is also the second-generation video coding standard established by the Audio and Video Coding Standard (AVS) Working Group of China; the first-generation AVS1 was developed by the AVS Working Group and issued as Chinese national standard GB/T 20090.2-2006 in 2006. The AVS Working Group was founded in 2002 and is dedicated to providing the digital audio-video industry with highly efficient and economical coding/decoding technologies. So far, the AVS1 video coding standard is widely implemented in regional broadcasting, communication, and digital video entertainment systems. As the successor of AVS1, AVS2 is designed to achieve significant coding efficiency improvements relative to the preceding H.264/MPEG4-AVC and AVS1 standards. The basic coding framework of AVS2 is similar to the conterminous HEVC/H.265, but AVS2 can provide more efficient compression for certain video applications such as surveillance as well as low-delay communication such as videoconferencing. AVS2 is making video coding smarter by adopting intelligent coding tools that not only improve coding efficiency but also help with computer vision tasks such as object detection and tracking.
Semantic object segmentation by a spatio-temporal MRF model

In this paper, a region-based spatio-temporal Markov random field (STMRF) model is proposed to segment moving objects semantically. The STMRF model combines segmentation results of four successive frames and integrates the temporal continuity in the uniform energy function. The segmentation procedure is composed of two stages: one is the short-term's classification and the other is temporal integration. At the first stage, moving objects are extracted by a region-based MRF model between two frames in a frame group of four successive frames. At the second stage, the ultimate semantic object is labeled by minimization the energy function of the STMRF model. Such phased segmentation process is corresponding to a multi-level simulated anneal strategy. Experimental results show that the proposed algorithm can efficiently capture the motion semantic meaning of objects and accurately extract moving objects.
Coarse-to-fine video text detection

In this paper, we propose an effective coarse-to-fine algorithm to detect text in video. Firstly, in coarse-detection section, stroke filter is employed to detect all candidate stroke pixels, and then a fast region growing method is developed to connect these pixels into regions which are further separated into candidate text lines by projection operation. Secondly, in fine-detection section, correct text regions are selected from candidate ones by support vector machine (SVM) model and stroke features, and text regions in multi-resolution are integrated. Finally, the result is optimized significantly according to temporal correlation information. Experimental results show that our algorithm achieves real-time performance and is robust for the variation of language, font, size, color and noise of text caused by low frame resolution in video.
On the interoperability of local descriptors compression

There are a number of component technologies that are useful for visual search, including format of visual descriptors, descriptor extraction process, as well as indexing, and matching algorithms. As a minimum, the format of descriptors as well as parts of their extraction process should be defined to ensure interoperability. In this paper, we study the problem of interoperability among compressed local descriptors at different bit-rates; that is, allowing effective and efficient comparison of compact descriptors, which is fundamentally important to mobile visual search applications. We propose to combine feature transform and multi-stage vector quantization to implement the interoperability of compact local descriptors. First, an orthogonal transform (e.g. Principle component analysis, PCA) is employed to eliminate the correlation between local feature dimensions, which improves the performance of compressed domain descriptor matching with the well-aligned distance computing of sorted important features in transform space. Second, a multi-stage vector quantization (MSVQ) is applied to generate compact codes for local descriptors. At light quantization tables, MSVQ takes advantage of the transform domain features to properly allocate different budgets to each group of transformed feature dimensions, respectively. The interoperability between compressed descriptors at different bit rates can be achieved by the descriptors' fast matching in the orthogonal feature space. In other words, descriptor decoding into the original feature space (SIFT space) is unnecessary, as the distance can be calculated by pre-computed lookup tables. In particular, such efficient matching in transform domain is significant for large-scale visual search. Over a set of benchmark datasets, we have reported superior performance over state-of-the-arts.
A High-Voltage Closed-Loop SC Interface for a ± 50 g Capacitive Micro-Accelerometer With 112.4 dB Dynamic Range

A low-noise high-linearity switched capacitor interface implemented in a 0.35<inline-formula> <tex-math notation="LaTeX">$\mu \text {m}$ </tex-math></inline-formula> 3.3 V/15 V CMOS process is presented in this paper for a ±50g capacitive micro-accelerometer. The sensing element is enclosed in an optimized closed loop with proportional-integral compensation, improving the linearity significantly. In order to suppress the flicker noise of the front-end at the lowest cost, a novel switched capacitor PI controller combined with a charge sensitive amplifier using correlated double sampling is proposed. Furthermore, a detailed noise analysis of the proposed CSA-PIC combination with a final goal of achieving 120 dB dynamic range is also presented. The theoretical analysis is verified by the consistency between calculated results and PNoise simulation results. The sensor output can be read in analogue domain from a sample-and-hold buffer or in digital domain from a back-end 2–1 MASH <inline-formula> <tex-math notation="LaTeX">$\Sigma$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\Delta$ </tex-math></inline-formula> ADC. The fabricated prototype circuit measures 11.75 mm<sup>2</sup> and operates under 15-V and 3.3-V supplies at a sampling clock of 111 KHz. Meanwhile, a sensitivity of 99.7 mV/g is achieved with a dynamic range of 112.4 dB over a 200-Hz bandwidth. The accelerometer has a DC nonlinearity of 0.623 in the range of −18 g to +14 g measured in self-test mode and the bias instability is 200 <inline-formula> <tex-math notation="LaTeX">$\mu \text {g}$ </tex-math></inline-formula>.
Measuring visual saliency by Site Entropy Rate

In this paper, we propose a new computational model for visual saliency derived from the information maximization principle. The model is inspired by a few well acknowledged biological facts. To compute the saliency spots of an image, the model first extracts a number of sub-band feature maps using learned sparse codes. It adopts a fully-connected graph representation for each feature map, and runs random walks on the graphs to simulate the signal/information transmission among the interconnected neurons. We propose a new visual saliency measure called Site Entropy Rate (SER) to compute the average information transmitted from a node (neuron) to all the others during the random walk on the graphs/network. This saliency definition also explains the center-surround mechanism from computation aspect. We further extend our model to spatial-temporal domain so as to detect salient spots in videos. To evaluate the proposed model, we do extensive experiments on psychological stimuli, two well known image data sets, as well as a public video dataset. The experiments demonstrate encouraging results that the proposed model achieves the state-of-the-art performance of saliency detection in both still images and videos.
Adaptive frame level rate control in 3D-HEVC

In this paper, we propose a new frame level rate control algorithm for the high efficiency video coding (HEVC) based 3D video (3DV) compression standard. In the proposed scheme, a new initial quantization parameter (QP) decision scheme is provided, and the bit allocation for each view is investigated to smooth the bitrate fluctuation and reach accurate rate control. Meanwhile, a simplified complexity estimation method for the extended view is introduced to reduce the computational complexity while improves the coding performance. The experimental results on 3DV test sequences demonstrate that the proposed algorithm can achieve better R-D performance and more accurate rate control compared to the benchmark algorithms in HTM10.0. The maximum performance improvement can be up to 12.4% and the average BD-rate gain for each view is 5.2%, 6.5% and 6.6% respectively.
Face detection under variable lighting based on resample by face relighting

Different environment illuminations have a great impact on face detection. We present a solution based on face relighting technology. The basic idea is that there exists nine harmonic images that can be derived from a 3D model of a face, and by which we can estimate the illumination coefficient of any face sample. Using an illumination radio image, we can produce images under new lighting conditions. To detect the faces under certain lighting condition, we relight the original face samples to get more new faces under different kinds of possible lighting condition, and add them to the training set. Our experimental results on support vector machine (SVM) turns out that the relighting subspace is effective in face detection under various lighting conditions. Moreover, if we relight original face samples to the new samples under different illuminations, the collected example sets are multiplied. We use the expanded database to train an AdaBoost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be efficiently speeded up by the proposed methods. The later experiment also verifies the generalization capability of the proposed method.
Minimizing Reconstruction Bias Hashing via Joint Projection Learning and Quantization

Hashing, a widely studied solution to the approximate nearest neighbor search, aims to map data points in the high-dimensional Euclidean space to the low-dimensional Hamming space while preserving the similarity between original points. As directly learning binary codes can be NP-hard due to discrete constraints, a two-stage scheme, namely, “projection and quantization”, has already become a standard paradigm for learning similarity-preserving hash codes. However, most existing hashing methods typically separate these two stages and thus fail to investigate complementary effects of both stages. In this paper, we systematically study the relationship between “projection and quantization”, and propose a novel minimal reconstruction bias hashing (MRH) method to learn compact binary codes, in which the projection learning and quantization optimizing are jointly performed. By introducing a lower bound analysis, we design an effective ternary search algorithm to solve the corresponding optimization problem. Furthermore, we conduct some insightful discussions on the proposed MRH approach, including the theoretical proof, and computational complexity. Distinct from previous works, the MRH can adaptively adjust the projection dimensionality to balance the information loss between the projection and quantization. The proposed framework not only provides a unique perspective to view traditional hashing methods, but also evokes some other researches, e.g., guiding the design of the loss functions in deep networks. Extensive experiment results have shown that the proposed MRH significantly outperforms a variety of state-of-the-art methods over eight widely used benchmarks.