nils-herrmann/clean_scirepeval huggingface dataset:
Robust Stability of Switched Linear Systems With Polytopic Uncertainty

In this paper, we study robust stability of a class of switched linear systems (SLSs) that is composed of a finite number of linear continuous variable systems (CVSs) and the switchings of CVSs are predefined and represented by a finite state machine. Each CVS consists of a cluster of unstable matrices, which are simultaneously block-triangularizable. This type of SLSs cannot be addressed by existing results. A decomposition method is used to decompose the original system into several lower dimensional SLSs at the diagonal block positions. Our results show that the original higher dimensional SLS is asymptotically stable if all these lower dimensional SLSs are asymptotically stable. The synergy between the continuous of each lower dimensional SLSs and the discrete dynamics is fully utilized by the cycle analysis method to derive sufficient conditions on the robust stability of each lower dimensional SLS. With our method, all CVSs in a cycle instead of each individual CVS is taken as an analysis unit. The condition on the stability of SLSs can then be relaxed in the sense that Lyapunov functions are only required to be non-increasing along each type of cycle instead of each CVS.
Single-Pass Rate Control With Texture and Non-Texture Rate-Distortion Models

One of the challenges in video rate control lies in determining a quantization parameter (Qp) that will be used for both the rate-distortion (R-D) optimization process and the quantization of transform coefficients. In this paper, we attempt to achieve effective rate control with a different approach. By modeling the relationships of distortion, texture bits, non-texture bits, and Qp, we derive the Qp required for both R-D optimization and quantization through Lagrangian optimization. From experiments with several video sequences, we found that our rate control scheme is capable of effective rate control with only a few model updates during encoding. The proposed rate control scheme adapts quickly to the characteristics of the source data and is particularly effective at controlling the rate of videos with high and unpredictable motion content.
A Novel Rate Control Scheme for Low Delay Video Communication of H.264/AVC Standard

This paper presents a novel rate control scheme for low delay video communication of H.264/AVC standard. A switched mean-absolute-difference (MAD) prediction scheme is introduced to enhance the traditional temporal MAD prediction model, which is not suitable for predicting abrupt MAD fluctuations. Our new model could reduce the MAD prediction error by up to 69%. Furthermore, an accurate linear rate-quantization (R-Q) model is also formulated to describe the relationship between the total amount of bits for both texture and nontexture information and the quantization parameter (QP), so that the negative effect caused by the inaccurate estimation of nontexture bits is removed. By exploring the relationship between peak signal-to-noise ratio and QP value, the proposed linear R-Q model could further optimize QP calculation at the macroblock level. When compared with the rate control scheme JVT-G012 which is adopted by the latest JVT H.264/AVC reference model JM9.8, the proposed rate control algorithm could reduce the mismatch between actual bits and target ones by up to 75%. To meet the low delay requirement, the buffer is better controlled to prevent overflowing and underflowing. The average luminance PSNR of reconstructed video is increased by up to 1.13 dB at low bit rates, and the subjective video quality is also improved
An iterative method for hypothetical reference decoder [video coding]

We propose a simple iterative method to verify whether a coded bitstream conforms to a hypothetical reference decoder (HRD). A concept of maximum tolerated delay (MTD) is introduced to study the possible low-delay operation, such that we can bound the degree of incorrect motion rendition caused by the variation in end to end delay in the neighborhood of big pictures. Our method can be used in the design of a rate control algorithm to improve the possibility of a coded bitstream that conforms to the HRD.
A Perceptually Relevant MSE-Based Image Quality Metric

Image quality metrics (IQMs), such as the mean squared error (MSE) and the structural similarity index (SSIM), are quantitative measures to approximate perceived visual quality. In this paper, through analyzing the relationship between the MSE and the SSIM under an additive noise distortion model, we propose a perceptually relevant MSE-based IQM, MSE-SSIM, which is expressed in terms of the variance of the source image and the MSE between the source and distorted images. Evaluations on three publicly available databases (LIVE, CSIQ, and TID2008) show that the proposed metric, despite requiring less computation, compares favourably in performance to several existing IQMs. In addition, due to its simplicity, MSE-SSIM is amenable for the use in a wide range of image and video tasks that involve solving an optimization problem. As an example, MSE-SSIM is used as the objective function in designing a Wiener filter that aims at optimizing the perceptual visual quality of the output. Experimental results show that the images filtered with a MSE-SSIM-optimal Wiener filter have better visual quality than those filtered with a MSE-optimal Wiener filter.
Detail-enhanced fusion of differently exposed images

In a typical processing chain of an image enhancement, an exposure fusion scheme can be used which usually includes smoothing effect which allows prevention of occurrence of seams in the final image. But such step usually comes with a price of losing fine details thereby blurring the final image, which then makes the scheme unable to preserve fine details as good as high dynamic range (HDR) imaging schemes. In this paper, we introduce a new quadratic optimization based method to extract fine details from a vector field. The new method extracts fine details from a set of differently exposed low dynamic range (LDR) images simultaneously. The extracted details are then added to an intermediate image that is fused by simply using an existing exposure fusion scheme. With this, the proposed scheme can preserve fine details as good as a normal HDR imaging scheme.
Proactive frame-skipping decision scheme for variable frame rate video coding

Many rate control algorithms focus on the adjustment of quantisation values to retain a certain buffer level, and arbitrary frame-skipping is often needed to keep the buffer from overflow at very low bit-rates. A content adaptive rate control is proposed to optimise the balance between spatial and temporal quality via active frame-skipping. The occurrence of frame-skipping is jointly dependent on the temporal and spatial quality of the video, and on the fullness of the buffer. This helps to achieve a consistent spatial and temporal quality and to enhance the overall perceptual quality. Experimental results show that the new scheme is simple but very effective, with large average PSNR gains and consistently improved visual quality, and the improvement in perceptual quality is much more significant than that in average PSNRs.
Fast Mode Decision for Coarse Granular Scalability via Switched Candidate Mode Set

This paper presents an improved fast mode decision algorithm for coarse grain scalability (CGS). The modified fast mode decision algorithm extends previous work to provide better encoder complexity reduction with insignificant degradation in picture quality. The candidate mode set is adaptive to the quantization parameter difference between a layer and its "base layer". Furthermore, the proposed scheme fully utilizes the statistics of mode transition between a layer and its "base layer" when the quantization parameter difference between these two layers is small. Simulation results demonstrate that the proposed scheme provides up to 64% of time saving compared with the original SVC encoder.
Hierarchical random exploring with multiple linking modes

Narrow passages in the configuration space pose challenge for sampling-based planners. In this paper, the conventional wisdom of ”divide and conquer” is adopted to design a hierarchical strategy to address this challenging problem. Rapidly-exploring random tree (RRT) is taken as an example to illustrate the proposed strategy. A hybrid map which consists of a topological map and a set of metric maps is built up to represent the configuration space and information on the narrow passages is included in the topological map. The path from the starting configuration to the goal one is divided into segments using the information on the narrow passages. The RRT runs independently to search for a sub-path for each segment. The overall path is obtained by concatenating all the sub-paths. In addition, to improve the possibility of avoiding obstacles, two more circle-based linking modes are introduced to the RRT planner. Experimental results show that the planning time can be reduced significantly using the proposed planning methods. As a result, the success rate of path planning can be improved if the time for planning is limited.
Adaptive intra-frame quantization for very low bit rate video coding

This paper presents, for I-frames, a generic relationship among the quantization value, data bits, and their spatial complexity. This relationship provides a mechanism to select the quantization value for an I-frame (with different spatial complexity) at given bit budget before actually encoding it. Therefore it helps to adaptively distribute the bit budget among I-, P- and B-frames in a sequence. Experiments show that the adaptive intra frame quantization algorithm reduces the number of frame skippings significantly with comparable average PSNRs, and thus improves the overall subjective quality of the reconstructed video.
Perceptually fine detail extraction from a vector field

In this paper, a perceptually weighted quadratic optimization problem is formulated for the extraction of fine details from a vector field. To achieve better edge-preserving performance, a weighted sum square error (SSE) is adopted in the proposed optimization problem. The weighted SSE is used as data term in the proposed quadratic optimization problem to refine the extraction strength according to local intensity variance. Experimental results show that the perceptually weighted optimization problem can minimize sharp edge information in the extracted details.
Learning visual odometry for unmanned aerial vehicles

We describe a method to learn the flight dynamics of an unmanned aerial vehicle (UAV). This follows the recent trend to adopt a learning approach to Visual Odometry (VO). Our novelty is the inclusion of a module to compensate for the roll pitch angular motion of the UAV due to body vibration. This is a significant deviation from most existing works that are applied to land vehicles. We empirically verify our results on real flight data, showing that after compensating for angular vibration, the ego-motion of the UAV can be robustly estimated even by using simple regression tools. This enables the advantages of learning based VO to be within reach of the UAV community.
Dynamic threshold-based keyframe detection and its application in rate control

This study proposes a new dynamic threshold model to detect keyframes for the coding of a video sequence. The proposed detection threshold is content adaptive which dynamically updates its value at each frame by taking into consideration both the statistical properties of the video sequence and the resolution as well as the quantisation parameters. The content variation metric used in the proposed detection method utilises the information that is already generated by the encoder during the encoding process and thus it makes the detection process fast. By applying the proposed scheme to rate control, the sizes of group of pictures are now adapted to the video content and it removes the temporal redundancy among different frames better, which transforms to the improvements on coding efficiency. The experimental results have shown that the proposed method achieves peak signal-to-noise ratio (PSNR) improvement.
Conversational Video Communication of H.264/AVC with Region-of-Interest Concern

Although region-of-interest (ROI) based video coding has been well studied for some other video coding standards, its application for H.264 is still of significant interest because there exists a dilemma in the detection of the ROI due to the rate distortion optimization. In this paper, the ROI is first detected by using the direct MAD that is determined without motion information. In this way, the ROI-motion dilemma of H.264/AVC is solved. The rate control scheme with ROI concern can then adjust the quantization parameter (QP) to allocate more bits to the ROI, so the overall subjective visual quality is improved.
Variance adaptive SLIC

Superpixel segmentation becomes more and more popular in the fields of computer vision and image processing. The simple linear iterative clustering (SLIC) is widely used due to its high segmentation accuracy and low computational complexity. In this paper, we propose a variance adaptive SLIC (VASLIC) algorithm. The compactness factor of the proposed algorithm is determined according to the image neighbourhood variance. In this way, more suitable compactness number is selected for each pixel. As a result, the proposed algorithm has higher segmentation accuracy than the SLIC and lower computational complexity. Experimental results on superpixel benchmark show that this newly proposed algorithm is indeed better than the SLIC.
Pricing based differentiated bandwidth and delay services with weighted Max-Min fairness

In this paper, we design a framework that is composed of feedback signals and the corresponding source adaptation scheme to provide both differentiated bandwidth service and differentiated delay service for elastic and inelastic applications. The feedback signals consist of controlling signals and charging signals. Since the number of congested links is not limited to 1, continuous auxiliary control signals are adopted to represent the relative congestion of all links. The values of the most congested link are used as control signals for each source to adjust the sending rates. The sum of link prices will serve as the charging signals to determine the cost that each source should pay and the distribution of the profit among service providers. Traffic can achieve weighted Max-Min fairness by adjusting its sending rate according to the feedback signals, the corresponding source adaptation scheme and its willingness-to-pay. Our pricing system allows us to formulate a reasonable pricing framework to charge the sources. With the proposed scheme, each link has the incentive to increase its capacity because it can get more profit. This is very helpful for the evolution of the Internet.
Adaptive Decoder Complexity Reduction for Coarse Granular Scalability

The on-going scalable video coding (SVC) standard is an extension of H.264/AVC. It enables significantly improved compression performance at the expense of greatly increased computational complexity at both the encoder and the decoder sides. This paper presents an adaptive algorithm to reduce the complexity of decoder at the encoder side for coarse granular scalability. Hence, lightweight bitstreams are generated at the encoder which requires significantly less decoding complexity. The experimental results show that the proposed scheme provides significant reduction in the complexity of decoder with acceptable coding loss and minor impact on the encoder complexity.
Distributed optimal energy scheduling based on a novel PD pricing feedback strategy in smart grid

Pricing function plays an important role in optimal energy scheduling problem in smart grid systems. In this paper, we propose a novel real time pricing strategy named proportional and derivative (PD) pricing strategy. An optimal energy scheduling problem is then formulated by minimizing the total social cost of the overall power system. A distributed optimization algorithm based on finite-time consensus and projected gradient method is provided for each agent to iteratively determine an optimal solution to the problem. As iteration increases, the solutions from all the agents reach consensus and this solves the formulated optimal problem. A case study of heating ventilation and air conditioning (HVAC) system shows efficiency of the proposed algorithm.
L0 smoothing based detail enhancement for fusion of differently exposed images

Exposure fusion is a technique to fuse several differently exposed low dynamic range (LDR) images to an LDR image. The output image has more information than each of the input images, but it could suffer from loss of fine details. In this paper, we propose a detail-enhanced exposure fusion algorithm by introducing an L0 norm based optimization in gradient domain. The proposed algorithm extracts fine details from a vector field that is generated by using the gradient fields of the input images and adds the fine details to an intermediate image that is fused by an existing exposure fusion algorithm. Experimental results prove that the proposed method can enhance fine details for fused images.
Assess the accuracy of the Globcover cultivated land data in Northeast China

This study aims to examine the suitability of Globcover land cover dataset for its accuracy in mapping and monitoring cropland in Northeast China three provinces. To do so, Globcover dataset was compared with the reference national land cover dataset 2000 (NLCD-2000) for assessing the aggregated area and spatial extent of cropland across the study area. The results showed that the accuracy in Jilin province is higher than the other two provinces. Regions with relatively higher accuracy are mainly distributed in the central and west part of the study area, whereas low accuracy mainly exists at the east and south coastal area. The coarse spatial resolution and per pixel classification approach, as well as landscape heterogeneity, are the main reasons for large discrepancies between Globcover cropland dataset and NLCD-2000.
Detecting and composing near-identical HDR images without exposure information

In high dynamic range (HDR) imaging, two essential problems are to compose HDR image from conventional image set without any prior information about their exposures, and to access the synthesis result. To solve these problems, we first develop an exposure ratio estimation algorithm based on intensity mapping function (IMF). Then, we introduce an HDR image comparison method to verify whether two HDR images are from the same scene by using their log histogram similarity. Even though the images carrying the same information, their similarity cannot be detected by pixel-wise comparisons. We name such a pair of HDR images as near-identical images. According to experiments, our detection method is able to identify near-identical HDR images effectively, and our synthesis algorithm is able to recover the correct exposure ratios and compose near-identical HDR images.
Reducing frame skipping in MPEG-4 rate control scheme

Frame skipping in low bit video coding could significantly reduce the visual quality of the reconstructed video. This paper analyzes the' main causes of frame skipping in current MPEG-4 frame rate control scheme, and presents a number of ways to reduce the occurrence of frame skipping. The main features of the new algorithm include an adaptive thresholding technique, an adaptive P-frame target bits allocation, and an adaptive I-frame target bits allocation. Experimental results show that the new rate control scheme has significantly reduced the risks of frame skipping without sacrificing the overall PSNR of the reconstructed video.
Edge-Preserving Decomposition-Based Single Image Haze Removal

Single image haze removal is under-constrained, because the number of freedoms is larger than the number of observations. In this paper, a novel edge-preserving decomposition-based method is introduced to estimate transmission map for a haze image so as to design a single image haze removal algorithm from the Koschmiedars law without using any prior. In particular, weighted guided image filter is adopted to decompose simplified dark channel of the haze image into a base layer and a detail layer. The transmission map is estimated from the base layer, and it is applied to restore the haze-free image. The experimental results on different types of images, including haze images, underwater images, and normal images without haze, show the performance of the proposed algorithm.
Model-Based Online Learning With Kernels

New optimization models and algorithms for online learning with Kernels (OLK) in classification, regression, and novelty detection are proposed in a reproducing Kernel Hilbert space. Unlike the stochastic gradient descent algorithm, called the naive online Reg minimization algorithm (NORMA), OLK algorithms are obtained by solving a constrained optimization problem based on the proposed models. By exploiting the techniques of the Lagrange dual problem like Vapnik's support vector machine (SVM), the solution of the optimization problem can be obtained iteratively and the iteration process is similar to that of the NORMA. This further strengthens the foundation of OLK and enriches the research area of SVM. We also apply the obtained OLK algorithms to problems in classification, regression, and novelty detection, including real time background substraction, to show their effectiveness. It is illustrated that, based on the experimental results of both classification and regression, the accuracy of OLK algorithms is comparable with traditional SVM-based algorithms, such as SVM and least square SVM (LS-SVM), and with the state-ofthe-art algorithms, such as Kernel recursive least square (KRLS) method and projectron method, while it is slightly higher than that of NORMA. On the other hand, the computational cost of the OLK algorithm is comparable with or slightly lower than existing online methods, such as above mentioned NORMA, KRLS, and projectron methods, but much lower than that of SVM-based algorithms. In addition, different from SVM and LS-SVM, it is possible for OLK algorithms to be applied to non-stationary problems. Also, the applicability of OLK in novelty detection is illustrated by simulation results.
Applications of switched control in game theory

In this paper, we study a class of two-player systems. The concept of switched control is applied to design a switched strategy for player 1 such that a win-win situation can be achieved if player 2 contributes to the common good and a lose-lose situation will be reached if player 2 behaves selfishly. Our scheme is different from existing ones in the sense that player 1psilas strategy is not chosen to maximize his/her utility payoff according to player 2psilas strategy as in the conventional game theory. The knowledge that player 1 learns about player 2psilas behavior is utilized in our design. Thus, our switched controller has a learning capability.
Structural similarity indices for high dynamic range imaging

In this paper, a structural similarity index is first proposed for two images with possibly different dynamic ranges and intensities as well as possibly small rotation and translation. The proposed index is then extended by dividing two images into local windows, and the similarity is detected by checking all pairs of local windows. It is shown by experimental results that the proposed indices are more robust to large intensity and dynamic range changes of two images from the same scene than the structural similarity (SSIM) index in .