nils-herrmann/clean_scirepeval huggingface dataset:
Optimizing the Hierarchical Prediction and Coding in HEVC for Surveillance and Conference Videos With Background Modeling

For the real-time and low-delay video surveillance and teleconferencing applications, the newly video coding standard HEVC can achieve much higher coding efficiency over H.264/AVC. However, we still argue that the hierarchical prediction structure in the HEVC low-delay encoder still does not fully utilize the special characteristics of surveillance and conference videos that are usually captured by stationary cameras. In this case, the background picture (G-picture), which is modeled from the original input frames, can be used to further improve the HEVC low-delay coding efficiency meanwhile reducing the complexity. Therefore, we propose an optimization method for the hierarchical prediction and coding in HEVC for these videos with background modeling. First, several experimental and theoretical analyses are conducted on how to utilize the G-picture to optimize the hierarchical prediction structure and hierarchical quantization. Following these results, we propose to encode the G-picture as the long-term reference frame to improve the background prediction, and then present a G-picture-based bit-allocation algorithm to increase the coding efficiency. Meanwhile, according to the proportions of background and foreground pixels in coding units (CUs), an adaptive speed-up algorithm is developed to classify each CU into different categories and then adopt different speed-up strategies to reduce the encoding complexity. To evaluate the performance, extensive experiments are performed on the HEVC test model. Results show our method can averagely save 39.09% bits and reduce the encoding complexity by 43.63% on surveillance videos, whereas those are 5.27% and 43.68% on conference videos.
Highlight Ranking for Racquet Sports Video in User Attention Subspaces Based on Relevance Feedback

In this paper, we propose a method to rank the highlights of broadcast racquet sports videos. Compared with previous work, we integrate relevance feedback into highlight ranking framework to effectively capture the user's interest in attention subspaces and generate personalized ranking result. First, we establish three user attention subspaces and extract audio, visual, temporal affective features to represent the human perception of highlight in each subspace. Then, the highlight ranking models are constructed using support vector regression (SVR) for the three subspaces respectively. Finally, the three submodels are linearly combined to generate the final ranking model. Relevance feedback technique is employed to adjust the weights of each submodel to obtain the result which is suitable to the user's preference. Experimental results demonstrate our approach is effective.
Seamless switching of scalable video bitstreams for efficient streaming

Efficient adaptation to channel bandwidth is broadly required for effective streaming video over the Internet. To address this requirement, a novel seamless switching scheme among scalable video bitstreams is proposed in this paper. It can significantly improve the performance of video streaming over a broad range of bit rates by fully taking advantage of both the high coding efficiency of nonscalable bitstreams and the flexibility of scalable bitstreams, where small channel bandwidth fluctuations are accommodated by the scalability of a single scalable bitstream, whereas large channel bandwidth fluctuations are tolerated by flexible switching between different scalable bitstreams. Two main techniques for switching between video bitstreams are proposed. Firstly, a novel coding scheme is proposed to enable drift-free switching at any frame from the current scalable bitstream to one operated at lower rates without sending any overhead bits. Secondly, a switching-frame coding scheme is proposed to greatly reduce the number of extra bits needed for switching from the current scalable bitstream to one operated at higher rates. Compared with existing approaches, such as switching between nonscalable bitstreams and streaming with a single scalable bitstream, our experimental results clearly show that the proposed scheme brings higher efficiency and more flexibility in video streaming.
A low-power low-noise 160×120 17µm pixel pitch TEC-less uncooled infrared image sensor

This paper presents a 160×120 size 17µm pixel pitch TEC-less uncooled infrared image sensor with low noise output and low power consumption. Aimed at power-saving TEC-less applications, offset skimming and gain stabilization techniques according to substrate temperature change are proposed and implemented. Each column is integrated with a low-power, low-noise backend readout stage including a CTIA and a 14-bit single-slope ADC. The chip has been realized in 0.35µm process and operates at 100Hz frame rate under 5V/3.3V power supply. Power consumption of the chip is below 50mW. Measurements demonstrate a 2.5LSB RMS temporal output noise, which means 27mK noise equivalent temperature difference (NETD) performance is supposed to be obtained at room temperature.
Neural network based steganalysis in still images

Seganalysis has recently attracted researchers' interests with the development of information hiding techniques. In this paper we propose a new method based neural network to get statistics features of images to identify the underlying hidden data. We first extract features of image embedded information, then input them into neural network to get output. And experiment results indicate this method is valid in steganalysis. This method will be used for Internet/network security, watermarking and so on.
Robust view interpolation with mesh cutting

Existing view interpolation methods like DIBR require accurate disparity map which greatly limits their applications. To this aim, we propose a novel mesh-based view interpolation algorithm capable of synthesizing visually coherent virtual views with rough disparity map estimated by stereo matching algorithms. We adopt an edge-aware mesh cutting method to explicitly handle occlusion and preserve sharp depth discontinuities. Experiments on Middlebury dataset and 3D-HEVC test sequences demonstrate that proposed method outperforms DIBR and state-of-the-art mesh-based view interpolation algorithm in terms of visual quality and PSNR.
Image deblocking using group-based sparse representation and quantization constraint prior

To alleviate the conflict between bit reduction and quality preservation, deblocking as a post-processing strategy is an attractive and promising solution without changing existing codec. In this paper, in order to reduce blocking artifacts and obtain high-quality image, image deblocking is formulated as an optimization problem via maximum a posteriori framework, and a novel algorithm for image deblocking using group-based sparse representation (GSR) and quantization constraint (QC) prior is proposed. GSR prior is utilized to simultaneously enforce the intrinsic local sparsity and the nonlocal self-similarity of natural images, while QC prior is explicitly incorporated to ensure a more reliable and robust estimation. A new split Bregman iteration based method with adaptively adjusted regularization parameter is developed to solve the proposed optimization problem for image deblocking. The parameter-adaptive advantage enables the whole algorithm more attractive and practical. Experiments manifest that the proposed image deblocking algorithm improves current state-of-the-art results by a large margin in both PSNR and visual perception.
A low-noise closed-loop interface for high-G capacitive micro-accelerometer

A switched capacitor interface is presented in this paper for a ±50 g capacitive micro-accelerometer. In order to suppress the flicker noise of the frontend, a switched capacitor proportional-integral controller combined with a charge sensitive amplifier using correlated double sampling is proposed. The controller has a simple structure with minimized number of switches and cancels the offset of both the charge sensitive amplifier and itself. A delay buffer following the controller is used to generate a z-1/2 delay and drive the middle plate of the sensing element. The interface is implemented in a 0.35 μm 3.3 V/15 V CMOS process and the area of the chip is 11.75 mm2. The fabricated prototype circuit operates under 15 V and 3.3 V supply at a sampling clock of 100 kHz. Meanwhile, a sensitivity of 99.7 mV/g is achieved with the bias instability of 200 μg. The dynamic range of the accelerometer is 106.16 dB over a signal bandwidth of 200 Hz.
A Method of Perceptual-Based Shape Decomposition

In this paper, we propose a novel perception-based shape decomposition method which aims to decompose a shape into semantically meaningful parts. In addition to three popular perception rules (the Minima rule, the Short-cut rule and the Convexity rule) in shape decomposition, we propose a new rule named part-similarity rule to encourage consistent partition of similar parts. The problem is formulated as a quadratic ally constrained quadratic program (QCQP) problem and is solved by a trust-region method. Experiment results on MPEG-7 dataset show that we can get a more consistent shape decomposition with human perception compared with other state-of-the-art methods both qualitatively and quantitatively. Finally, we show the advantage of semantic parts over non-meaningful parts in object detection on the ETHZ dataset.
Synchronized Sampling Technology-Based Compensation for Network Effects in WAMS Communication

Wide Area Measurement System (WAMS)-based closed-loop power system control faces lots of challenges such as network effects in WAMS communication which include network-induced delays, data packet dropouts and packet disordering, etc. As of now, design of wide area feedback controller robust against those network effects typically relies on linear matrix inequalities (LMIs) methods. However, because LMIs methods results in conservative controller and sometimes feasible solution cannot be found although they exist, it is still difficult to implement wide area feedback control in real power systems. In order to overcome those problems, a more practical scheme is presented in this paper for WAMS-based closed-loop power system control. In the new scheme, a GPS receiver is configured at the wide area feedback controller/actuator which can calculate feedback signals' delays by comparing the time differences between their own clocks and time stamps of feedback signals. Then, by using synchronized sampling technology again, trajectories of power systems are extrapolated to compensate those network effects in WAMS communication. Therefore, in the new scheme, hardware technologies (e.g., GPS-based synchronized sampling technologies) instead of complicated LMIs methods are used to deal with network effects in WAMS communication. So the new control scheme can overcome the conservative nature from LMIs methods and can be implemented practically in real power systems. Simulation results are given to show the effectiveness of the proposed control scheme.
Local Visual Primitives (LVP) for Face Modelling and Recognition

This paper proposes a novel simple yet effective generative model based on local visual primitives (LVP) for face modeling and classification. The LVPs, as the pattern of local face region, are learnt by clustering a great number of local patches. Visually, these LVPs correspond to intuitive low-level micro visual structures very well, and they are expected to constitute those high-level semantic features, such as eyes, nose and mouth. We show that, though face appearances vary dramatically, these LVPs are very effective for face image reconstruction. For face recognition, block-based histograms of the LVPs indexes are extracted as the face representation to compare for classification. Primary experiments on FERET face database have shown that the LVP method can achieve encouraging recognition rate
Face locating and tracking method based on chroma transform in color images

This paper has investigated the color facts in color images, obtained the relationship between chroma and color coordinates, and established a type of coordinate transform which is able to improve chroma of the skin and lip. With these coordinates, a new method of human face locating and tracking based on skin chroma and lip chroma transform in color images has been presented. It is effective and robust to detect the position of objects, not impacted by the pose of object and complexity of background. It is able to overcome the influence of illumination variance or different camera devices by on-line learning. This method is fast and stable. This algorithm is prerequisite for most human face perception system. By this approach, a feasible, reliable and real-time property algorithm to work out locating and tracking problem in real system is obtained.
Improved entropy of primitive for visual information estimation

Sparse representation has been observed to be highly efficient in dealing with rich, varied and directional information in natural scenes. Based on the statistical analysis of primitives in sparse coding, the entropy of primitive (EoP) was proposed for measuring visual information of images, and its changing tendency has been shown to be highly relevant with the human visual system (HVS). But the sparse coefficient energy was ignored when calculating EoP, which may be critical in accounting for the primitive characteristics. To tackle this, an improved EoP is developed in this work via ℓ2 norm calculation. We further give mathematical derivations for its convergence verification. Experimental evaluations have also demonstrated that the improved EoP can achieve more stable convergence tendencies, which is consistent with the perceptual experiences.
An admission control scheme to provide end-to-end statistical QoS provision in IP networks

This paper presents a distributed and scalable admission control scheme to provide end-to-end statistical QoS guarantees in Differentiated Services (DiffServ) networks. The basic idea of the scheme is that the ingress routers make admission decisions according to the network status information obtained by sending probing packets from the ingress to the egress of the network. Each router passively monitors the arriving traffic and marks the probing packets with its network status. The performance of our scheme is evaluated with a variety of traffic models, QoS metrics and network topologies. The simulation results show that the proposed scheme can accurately control the admissible region and effectively improve the utilization of network resource.
Spectrum Sensing for OFDM Systems Employing Pilot Tones and Application to DVB-T OFDM

Spectrum sensing is one of the core technologies of cognitive radio (CR) systems which provide a viable solution to the problem of sparsity of wireless spectrum. Nowadays, OFDM techniques are adopted by many existing or progressing wireless communication standards. Thus, a robust spectrum sensing algorithm for OFDM modulated signals is highly desired to implement CR when the primary signal uses OFDM modulation. Motivated by this demand, a time-domain symbol cross-correlation based spectrum sensing algorithm (TDSC method) is presented in this paper. The algorithm makes use of the property that the mean of the TDSC of two OFDM symbols is not zero if they have embedded the same frequency-domain pilot tones. The statistical behavior of the proposed spectrum sensor is explicitly analyzed and a theoretical lower bound on the misdetection probability is derived in this paper. An intuitive spectrum sensing method which utilizes the cyclic prefix nature of the OFDM modulated signals (CP method) is also described in this paper as a reference for comparison. Finally, we use the DVB-T Standard  as an application example to illustrate the proposed spectrum sensing algorithm. The simulated channel environments are the Ricean and Rayleigh channels defined in the DVB-T Standard and the additive white Gaussian noise (AWGN) channel. Four CP ratios defined in the DVB-T Standard are simulated for the probability of false alarm equaling 0.01 and the sensing time set equal to 50 ms. Simulation results show that the TDSC method can achieve a misdetection probability of 0.1 when the SNR equals -20.5 dB for all four CP ratios. The TDSC method outperforms the CP method in all cases. Furthermore, the proposed method has approximately the same detection performance for different CP ratios while the detection performance of the CP method degrades dramatically when the CP ratio becomes small. Results also reveal that the simulated performance is very close to the theoretical lower bound indicating that the lower bound can be used as a good prediction of performance.
Application of a Real-Time Data Compression and Adapted Protocol Technique for WAMS

This paper proposes a real-time data compression and adapted protocol technique for wide-area measurement systems (WAMS). The compression algorithm combines exception compression (EC) with swing door trending (SDT) compression. The compression logic is designed to perform this algorithm in real time. Selection of compression parameters and data reconstruction are presented. An adapted protocol is introduced by improving the format of data frames defined by IEEE standard C37.118 for compressed data packets. The proposed compression technique and protocol were applied to the phasor measurement units (PMUs) of a hydropower plant in Guizhou Power Grid in Southwest China. A low-frequency oscillation incident was recorded by this technique. The raw, compressed and reconstructed data were analyzed to verify the compression and determine the accuracy of the proposed technique. Also, the wavelet-based data compression, the standalone EC and SDT are compared with the proposed compression technique. Our results demonstrated that this compression can reach the compression ratios in the range of 6 to 11. Also, this compression and adapted protocol technique can reduce the size of data packets by approximately 75% with high accuracy in both dynamic and steady states.
Accelerating Image-Domain-Warping Virtual View Synthesis on GPGPU

The image-domain-warping (IDW) method can effectively create high-quality virtual views. However, the IDW algorithm is very complex, and the software implementation for this method is far from real-time. In this paper, we propose an IDW-based view synthesis acceleration method on general-purpose computing on a graphics processing unit (GPGPU). Our method makes two main contributions. First, at the algorithm level, we employ FAST for sparse disparity estimation and adopt the successive over-relaxation iterative method to calculate warps. Second, at the platform level, two computation-intensive modules (data extraction and view synthesis) in IDW are offloaded to GPU using efficient data-level parallelism strategies. Experimental results demonstrate that our proposed acceleration method can speed up the original IDW algorithm by more than 110x, and HD stereo three-dimensional video can be converted to 8-view 4 K video (each view has an approximate 720P resolution) in real-time on a hybrid CPU + GPU (NVIDIA GTX980) platform.
H.264 video encryption scheme adaptive to DRM

In the paper, we propose an H.264 video content encryption scheme, which is based on the analysis of H.264 entropy coding, and has the features of irrelative to individual ciphers and adaptive to digital right management. Our scheme applies partial encryption of slice data to preserve the network-friendliness and compression performance of video coding. Compared with previous schemes such as selective encryption of fields, our scheme can efficiently overcome the problem of information leakage resulting from error concealment tools of video coding, greatly reduce the hardware design complexity of deencryption process, and still retain their virtues. Experiments show that, by encrypting only a little of the whole video data, our scheme can provide several different levels of content security involved in degraded picture quality from partial to no information leakage, and is suitable to be applied in the fields of multimedia storage and transmission
A Fast Intra Mode Decision Algorithm for AVS to H.264 Transcoding

H.264/AVC is the latest video coding standard developed by MPEG and ITU. AVS is the latest video coding standard developed by China. The two new video coding standards will co-exist in the market in the future. So it is necessary to transcode the AVS format to the H.264/AVC format or vice versa. Because the AVS video coding standard is based on the 8times8 block and also uses the intra prediction technology, it is possible to use the AVS intra mode information to predict the H.264/AVC intra mode. In this paper, a fast intra mode decision algorithm was proposed to reduce the computation complexity for AVS to H.264/AVC intra transcoding. The evaluation results show that the proposed algorithm can save at least 50% computation with about 0.1 dB loss in PSNR
Modeling background from compressed video

models have been widely used for video surveillance and other applications. Methods for constructing background models and associated application algorithms are mainly studied in the spatial domain (pixel level). Many video sources, however, are in a compressed format before processing. In this paper, we propose an approach to construct background models directly from compressed video. The proposed approach utilizes the information from DCT coefficients at block level to construct accurate background models at pixel level. We implemented three representative algorithms of background models in the compressed domain, and theoretically explored their properties and the relationship with their counterparts in the spatial domain. We also present some general technical improvements to make them more capable for a wide range of applications. The proposed method can achieve the same accuracy as the methods that construct background models from the spatial domain with much lower computational cost (50% on average) and more compact storages.
Influence of sintering temperature on energy conversion efficiency in dye-sensitized solar cell

The dye-sensitized nanocrystalline TiO2 film solar cells were fabricated here. The TiO2 electrodes were prepared by a sol-gel process and were sensitized by the nature dye extracted from a paper mulberry tree. When the sintering temperature of TiO2 films rises from 90 to 500 °C, the energy conversion efficiency of the solar cell improves considerably and up to an optimum value at 470 °C. To investigate this, the nano-TiO2 films were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and UV-Vis spectrum, and the current-voltage (I-V) characteristic of the heteroj unction between the nano-TiO2 and the indium tin oxide (ITO) was measured. The results show the size, crystal phase and band gap of TiO2 nanoparticles undergo changes at the various sintering temperatures. Besides, the resistance of the heteroj unction decreases with increasing the sintering temperature. As the temperature going up and beyond 450 °C, the heterojunction begins to develop into a Schottky junction, and its potential barrier decreases.
A Real-Time Score Detection and Recognition Approach for Broadcast Basketball Video

For broadcast sports video, score information is an effective mid-level representation to facilitate high-level video content analysis. In this paper, we propose a real-time approach to detect score region and recognize scores in broadcast basketball video. First, score region is automatically detected using frame difference and texture information without any prior knowledge. Then, score digit is recognized using a coarse-to-fine scheme based on score spatial structure, temporal correlation and changing rules. Compared with traditional video text recognition method, our approach is computing-insensitive and independent of digit model training. Experimental results show that our approach achieves real-time performance and is robust for the variation of digit font, size, color and noise caused by low frame resolution in broadcast basketball video.