nils-herrmann/clean_scirepeval huggingface dataset:
Multi-scale exposure fusion via gradient domain guided image filtering

Multi-scale exposure fusion is an efficient way to fuse differently exposed low dynamic range (LDR) images of a high dynamic range (HDR) scene into a high quality LDR image directly. It can produce images with higher quality than single-scale exposure fusion, but has a risk of producing halo artifacts and cannot preserve details in brightest or darkest regions well in the fused image. In this paper, an edge-preserving smoothing pyramid is introduced for the multi-scale exposure fusion. Benefiting from the edge-preserving property of the filter used in the algorithm, the details in the brightest/darkest regions are preserved well and no halo artifacts are produced in the fused image. The experimental results prove that the proposed algorithm produces better fused images than the state-of-the-art algorithms both qualitatively and quantitatively.
Parking of nonholonomic mobile robots via switched control in the discrete time domain

Due to nonholonomic constraints, it is challenging to study parking of mobile robots, especially in the discrete time domain. In this paper, a switched controller is designed in the discrete time domain for the parking of mobile robots towards a target pose. The proposed controller is implemented by using the concept of a sequence of discrete time instances. The angular velocity of the robot is fixed between two discrete time instances to guarantee the controllability of the error dynamics. Moreover, the conventional pole assignment is employed to asymptotically stabilize the system. Meanwhile, instead of moving towards the target pose, the mobile robot is driven to approach the intermediated poses, which are constructed using positions of the robot at a predefined set of discrete time instances and the target pose. The introduction of the time-varying intermediated poses reduces the risk of high gain. Simulation results demonstrate the effectiveness of the proposed switched controller for parking of mobile robots.
Balanced Inter-Layer Prediction for Combined Coarse Granular Scalability and Spatial Scalability

An inter-layer prediction scheme with two base layers and a new concept of auxiliary layer are introduced in this paper. The scheme achieves a good balance among all layers for the combined coarse granular scalability and spatial scalability. The objective is to improve the coding efficiency of layers with higher resolution. Meanwhile, a price based scheme is proposed to determine the auxiliary layer. With the proposed scheme, a new element of price can be integrated into scalable video coding which in turn justifies the necessity of scalable coding
Mode-dependent fast separable KLT for block-based intra coding

In this paper, we derive separable KLTs for coding H.264/AVC intra prediction residuals, using a simple image correlation model. Our analysis shows that for some intra prediction modes, we can in fact just use the DCT for performing either the row-wise or column-wise transform. Furthermore, we also compute the KLT that should be used based on the image correlation model, which happens to have sinosuidal terms. The 4 × 4 transform also has a structure that can be exploited to reduce the operation count of the transform operation. In our simplified implementation of mode-dependent directional transforms (MDDT), we only need to make use of two matrices: the DCT and the derived KLT. Our experimental results show that in terms of coding efficiency, our proposed approach has similar performance when compared with MDDT. More importantly, compared to MDDT, our approach requires no training and has lower computational and storage costs.
Dynamic Range Analysis in High Efficiency Video Coding Residual Coding and Reconstruction

We present a method for analyzing dynamic range along the residual coding and reconstruction pathways during video coding by deriving bounds on the maximum absolute value of intermediary data using a simple combination of triangle inequality and reformulation using Kronecker products. The proposed method is then applied toward analyzing the residual coding and reconstruction process in the emerging High Efficiency Video Coding (HEVC) standard. Our analysis shows that, for an input residual with a bitdepth of (B+1) that uses a uniform quantizer, the dynamic range of quantized levels and dequantized coefficients are no more than (B+7) bits and 17 bits, respectively. Furthermore, a 16 bits transpose buffer is sufficient, while up to 5 bits of bitdepth expansion can occur in the reconstructed residual. The analysis is validated by simulation results with both randomly generated residual and encoding/decoding test video sequences using the HEVC reference software.
Controller design for the integral-input-to-state stabilization of switched nonlinear systems: a cycle analysis method

This paper considers the integral-input-to-state stabilization of a class of switched nonlinear systems, which consist of integral-input-to-state stable (IISS) subsystems and subsystems whose continuous states may eventually be unbounded even if integrals of the corresponding inputs are small. In general, a switched controller has two parts: local controllers for subsystems and their switching law. Here, a cycle analysis method is used to derive a switched controller for the integral-input-to-state stabilization of the considered system. The results obtained here extend our previous work in that all subsystems are not required to be IISS.
Aligning multi-exposed images: What are the good feature and similarity measure?

High dynamic range (HDR) imaging, a technique to synthesize a sequence of multi-exposed images, has been recently developed to reduce the dynamic range gap between captured images and real scenes. Unlike conventional cases of varying illumination in which each image is best exposed, the multi-exposed images contain severely under/over-exposed regions and have significant variations in intensity, which offer great challenge in image registration. This paper aims to identify what is the invariant representation of multi-exposed images and which similarity measure is good for these images. To this end, we present a comprehensive comparison of existing ordering features and similarity measures. Experimental results show that the mutual information is the best similarity metric, and the median threshold bitmap is the best feature in terms of accuracy and robustness.
Motion based adaptive media playout for real time video over relative differentiated quality of service

This paper proposes a motion based adaptive media playout (AMP) for video over the Internet with relative differentiated quality of service. Our scheme is based on the fact that human eyes are more sensitive to high motion videos. The concept of perceived motion energy is adopted to distinguish video motion. Different upper and lower bounds are derived for video with different motions. They are used to bound the actual playout rate, which is computed using control theory by taking both the trend of buffer occupancy variation and the difference between the buffer occupancy and a predefined threshold into consideration. The final visual quality is improved by our scheme.
Exposure-Robust Alignment of Differently Exposed Images

This letter presents a novel exposure-robust method to align differently exposed images. First, a directional mapping approach is introduced to normalize differently exposed images so as to alleviate the effect of saturation. Then, a non-parametric local binary pattern (LBP) is employed to represent intensity-invariant features of these images. An efficient two-stage alignment is proposed for motion estimation. Experiments on a variety of synthesized and real image sequences demonstrate that the proposed method is less sensitive to the reference image, and robust to 12 exposure values (EV) increments, which is superior to existing methods.
Adaptive Mad Prediction and Refined R-Q Model for H.264/AVC Rate Control

This paper presents an improved rate control scheme for the H.264/AVC video coding scheme. By analyzing the relationship between direct mean absolute difference (MAD) and actual MAD, a new MAD prediction scheme is introduced to enhance traditional linear MAD prediction model, which is unable to predict abrupt MAD fluctuations. Our proposed adaptive model could reduce MAD prediction error by up to 34%. One simple sum bit quadratic R-Q model is also presented to solve the problem caused by inaccurate texture bits estimation of H.264/AVC. With the new MAD prediction model and R-Q model, our proposed scheme could reduce the mismatch between actual frame bits and target frame bits by up to 32%, and the buffer occupancy is much closer to the ideal status. Meanwhile, reconstructed video quality is also improved by up to 0.21 dB at low bitrate
A new online learning with Kernels method in novelty detection

A new optimization problem together with its model for online learning with kernels in novelty detection is formulated in a Reproducing Kernel Hilbert Space (RKHS). By exploiting the techniques of Lagrange dual problem in a similar way to Vapnik's support vector machine (SVM), the optimization problem is solved iteratively and this gives an algorithm named online learning with kernels denoted as (OLKN). The algorithm is applied to novelty detection including real time background substraction. Such successful applications illustrate the effectiveness of the OLKN in novelty detection.
Movement detection for the synthesis of high dynamic range images

In this paper, we propose an intensity mapping function (IMF) based scheme to detect moving objects in a set of low dynamic range (LDR) images with different known exposure times. The objective is to remove ghosting artifacts from the eventual high dynamic range (HDR) image. Our contributions include a bidirectional similarity detection method, an adaptive threshold for movement detection, and an IMF based method for the synthesis of pixels to fill in the regions of moving objects. Experimental results show that the proposed scheme outperforms existing schemes.
Fast mode decision algorithm for intraprediction in H.264/AVC video coding

The H.264/AVC video coding standard aims to enable significantly improved compression performance compared to all existing video coding standards. In order to achieve this, a robust rate-distortion optimization (RDO) technique is employed to select the best coding mode and reference frame for each macroblock. As a result, the complexity and computation load increase drastically. This paper presents a fast mode decision algorithm for H.264/AVC intraprediction based on local edge information. Prior to intraprediction, an edge map is created and a local edge direction histogram is then established for each subblock. Based on the distribution of the edge direction histogram, only a small part of intraprediction modes are chosen for RDO calculation. Experimental results show that the fast intraprediction mode decision scheme increases the speed of intracoding significantly with negligible loss of peak signal-to-noise ratio.
Chroma intra prediction using template matching with reconstructed luma components

Intra coding in the current H.264/AVC video coding standard achieves high compression efficiency, in part due to the highly effective intra prediction process that exploits spatial directional correlation. However, intra prediction of chroma components in YUV 4:2:0 videos uses a limited set of possible predictions available for coding of luma components. Furthermore, coding of chroma components proceeds somewhat independently of luma components, and ignores any possible correlation between them. In this paper, we show a way of using reconstructed luma pixels to help with intra prediction of chroma pixels. By making use of the reconstructed co-located luma block to perform template matching in the luma plane, we are able to use as predictors the co-located chroma blocks of the matched luma blocks. Simulations results indicate that the proposed approach is able to obtain up to 33% chroma bit-rate reduction and up to 8% overall bit-rate reduction over H.264/AVC.
Perceptually relevant energy function for seam carving

Seam carving, an image re-targeting method, works by progressively finding and removing connected paths of low energy pixels in an image until a desired image aspect ratio is reached. In this paper, we first cast the problem of minimizing an energy function as that of minimizing a distortion cost. We then leverage on our understanding of image quality metrics/ distortion metrics in proposing a perceptually relevant energy function. Experimental results show that our proposed energy function can generate more desirable resized images in which the original structures of the images are better preserved.
Region-of-Interest Based Resource Allocation for Conversational Video Communication of H.264/AVC

Due to the complexity of H.264/AVC, it is very challenging to apply this standard to design a conversational video communication system. This problem is addressed in this paper by using region-of-interest (ROI) based bit allocation and computational power allocation schemes. In our system, the ROI is first detected by using the direct frame difference and skin-tone information. Several coding parameters including quantization parameter, candidates for mode decision, the number of referencing frames, accuracy of motion vectors and the search range of motion estimation are adaptively adjusted at the macroblock (MB) level according to the relative importance of each MB. Subsequently, the encoder could allocate more resources such as bits and computational power to the ROI, and the decoding complexity is also optimized at the encoder side by utilizing an ROI based rate-distortion-complexity (R-D-C) cost function. The encoder is thus simplified and decoding-friendly, and the overall subjective visual quality can also be improved.
Adaptive rate control for H.264

This paper presents a rate control scheme for H.264 by introducing the concept of basic unit and a linear prediction model. The basic unit can be a macroblock (MB), a slice, or a frame. It can be used to obtain a trade-off between the overall coding efficiency and the bits fluctuation. The linear model is used to solve the chicken and egg dilemma existing in the rate control of H.264. Both constant bit rate (CBR) and variable bit rate (VBR) cases are studied. Our scheme has been adopted by H.264.
Hierarchical decomposition based distributed adaptive control for output consensus tracking of uncertain nonlinear systems

In this paper, we aim to design distributed adaptive controllers for output consensus tracking of multiple nonlinear subsystems with intrinsic mismatched unknown parameters. The graph representing the communication status among subsystems is assumed to have directed and fixed topology. Only a small percentage of the subsystems can obtain the desired trajectory information, which is regarded as a virtual leader node added to the original communication graph. We first split the communication graph into a hierarchical structure according to the shortest possible path of each subsystem originated from the virtual leader. Then local adaptive controllers for subsystems in different layers can be designed in a sequential order. By introducing the estimates of the uncertainties of its neighbors located in the upper layer into the local controller of a subsystem, the transmission of parameter estimates among connected subsystems is avoided. It is proved that output consensus tracking of the overall system can be achieved asymptotically and all closed-loop signals are ensured bounded. Simulation results show the effectiveness of our scheme.
Photorealistic detail manipulation via multi-light images

In this paper, a new image synthesis framework is proposed to provide delicate description of a scene. The input is a set of images that capture the same scene under different lighting conditions. By solving a gradient domain optimization problem, the details of the input images can be combined together into an auxiliary image. Then a quadratic filter is used to divide the auxiliary image into base layer and detail layer. To make the contents in the detail layer more visible, the local contrast of the detail layer is enhanced. The base layer is replaced by one of the input image with a spatial varying exposure function to adjust the extreme luminance in base layer. The final desired image is generated by multiplying the enhanced detail layer to the adjusted base layer. It is shown that the final synthesized image is able to provide a more detailed description of the captured scene than anyone of the input images.
Content adaptive guided image filtering

Local filtering based edge-preserving smoothing technique could suffer from halo artifacts when it is applied for image enhancement. In this paper, an adaptive guided image filter is proposed by incorporating edge aware weighting which is derived from normalized local variance of a guidance image into an existing guided filter. It is shown that the proposed filter preserves sharp edges better than the existing guided filter. With the observation, it is applied for tone mapping of high dynamic range images and fusion of differently exposed images. Experimental results show that the resultant tone mapping and exposure fusion algorithms can prevent halo artifacts from appearing in the final images.
Fast intermode decision in H.264/AVC video coding

The new video coding standard, H.264/MPEG-4 AVC, uses variable block sizes ranging from 4/spl times/4 to 16/spl times/16 in interframe coding. This new feature has achieved significant coding gain compared to coding a macroblock (MB) using fixed block size. However, this feature results in extremely high computational complexity when brute force rate distortion optimization (RDO) algorithm is used. This paper proposes a fast intermode decision algorithm to decide the best mode in intercoding. It makes use of the spatial homogeneity and the temporal stationarity characteristics of video objects. Specifically, spatial homogeneity of a MB is decided based on the MB's edge intensity, and temporal stationarity is decided by the difference of the current MB and it colocated counterpart in the reference frame. Based on the homogeneity and stationarity of the video objects, only a small number of intermodes are selected in the RDO process. The experimental results show that the fast intermode decision algorithm is able to reduce on the average 30% encoding time, with a negligible peak signal-to-noise ratio loss of 0.03 dB or, equivalently, a bit rate increment of 0.6%.
Fast movement detection for high dynamic range imaging

When a high dynamic range image is synthesized by using a set of differently exposed low dynamic range (LDR) images, it is important to detect moving objects so as to remove ghosting from the final HDR image. A pixel level movement detection scheme was recently proposed in . It included a pixel level similarity index for differently exposed LDR images, an adaptive threshold for the classification of pixels and an approach that utilizes intensity mapping (IMF) function for patching invalid regions. In this paper, we first propose a new adaptive threshold and a new patching approach to improve the scheme in . Then, a sub-sampling method is introduced to simplify the improved movement detection scheme. Experimental results show that the improved movement detection scheme indeed outperforms the scheme in . In addition, the speed is significantly improved by the proposed fast movement detection scheme.
Implicit Bit Allocation for Combined Coarse Granular Scalability and Spatial Scalability

A new type of implicit bit allocation (IBA) is studied for the combined coarse granular scalability (CGS) and spatial scalability. Given a region which is a quality level at a spatial resolution and an input of weighting factor in the region that is determined by customers' interest, the IBA is formulated as a multiple objective optimization problem. The IBA exhibits a distinguished feature that allows bits allocation to each region being fixed and only tradeoff between motion information and residual information in each region can be properly set such that coding efficiency of each region is guaranteed in order according to the weighting factor. Due to the nonlinearity of the combined CGS and spatial scalability, this optimization problem is very complex. In this paper, a simple solution to the optimization problem is proposed by using the conventional approach of "Divide and Conquer" or "Hierarchy." Two cross-layer motion estimation/motion compensation (ME/MC) schemes that were introduced for the CGS and spatial scalability are further adopted to support the solution. It is shown that a combined CGS and spatial scalability scheme together with adaptability to customer composition allows the solution to achieve a customer oriented scalable tradeoff (COST)
Liquid Tunable Grating Using On-chip Microfluidic Control System

A liquid tunable grating in a micro-optical fluidic system (MOFS) chip is demonstrated in this paper. The on-chip grating is realized through a microfluidic control system comprising of multilayer laminar flow. The liquid grating provides tuning possibility through the modulation of refractive index (RI) and grating pitch. The liquid tunable grating can be employed for accurate measurement of refractive index of biological and chemical fluids (e.g. Phosphate Buffered Saline (PBS)) and therefore has enormous potential applications in Lab-on-a-chip based bioanalysis.
Noise reduction for differently exposed images

For scenes under low lighting condition, cameras are usually set to a high sensitivity (ISO) mode to reduce motion blur at the cost of increased image noise. When multiple differently exposed images are used to generate a high dynamic range (HDR) image, the high ISO noise from each low dynamic range (LDR) image can be further amplified by the HDR synthesis algorithm which would result in severely degradation of visual quality. This paper proposes an intensity mapping function based noise reduction method for differently exposed images with high ISO noise. The proposed method does not require any knowledge on either camera response functions or exposure times. In addition, the method is simple yet effective for noise removal from the LDR images without introducing any blurring or other artifacts.
Content Adaptive Image Detail Enhancement

Detail enhancement is required by many problems in the fields of image processing and computational photography. Existing detail enhancement algorithms first decompose a source image into a base layer and a detail layer via an edge-preserving smoothing algorithm, and then amplify the detail layer to produce a detail-enhanced image. In this letter, we propose a new L0 norm based detail enhancement algorithm which generates the detail-enhanced image directly. The proposed algorithm preserves sharp edges better than an existing L0 norm based algorithm. Experimental results show that the proposed algorithm reduces color distortion in the detail-enhanced image, especially around sharp edges.
Relative differentiated delay service: time varying deficit round robin

This paper proposes a novel algorithm based on the existing deficit round robin (DRR) to provide relative differentiated delay service such that it is possible to support real time applications over the Internet. In our algorithm, a method based on the fluid-flow traffic model is first presented to compute the service rate for each priority traffic flow. The sending quantum that is proportional to the service rate is then determined, and a time varying deficit round-robin algorithm is provided to send the packets to the next router. Our algorithm only requires minor changes to the infrastructure of the current Internet. Thus, it is a very possible choice to upgrade the best effort service of the current Internet.