nils-herrmann/clean_scirepeval huggingface dataset:
On Rate-Distortion Modeling and Extraction of H.264/SVC Fine-Granular Scalable Video

Fine-granular scalable (FGS) technologies in H.264/AVC-based scalable video coding (SVC) provide a flexible foundation to accommodate different network capacities. To support efficient quality extraction, it is important to obtain the rate-distortion (R-D) or Distortion-Rate (D-R) function of each individual picture or a group of pictures (GOP). In this paper, firstly, the R-D function of SVC FGS pictures is analyzed with generalized Gaussian model and the D-R curve is proved to be a concave function overall. Considering the current sub-bitplane technology, the D-R function is revisited and inferred to be linear under MSE criterion within an FGS level, which also explains why the observed D-R curve with PSNR criterion is a piece-wise convex function. Secondly, the drift issue of SVC is analyzed, and a simple and effective distortion model is proposed to estimate the reconstruction distortion with drift error. Thirdly, with the above analysis and models, a virtual GOP concept is introduced, and a new priority setting algorithm is designed to achieve the optimal R-D performance in a virtual GOP. The D-R slope of each FGS packet and the D-R function of each virtual GOP are also obtained during the process. Finally, the D-R slopes of FGS levels are used in quality layer assignment to achieve equivalent coding efficiency to the SVC test model but with significantly reduced complexity. The D-R functions of virtual GOPs are utilized to design a practical method for smooth quality reconstruction. Compared to the prior methods, the smoothed video quality is improved not only objectively but also subjectively.
An Enhanced Robust Entropy Coder for Video Codecs Based on Context-Adaptive Reversible VLC

This paper proposes an enhanced RVLC coder, context-adaptive reversible variable length coder (CRVLC), for DCT coefficients by using the techniques of data sub-partitioning and context modeling. The data sub-partitioning means that the data part of DCT coefficients is split into several small sub-partitions. As each sub-partition can be reversibly decoded by RVLC, more data as well as higher error resilience can be obtained. The context modeling exploits the correlation of DCT coefficients for further compression. This modeling defines the contexts by hierarchical-dependent information. The information is also available in the backward decoding, so that it supports the reversible decoding. And with it the data outputted by CRVLC can be naturally placed into multiple sub-partitions.
Illumination normalization for robust face recognition against varying lighting conditions

Evaluations of the state-of-the-art of both academic face recognition algorithms and commercial systems have shown that recognition performance of most current technologies degrades due to the variations of illumination. We investigate several illumination normalization methods and propose some novel solutions. The main contribution includes: (1) A gamma intensity correction (GIC) method is proposed to normalize the overall image intensity at the given illumination level; (2) A region-based strategy combining GIC and the histogram equalization (HE) is proposed to further eliminate the side-lighting effect; (3) A quotient illumination relighting (QIR) method is presented to synthesize images under a predefined normal lighting condition from the provided face images captured under nonnormal lighting condition. These methods are evaluated and compared on the Yale illumination face database B and Harvard illumination face database. Considerable improvements are observed. Some conclusions are given at last.
A Model for Assessing the Power Variation of a Wind Farm Considering the Outages of Wind Turbines

Most of the published models cannot properly incorporate the outages of wind turbines in estimating the stochastic variation of the wind power output. In reality, wind turbines are exposed to an open and uncontrolled environment, sometimes harsh, to harvest energy from wind. The environmental condition of wind turbines is thus worse than that of the conventional generators. This results in a relatively higher outage probability of the wind turbines, which significantly affects the power output from a wind farm. This paper proposes a model for including the outage probability of wind turbines in simulating the power output of wind farms. The contribution of this paper is in introducing a model to represent the relationship between the outage probabilities of wind turbines and wind speed and then integrating this model with a frequency-domain wind power output model. A numerical simulation approach based on the Monte Carlo method is then proposed to simulate the wind power variation including the outage probability of wind turbines. A validation study based on the field-measured data from a real wind farm shows that the simulated wind power variation matches well with the actual field measurements when the probability-based outage model is included in the calculations.
Rate-distortion analysis for H.264/AVC video coding and its application to rate control

In this paper, an efficient rate-control scheme for H.264/AVC video encoding is proposed. The redesign of the quantization scheme in H.264/AVC results in that the relationship between the quantization parameter and the true quantization stepsize is no longer linear. Based on this observation, we propose a new rate-distortion (R-D) model by utilizing the true quantization stepsize and then develop an improved rate-control scheme for the H.264/AVC encoder based on this new R-D model. In general, the current R-D optimization (RDO) mode-selection scheme in H.264/AVC test model is difficult for rate control, because rate control usually requires a predetermined set of motion vectors and coding modes to select the quantization parameter, whereas the RDO does in the different order and requires a predetermined quantization parameter to select motion vectors and coding modes. To tackle this problem, we develop a complexity-adjustable rate-control scheme based on the proposed R-D model. Briefly, the proposed scheme is a one-pass process at frame level and a partial two-pass process at macroblock level. Since the number of macroblocks with the two-pass processing can be controlled by an encoder parameter, the fully one-pass implementation is a subset of the proposed algorithm. An additional topic discussed in this paper is about video buffering. Since a hypothetical reference decoder (HRD) has been defined in H.264/AVC to guarantee that the buffers never overflow or underflow, the more accurate rate-allocation schemes are proposed to satisfy these requirements of HRD.
Performance comparison of a fuel cell-battery hybrid powertrain and a fuel cell ultracapacitor hybrid powertrain

This work studies two hybrid power systems for vehicle applications: a fuel cell-battery hybrid powertrain and a fuel cell-ultracapacitor hybrid powertrain. First, the characteristics of fuel cell, battery, and ultracapacitor as power sources are summarized. Then the configurations of the two types of hybrid fuel cell powertrains are presented. Finally, example hybrid powertrains are designed and simulated using ADVISOR. The simulation results indicate that ultracapacitors can more effectively assist the fuel cell to meet the vehicle power demand and help achieve a better performance and a higher fuel economy.
An improved active shape model for face alignment

In this paper, we present several improvements on conventional active shape models (ASM) for face alignment. Despite the accuracy and robustness of ASMs in image alignment, its performance depends heavily on the initial parameters of the shape model, as well as the local texture model for each landmark and the corresponding local matching strategy. In this work, to improve ASMs for face alignment, several measures are taken. First, salient facial features, such as the eyes and the mouth, are localized based on a face detector. These salient features are then utilized to initialize the shape model and provide region constraints on the subsequent iterative shape searching. Secondly, we exploit edge information to construct better local texture models for landmarks on the face contour. The edge intensity at the contour landmark is used as a self-adaptive weight when calculating the Mahalanobis distance between the candidate and reference profile. Thirdly, to avoid unreasonable shift from pre-localized salient features, landmarks around the salient features are adjusted before applying global subspace constraints. Experiments on a database containing 300 labeled face images show that the proposed method performs significantly better than traditional ASMs.
A design of readout circuit for 384×288 uncooled microbolometer infrared focal plane array

A readout integrated circuit (ROIC) for uncooled infrared focal plane array (IRFPA) is presented in this paper. The ROIC is designed for a 384×288 detector array made of amorphous silicon microbolometers with a pixel-pitch of 35μm. A capacitive trans-impedance amplifier (CTIA) is used in each column to integrate the pixel signal. The design is tuned on a detailed theoretical analysis of the sensor signal and noise properties. The chip has been fabricated using a 0.35μm 2P4M CMOS process under 5V supply voltage. The ROIC can operate at a data rate of 7MHz, achieving a frame rate of 60Hz and the total power dissipation is less than 108m W. A 32×32 experimental chip has been tested.
FGS Coding Using Cycle-Based Leaky Prediction Through Multiple Leaky Factors

This paper proposes a fine granularity scalable (FGS) coding using cycle-based leaky prediction, in which the multiple leaky factors are used to yield enhancement layer prediction to make a good compromise between coding efficiency and drift error. In this proposed method, first, the error propagation for leaky prediction with two leaky factors is theoretically analyzed in case only the base-layer bitstream and part of the enhancement-layer bitstream are available at the decoder. Based on this analysis, in this paper, we investigate how to effectively introduce enhancement-layer information into the prediction loop for enhancement-layer coding by the proper leaky factors to constrain drift error while keeping high coding efficiency. Furthermore, a coefficient scaling approach in the transform domain is proposed to address the decoding complexity issue for multiple reconstructions of partial enhancement layers at different quality levels. Finally, an encoder optimization approach is presented to further control drift error for multiple FGS layers coding. The experimental results show that compared to AR-FGS in JSVM, the proposed method can significantly improve the coding performance over a wide range of bitrates.
High-Resolution Face Fusion for Gender Conversion

This paper presents an integrated face image fusion framework, which combines a hierarchical compositional paradigm with seamless image-editing techniques, for gender conversion. In our framework a high-resolution face is represented by a probabilistic graphical model that decomposes a human face into several parts (facial components) constrained by explicit spatial configurations (relationships). Benefiting from this representation, the proposed fusion strategy is able to largely preserve the face identity of each facial component while applying gender transformation. Given a face image, the basic idea is to select reference facial components from the opposite-gender group as templates and transform the appearance of the given image toward the selected facial components. Our fusion approach decomposes a face image into two parts-sketchable and nonsketchable ones. For the sketchable regions (e.g., the contours of facial components and wrinkle lines, etc.), we use a graph-matching algorithm to find the best templates and transform the structure (shape), while for the nonsketchable regions (e.g., the texture area of facial components, skin, etc.), we learn active appearance models and transform the texture attributes in the corresponding principal component analysis space. Both objective and subjective quantitative evaluation results on 200 Asian frontal-face images selected from the public Lotus Hill Image database show that the proposed approach is able to give plausible gender conversion results.
Intelligent analysis oriented surveillance video coding

The fast growth of surveillance video big data presents great challenges to the video coding technology. Most existing video coding techniques target for visual quality optimization, while the ultimate utility of surveillance videos mainly lies in intelligent analyses, e.g., pedestrian detection and vehicle tracking. In view of this, we aim at proposing an efficient, standard-compatible and simultaneously analysis-friendly coding framework for intelligent surveillance videos. In particular, the foreground objects are first extracted from the video sequence by accurate background modeling. Subsequently, the foregrounds can be constructed as a sequence and compressed in higher quality while very few background pictures are required to signal in lower quality. At the decoder side, the foreground objects can be directly used for efficient analysis tasks and the surveillance videos can be also reconstructed by synthesizing background and foreground frames. The effectiveness and potential of the proposed framework have been demonstrated in the pedestrian detection application, where the coding bits can be greatly saved with the detection accuracy being well maintained.
Accurately weighting subbands in temporal wavelet transform

Wavelet-based scalable video coding is a promising technique for streaming video applications; especially the motion compensated temporal filtering (MCTF). It usually decomposes the input video frames into several subbands in temporal domain. A quantization error in each subband contributes to the overall distortion in the ultimate reconstructed video with different weights. These weighting factors play an important role in allocating bit budget to different subbands and influence the coding performance greatly. In theory, these weighting factors can be derived from the wavelet filter, but they are not match the experimental data very well. This paper first proposes a probability model to estimate the influence of different motion connections in MCTF. And the zero-coefficient percentage of each subband is presented to model the influence of accumulated rounding error. These two proposed models can make a good estimation on the subband weighting factors. The experimental results show that they have the consistent trend with actual data
Joint Source-Channel Rate-Distortion Optimization for H.264 Video Coding Over Error-Prone Networks

For a typical video distribution system, the video contents are first compressed and then stored in the local storage or transmitted to the end users through networks. While the compressed videos are transmitted through error-prone networks, error robustness becomes an important issue. In the past years, a number of rate-distortion (R-D) optimized coding mode selection schemes have been proposed for error-resilient video coding, including a recursive optimal per-pixel estimate (ROPE) method. However, the ROPE-related approaches assume integer-pixel motion-compensated prediction rather than subpixel prediction, whose extension to H.264 is not straightforward. Alternatively, an error-robust R-D optimization (ER-RDO) method has been included in H.264 test model, in which the estimate of pixel distortion is derived by simulating decoding process multiple times in the encoder. Obviously, the computing complexity is very high. To address this problem, we propose a new end-to-end distortion model for R-D optimized coding mode selection, in which the overall distortion is taken as the sum of several separable distortion items. Thus, it can suppress the approximation errors caused by pixel averaging operations such as subpixel prediction. Based on the proposed end-to-end distortion model, a new Lagrange multiplier is derived for R-D optimized coding mode selection in packet-loss environment by taking into account of the network conditions. The rate control and complexity issues are also discussed in this paper
Low-Complexity Adaptive Block-Size Transform Based on Extended Transforms

In this paper, a low-complexity 8times8/4times4 adaptive block-size transform (ABT) scheme is tentatively proposed for the Chinese audio and video coding standard (AVS). In the proposed ABT scheme, an integer 8times8 transform is derived from the integer 4times4 transform used in AVS according to a transform extension principle. The 8times8 transform not only has high energy compacted property but also can be merely implemented within several additions and shifts, and all intermediate results are limited with 16-bit. The 8times8 transform and 4times4 transform can be merged together and share the same scale matrix so that the hardware units and storage resources are efficiently saved for both encoder and decoder. The experimental results on numerous sequences show that the proposed ABT scheme can achieve significant performance improvement for AVS
Locally Linear Regression for Pose-Invariant Face Recognition

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.
Low-delay View Random Access for Multi-view Video Coding

Multi-view video coding is becoming a very active research topic, as multi-view video system provides the interactive feature which makes viewers experience the free viewpoint navigation within the range covered by the shooting cameras compared with the traditional single view video. Multi-view video coding aims at compressing the redundancy between views besides temporal correlations within each view. Rapid view random access is a basic requirement for multi-view video communication. Though inter-view prediction enhances the coding efficiency, it limits the rapid view random access capability. In this paper, we propose three approaches to provide low-delay view random access capability while keeping the high rate-distortion performance. The proposed techniques, including SP/SI frame coding, interleaved view coding and secondary representation coding, vastly reduce the decoding delays while view random access occurs and greatly improve the ability of switching between views.
Progressive Image Restoration through Hybrid Graph Laplacian Regularization

In this paper, we propose a unified framework to perform progressive image restoration based on hybrid graph Laplacian regularized regression. We first construct a multi-scale representation of the target image by Laplacian pyramid, then progressively recover the degraded image in the scale space from coarse to fine so that the sharp edges and texture can be eventually recovered. On one hand, within each scale, a graph Laplacian regularization model represented by implicit kernel is learned which simultaneously minimizes the least square error on the measured samples and preserves the geometrical structure of the image data space by exploring non-local self-similarity. In this procedure, the intrinsic manifold structure is considered by using both measured and unmeasured samples. On the other hand, between two scales, the proposed model is extended to the parametric manner through explicit kernel mapping to model the inter-scale correlation, in which the local structure regularity is learned and propagated from coarser to finer scales. Experimental results on benchmark test images demonstrate that the proposed method achieves better performance than state-of-the-art image restoration algorithms.
Data-based predictive control with delay compensation for networked nonlinear systems with packet dropouts and delays in two channels

In this paper, it addresses a sort of predictive control with the delay compensation for a kind of nonlinear networked control systems, which have packet dropouts and delays in both sensor-to-controller channel and controller-to-actuator channel. Firstly, an event trigger is designed at the controller side to save the limited resources. Only when the control signals satisfy the event-triggered condition, can the control signals be transmitted to the controller. Secondly, in order to reduce the influence of the delays and packet dropouts, a delay compensator is designed at the actuator side, which includes calculating the control augmenters on the basis of the measured output errors at the controller side and designing an adaptive control algorithm in accordance with the newest control increment accessible at the actuator side. Then a sufficient condition is derived to ensure the stability of closed-loop systems and the convergence of output error. The method mentioned in this thesis is based on the data of the inputs and outputs of the system, without depending on the accurate information of the model. Finally, a numerical simulation is employed to demonstrate the practicability of the proposed scheme.
Research on coke oven gas-collector pressure decoupling control

The gas-collector pressure control system of coke oven has the time-varied characteristic with strongly coupling, high non-linear, which is influenced by the process disturbances with high amplitudes. Based on the introduction about the coke oven gas collector coupling and its effect, all kinds of control method settling out the coupling of the gas collector pressure system are discussed in details, such as fuzzy controller, neural network controller, expert controller and hybrid intelligent controller. In the end, the existed problems and future research directions in the related field is concluded.
Self-Frequency Conversion Laser in Nd-Doped Calcium Barium Niobate Ferroelectric Crystal

We demonstrate an efficient diode-pumped Nd:CBN-28 ferroelectric crystal laser for the first time. The maximum output power of 0.536 W is achieved with a slope efficiency as high as 27.7%, which is higher than that in the Nd:SBN ferroelectric crystal laser. The self-frequency doubling and self-sum-frequency processes are obtained simultaneously under intracavity conditions in the diode pumped Nd:CBN-28 laser. These processes show the ability of the Nd:CBN-28 crystal as a nonlinear optical multi-frequency converter without any angle or temperature tuning.