nils-herrmann/clean_scirepeval huggingface dataset:
Input-to-state stabilization of switched nonlinear systems

This note derives some sufficient conditions to ensure that the whole switched nonlinear system is input-to-state stabilizable (ISS) when each mode is ISS. Both cases, that the switchings of system modes coincide exactly and that they do not coincide with those of the corresponding controllers, are considered. For the latter, a model-based identification scheme is used to identify the system modes. The proposed scheme can overcome the finite escape time that may happen in this case.
Detail-Enhanced Exposure Fusion

In a typical processing chain of image enhancement, an exposure fusion scheme can be used to synthesize a more detailed low dynamic range (LDR) image directly from a set of differently exposed LDR images, without generation of an intermediate high dynamic range image. In this brief, 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 LDR images simultaneously. The extracted fine details are then added to an intermediate LDR image which is fused by simply using an existing exposure fusion scheme. With this, the proposed scheme can enhance fine details to produce sharper images.
Distributed optimal energy scheduling based on a novel PD pricing strategy in smart grid

Pricing function plays an important role in optimal energy scheduling problem in smart grid systems. The authors propose a novel real-time pricing (RTP) strategy named proportional and derivative (PD) pricing
. Different from conventional RTP strategies, which only depend on the current total energy consumption, their proposed pricing strategy also takes the historical energy consumption into consideration, which aims to further fill the valley load and shave the peak load. An optimal energy scheduling problem is then formulated to minimise the total social cost of the overall power system. Two different distributed optimisation algorithms with different communication strategies are proposed to solve the problem. Several case studies implemented on a heating ventilation and air conditioning system are tested and discussed to show the effectiveness of both the proposed pricing function and distributed optimisation algorithms.
A router based unequal error control scheme for video over the Internet

This paper presents a router based unequal error protection scheme for video over the Internet. The proposed scheme classifies the whole bitstream into two priorities according to their importance and packetizes them into packets with two priorities. To reduce the loss ratio of video packets with higher priority, an active queue management scheme is designed at each router to provide more chance for these packets to transmit over the Internet. Compared to an end-to-end based approach, in which some redundancy packets are generated to protect the packets with higher priority, our proposed scheme utilizes the whole bandwidth for the source coding. Therefore, the final picture quality is improved.
Variable frame rate encoding via active frame-skipping

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. In this paper, an active frame-skipping scheme is proposed to optimise the balance between spatial and temporal quality. 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 significant than that in average PSNR's.
Price based joint source and channel coding for real time video applications over the Internet with flow rate management

In this paper, we first propose an adaptation scheme at each source for adjusting send rates and channel code ratios of all flows at each source by using a joint source and channel coding based approach. Sufficient conditions are derived for formulating such a challenging problem as a convex optimization problem. A simple algorithm is provided to solve this problem. We then design a simple scheduling algorithm for transmitting packets from all flows at each link by using a weighted round robin based method. The proposed adaptation scheme at each source and the scheduling algorithm at each link form a framework for real time video applications over the Internet. With the proposed framework, Internet service providers (ISPs) have incentive to increase their capacities so that the Internet is more friendly to high bandwidth consuming applications such as real time video applications over the Internet.
De-ghosting of HDR images with double-credit intensity mapping

Ghosting artifacts are usually caused by moving object when composing a high dynamic range image from multiple differently exposed conventional images. In this paper, a robust de-ghosting algorithm is proposed based on a double-credit intensity mapping function (IMF) and an adaptive threshold model derived from statistical training. The double-credit IMF is estimated using both pixel intensity distribution and spatial correlation. A statistical threshold model is trained from the image database, and the key parameters are determined on the fly with variance vector calculated during the IMF estimation to adapt to different scenarios. Optimal bidirectional comparison is used for further improves the detection accuracy. The experiments show the effectiveness of the proposed de-ghosting method.
Automated road extraction from LiDAR data based on intensity and aerial photo

A clustering method based on intensity data is used to extract road from LiDAR data in this paper. And the result is refined by using the spectrum information of aerial images. An experiment is performed with a group of LiDAR data in BeiJing. The results presented in this paper have shown that clustering method based on intensity could extract road in some extent, but many error points exist at the same time. When fusing the spectrum information of aerial images, the experiment result is improved obviously. Large amount of error points is removed, while the point cloud of road keep down.
Intra Coding With Adaptive Partial Reconstruction

Intra prediction improves coding performance by reducing inter pixel redundancy. However, to accommodate the use of block transforms, not all pixels can be predicted from reconstructed pixels that are located close to themselves. This causes prediction performance to suffer as pixel values further apart are less correlated. This paper presents additional intra coding modes designed with the goal of improving prediction performance. Experimental results show an average gain of about 2% in the key technical area software when the new modes are incorporated in the current 8×8 prediction modes. Since the new coding modes (8×8) are designed with transform size smaller than coding block size, the modes can also be useful when the source block is larger than the maximum transform size.
An edge-based method for aligning differently exposed images

This paper presents an edge-based method for aligning a sequence of differently exposed images. The edge focusing is employed in Canny detector to accurately extract structural boundaries without weak or spurious edges. Two criteria for selecting thresholds of Canny detector are proposed so that edge detection is independent of image intensities. A histogram-based matching is developed to fast estimate transform parameters in D2 space instead of D3 space. Moreover, the coarse-to-fine technique by Gaussian Pyramid is employed to further reduce search space, and progressive image alignment is adopted to improve accuracy. Experimental results demonstrate that this method is accurate to 2 pixels.
New Framework for Encoder Optimization of Scalable Video Coding

In this paper, a new framework is proposed for the encoder optimization of scalable video coding (SVC). It is used to provide guidelines for the design of rate control and fast mode decision at the base layer and base layer selection at enhancement layer for the SVC. It is also pointed out that the coding technology of inter-layer prediction with two base layers is desired for the combined coarse granular scalability and spatial scalability such that the coding efficiency losses at layers with higher resolution are about 10% when compared to the corresponding single-layer video coding.
A unified approach for stability analysis of impulsive hybrid systems

We present a unified approach for the stability analysis of impulsive hybrid systems. The approach is composed of two key steps. The first step is to group a number of modes of the hybrid system together with the corresponding intervals and reset maps as units. This can be done by analyzing the discrete property of an impulsive hybrid system. The second step is to find an operation to combine each unit together. We show that the system is stable if the Lyapunov function is non-increasing along each unit of the system. In particular, we use the approach to analyze the stability of impulsive differential systems, sampled-data control systems and impulsive switched systems.
Intra-prediction with adaptive sub-sampling

Intra-prediction improves coding performance by reducing inter-pixel redundancy. However, to accommodate the use of block transforms, not all pixels can be predicted from reconstructed pixels that are located close to themselves. This causes prediction performance to suffer as pixel values further apart are less correlated. This paper presents additional intra-prediction modes designed with the goal of improving prediction performance. Experimental results show an average gain of about 2% in KTA when the new modes are incorporated in the current 8×8 prediction modes. Since the new coding modes (8×8) are designed with transform size smaller than coding block size, the modes can also be useful when the prediction unit is larger than the maximum transform size. The use of smaller transform sizes can potentially lead to reduction of decoder complexity and implementation costs.
An MPEG-4 FGS-based statistical multiplexer

We present a novel fine granular scalability (FGS)-based statistical multiplexing scheme for video broadcasting. In the light of our statistical study on intra- and inter-layer correlation, we develop a simple but efficient statistical multiplexing algorithm based on the proposed fairness criterion and smoothness constraint. Simulation results show effectiveness of the proposed statistical multiplexer in terms of high multiplexing efficiency, high bandwidth utilization, fairness and smoothness.
On Blind Quality Assessment of JPEG Images

JPEG is still the most widely used image compression format. Perceptual quality assessment for JPEG images has been extensively studied for the last two centuries. While a large number of no-reference perceptual quality metrics have been proposed along the years, it is shown in this paper that on existing image quality databases, statistically, performance of many those metrics is not better than the quality factor (Q) for JPEG images, as used in the popular implementation by the IJG (Independent JPEG Group). It should be noted that Q or the quantization table computed from Q is almost always availabe at the decoder end so analysis is focused on no-reference or blind quality assessment metrics. This research highlights the fact that despite of many progress achieved in that area, JPEG quality assessment is still a topic worth revisiting and further investigation.
Image quality/distortion metric based on α-stable model similarity

State-of-the-art objective image quality assessment (IQA) algorithms are generally designed for specific distortion types or based on complicated training procedures. For general-purpose applications, we propose a wavelet based no-reference IQA index nominated by Image Quality/Distortion Metric (IQDM). We characterize the leptokurtic and heavy-tailed behaviors of natural image's wavelet coefficients by symmetric α-stable (SαS) probability density, and distortion-caused distribution deviation is then used to gauge image quality by comparing degraded and reference SαS models in multi-scale wavelet sub-bands. We investigate the relationship between original and degraded parameters over scales, accordingly infer the reference parameters from the degraded ones using a linear approximation. A characteristic PDF for SαS is introduced and its closed form of Kullback-Leibler distance (KLD) is derived as IQDM using the model parameters. Extensive experiments demonstrate the highly comparable performance of IQDM to the distortion-sensitive or training based methods in consistency with subjective assessment scores.
Efficient Deblocking With Coefficient Regularization, Shape-Adaptive Filtering, and Quantization Constraint

We propose an effective deblocking scheme with extremely low computational complexity. The algorithm involves three parts: local ac coefficient regularization (ACR) of shifted blocks in the discrete cosine transform (DCT) domain, block-wise shape adaptive filtering (BSAF) in the spatial domain, and quantization constraint (QC) in the DCT domain. The DCT domain ACR suppresses the grid noise (blockiness) in monotone areas. The spatial-domain BSAF alleviates the staircase noise along the edge, and the ringing near the edge and the corner outliers. The narrow quantization constraint set is imposed to prevent possible oversmoothing and improve PSNR performance. Extensive simulation results and comparative studies are provided to justify the effectiveness and efficiency of the proposed deblocking algorithm.
Brightness preserving video contrast enhancement using S-shaped Transfer function

This paper presents an efficient perceptual model inspired efficient video contrast enhancement algorithm. We propose a S-shaped transfer function for image pixel values that effectively improves the perceived contrast while preserving brightness of the scene. The S-shaped transfer function has only one control parameter that can be adaptively chosen for different video contents, such as sports, cartoon, news, and landscape programs. Then, the input image brightness is further preserved, in order to maintain the perception of human visual system (HVS) to some special scenes, such as dark scene and seaside scene. Experiments and comparative study on VQEG Phase I test database demonstrate that the proposed S-shaped Transfer function based Brightness Preserving (STBP) contrast enhancement algorithm outperforms various histogram equalization based methods such as HE, DSIHE, RSIHE and WTHE, yet with much lower computational complexity.
No-reference image quality assessment metric by combining free energy theory and structural degradation model

In the research of image quality assessment (IQA), no-reference approaches are usually thought of as a big challenge since none of original image information is available. To tackle this problem, we propose a new no-reference image quality metric through combining two recently proposed reduced-reference IQA models, namely the free energy based distortion metric (FEDM) and the structural degradation model (SDM). In this work, it will be shown that there exists an approximate linear relationship between the original image information of the free energy feature and the structural degradation information. Based on this observation and the application of support vector machine (SVM) that is widely used in the current study of IQA, our newly developed No-reference Free energy and Structural degradation based Distortion Metric (NFSDM) is found to alleviate the dependance of original images, and has achieved remarkably well prediction accuracy, outperforming the most two full-reference IQA approaches PSNR/SSIM and several mainstream no-reference image quality metrics.
Image Reconstruction From Random Samples With Multiscale Hybrid Parametric and Nonparametric Modeling

Statistical image modeling is of central importance to many image-processing tasks that are ill-posed in nature. Existing image models can be categorized as parametric models and nonparametric models according to the statistical techniques used. In this paper, we develop a hybrid image reconstruction (HIR) algorithm from sparse random samples using parametric and nonparametric modeling of images. More specifically, the modeling strength of the parametric and nonparametric techniques are combined within a multiscale framework. The linear autoregressive parametric model and kernel regressive nonparametric models are used to explore the interscale and intrascale dependencies of the image, respectively. The proposed HIR algorithm is capable of recovering the image from very sparse samples (e.g., 5%), and experimental results suggest that the proposed algorithm achieves noticeable improvement over some of the existing approaches in terms of both peak signal-to-noise ratio and subjective qualities of the reconstruction results.
Simultaneous deblocking and error concealment for decoded visual signal

We propose a unified deblocking/error-concealment algorithm for simultaneously alleviating blockiness and restoring lost blocks. Blockiness and block loss are two of the most frequently encountered artifacts for block-DCT based visual signal coding and transmission. The problem of error-concealment and deblocking is formulated as block-wise expectation estimation processes conditioned on available pixels in a local neighborhood. A nonparametric kernel regression approach is used for approximating the conditional probability and thus the conditional expectation of each image block. Missing blocks are restored and blockiness are suppressed with a single pass of the algorithm. The algorithm is highly efficient in that only block-wise additions and multiplications in pixel domain within a local neighborhood are involved. Experimental results are provided to justify the effectiveness of the proposed unified error-concealment/deblocking algorithm.
Information security display via uncrowded window

With the booming of visual media, people pay more and more attention to privacy protection in public environments. Most existing research on information security such as cryptography and steganography is mainly concerned about transmission and yet little has been done to prevent the information displayed on screens from reaching eyes of the bystanders. This "security of the last foot (SOLF)" problem, if left without being taken care of, will inevitably lead to the total failure of a trustable information communication system. To deal with the SOLF problem, for the application of text-reading, we proposed an eye tracking based solution using the newly revealed concept of uncrowded window from vision research. The theory of uncrowded window suggests that human vision can only effectively recognize objects inside a small window. Object features outside the window may still be detectable but the feature detection results cannot be efficiently combined properly and therefore those objects will not be recognizable. We use eye-tracker to locate fixation points of the authorized reader in real time, and only the area inside the uncrowded window displays the private information we want to protect. A number of dummy windows with fake messages are displayed around the real uncrowded window as diversions. And without the precise knowledge about the fixations of the authorized reader, the chance for bystanders to capture the private message from those surrounding area and the dummy windows is very low. Meanwhile, since the authorized reader can only read within the uncrowded window, detrimental impact of those dummy windows is almost negligible. The proposed prototype system was written in C++ with SDKs of Direct3D, Tobii Gaze SDK, CEGUI, MuPDF, OpenCV and etc. Extended demonstration of the system will be provided to show that the proposed method is an effective solution to SOLF problem of information communication and display.
On monotonicity of image quality metrics

Perceptual image quality assessment (IQA) is an important research topic of visual signal processing both in its own right and for its utility in designing various optimal image processing and coding algorithms. This work is concerned with an issue that has been largely overlooked by the research community of IQA, that is, the monotonicity, or lack of it, between the subjective scores and the predictions of image quality metrics (IQM) for images with compression artifacts. We analyze the data of several well-known databases for IQA and expose among them a large number of instances of non-monotonicity between subjective and objective quality scores. Further, we observe that a nonlinear dynamical model of 3D cusp catastrophe can well explain the intricate relationship between the subjective and objective quality scores. Our findings identify an inherent flaw of current signal-distance or fidelity-based IQMs, which neglect the psycho-physiological aspect of human visual perception. This research suggests a new direction of IQA research and it also sheds light on the design of subjective quality evaluation process.
Image reconstruction from random samples with parametric and nonparametric modeling

Statistical image modeling is essential for many image processing tasks that are ill-posed in nature. Existing image models can be categorized as parametric models and nonparametric models according to the statistical techniques used. In this paper, we develop a new image reconstruction algorithm from sparse random samples based on hybrid parametric and nonparametric modeling of images. More specifically, the modeling strength of the parametric and nonparametric techniques are combined within a multiscale framework where the parametric and nonparametric image models are used to solve the interscale and intrascale estimation problems, respectively. The proposed algorithm is capable of recovering the image from very sparse samples (e.g. 5%), and experimental results suggest the proposed algorithm achieves significant improvement over existing pure parametric and nonparametric based approaches both in terms of PSNR and subjective qualities of the reconstruction results.
Cross-dimensional quality assessment for low bitrate video

In this paper, we address the problem of evaluating perceptual visual quality of compressed video under different settings for mobile communication. A 5-dimensional video feature space is constructed with the codec type, video content, frame size, frame rate and bitrate. The video quality assessment is formulated as a transformation between the multidimensional feature space and the quality space. Subject viewing tests for videos coded with H.263 and H.264 at different bitrates and spatial/temporal resolutions are performed. Based on statistical analysis, we find that the perceptual quality of the decoded video is affected by the encoder type, video content, bitrate, frame rate and frame size, in a descending order for significance. The methodology and findings in this paper designate new researching direction in video quality assessment and can be extended to other applications.