nils-herrmann/clean_scirepeval huggingface dataset:
Structure-Preserving Guided Retinal Image Filtering and Its Application for Optic Disk Analysis

Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration, and diabetic retinopathy. With the development of computer science, computer aided diagnosis has been developed to process and analyze the retinal images automatically. One of the challenges in the analysis is that the quality of the retinal image is often degraded. For example, a cataract in human lens will attenuate the retinal image, just as a cloudy camera lens which reduces the quality of a photograph. It often obscures the details in the retinal images and posts challenges in retinal image processing and analyzing tasks. In this paper, we approximate the degradation of the retinal images as a combination of human-lens attenuation and scattering. A novel structure-preserving guided retinal image filtering (SGRIF) is then proposed to restore images based on the attenuation and scattering model. The proposed SGRIF consists of a step of global structure transferring and a step of global edge-preserving smoothing. Our results show that the proposed SGRIF method is able to improve the contrast of retinal images, measured by histogram flatness measure, histogram spread, and variability of local luminosity. In addition, we further explored the benefits of SGRIF for subsequent retinal image processing and analyzing tasks. In the two applications of deep learning-based optic cup segmentation and sparse learning-based cup-to-disk ratio (CDR) computation, our results show that we are able to achieve more accurate optic cup segmentation and CDR measurements from images processed by SGRIF.
Early detection of all-zero block in H.264 with new rate-quantization models

This paper presents a new algorithm for detecting all-zero DCT coefficient blocks (AZB) prior to DCT and quantization for H.264. The early detection criterion is derived from a new rate-quantization model which is established by considering the unique features of quantization process in H.264. The proposed algorithm aims to eliminate the redundant computations in AZB and consequently speed up the encoding process of H.264 video codec. Simulation results show that the proposed algorithm achieves a high detection ratio up to 95.88% while maintain a very low false detection ratio. The results confirm that a more effective AZB detection for H.264 can be achieved by taking the unique feature of quantization into consideration.
Incentive Based Equilibrium and Applications

In this paper, we shall provide relevant formulations and examples to show that Incentive based equilibrium is desirable in many games applications. This is more so with the development and deployment of Internet even though these games may be performed only once. We shall also propose a novel scheme to properly allocate the enlarged total "pie", which is made possible by the incentive based equilibrium, among the players. Moreover, the proposal allocation scheme is extended to the Pareto optimality. Our Pareto optimal allocation scheme has an attractive feature, i.e. each player has an incentive to enlarge his/her capacity because he/she can be better off.
Bubble-based droplet mixers microfluidic systems

A method for on-demand droplet coalescence and mixing is realized using laser-induced cavitation bubbles. Water droplets are formed using a microchannel with the coupled T-junction structure. Laser-induced cavitation bubbles are generated inside water droplets to deform the droplets, which break up the continuous phase between the droplets and trigger the fusion process. The diffusion dominated mixing process after the fusion is observed. For two 0.11 pl droplets, they have been mixed in 800 µs to a high mixing quality.
Robust stability of quasi-periodic hybrid dynamic uncertain systems

Considers the robust stability of quasi-periodic hybrid dynamic systems (HDSs) with polytopic uncertainties. The quasi-periodic HDSs has infinite switchings, but the switching sequence forms a cycle and the cycle is repeated. We derive the stability conditions for quasi-periodic HDS with uncertainties in continuous-variable dynamic systems, and with variations in both the "switching"-conditional set and the reset map by analyzing the behavior of the system along the cycle. The results require the Lyapunov function to be bounded by a continuous function along each continuous-variable dynamic system, and is nonincreasing along a subsequence of the "switchings." They do not require the Lyapunov function to be nonincreasing along the whole sequence of the switchings.
Degressive error protection algorithm for MPEG-4 FGS video streaming

This paper presents a novel degressive error protection (DEP) algorithm adaptive to time varying packet loss rate for robustly transporting MPEG-4 fine granularity scalability (FGS) bit-stream over the packet erasure networks. By exploiting the embedded nature of FGS enhancement-layer bit-stream, a rate-distortion (R-D) optimization framework is developed to optimally partition the FGS enhancement-layer bit-stream into blocks and then to apply DEP to blocks by forward error correction (FEC). Experimental results show that this algorithm achieves graceful degradation of video quality in a wide range of packet loss rates.
Gradient Domain Guided Image Filtering

Guided image filter (GIF) is a well-known local filter for its edge-preserving property and low computational complexity. Unfortunately, the GIF may suffer from halo artifacts, because the local linear model used in the GIF cannot represent the image well near some edges. In this paper, a gradient domain GIF is proposed by incorporating an explicit first-order edge-aware constraint. The edge-aware constraint makes edges be preserved better. To illustrate the efficiency of the proposed filter, the proposed gradient domain GIF is applied for single-image detail enhancement, tone mapping of high dynamic range images and image saliency detection. Both theoretical analysis and experimental results prove that the proposed gradient domain GIF can produce better resultant images, especially near the edges, where halos appear in the original GIF.
Single image brightening via exposure fusion

It is very challenging to capture images via cell phone cameras in low-lighting conditions due to possible motion blur, especially for a high dynamic range (HDR) scene. In this paper, a new single image brightening algorithm is introduced to support capturing an image with a small exposure time and a small ISO value for a low-lighting scene. There are negligible motion blur and over-exposed pixels in the captured image while both details in the darkest regions and the brightness of the image are reduced. The proposed algorithm is applied to brighten the under-exposed regions and to enhance details of the under-exposed regions with negligible increment on the brightness of the brightest areas. The proposed algorithm can also be adopted to brighten an image captured for an HDR scene at day time but with dark objects.
A study of MPEG-4 rate control scheme and its improvements

This paper discusses some practical issues in implementing the MPEG-4 Q2 rate-control scheme, and proposes a number of ways to improve it. The improved algorithm has the following main features: (1) the bits allocated to each P-frame or B-frame are in proportion to its distance from the end this GOP. i.e., more bits are allocated to the frames that are nearer to their reference I-frame; (2) the target buffer level is a function of the frame position in the GOP, so that it will be achieved gracefully at the end of a GOP; and (3) the quantization value of an I-frame is decided based on its spatial complexity. Experimental results show that the improved rate-control scheme has significantly reduced the occurrence of frame skipping, increased the average PSNR by up to 0.6 dB, and improved the perceptual quality of the reconstructed video.
A unified architecture for real-time video-coding systems

This paper presents a unified architecture for a live video over the Internet with emphasis on solving some challenging problems such as network bandwidth adaptation for rate and congestion, loss packet recovery, joint source and channel coding, and packetization. In our architecture, a time-varying bit rate for the source coding and time-varying ratios for the channel coding are simultaneously computed by a new congestion-control protocol. An adaptive rate-control scheme is then proposed to calculate quantization parameters and to determine the number of skipping frames corresponding to the bit rate. An adaptive unequal error-control scheme is also provided to protect the bitstream. Furthermore, a simple and MPEG-4 standard compatible algorithm is designed to packetize generated bitstream at the SyncLayer by using the existing resynchronization marker approach. With the proposed architecture, the coding efficiency and the robustness of the whole system are improved greatly.
On residual quad-tree coding in HEVC

In the current working draft of HEVC, residual quad-tree (RQT) coding is used to encode prediction residuals in both Intra and Inter coding units (CU). However, the rationale for using RQT as a coding tool is different in the two cases. For Intra prediction units, RQT provides an efficient syntax for coding a number of sub-blocks with the same intra prediction mode. For Inter CUs, RQT adapts to the spatial-frequency variations of the CU, using as large a transform size as possible while catering to local variations in residual statistics. While providing coding gains, effective use of RQT currently requires an exhaustive search of all possible combinations of transform sizes within a block. In this paper, we exploit our insights to develop two fast RQT algorithms, each designed to meet the needs of Intra and Inter prediction residual coding.
Residual DPCM for lossless coding in HEVC

Incorporating sample-based prediction during lossless coding can significantly improve coding performance. However, its use within a codec designed for lossy coding requires a modification of the available prediction scheme. When implementing the codec, two different prediction processes will have to be implemented. This paper describes a lossless coding scheme that delays the sample-based prediction till the residue coding stage of the codec and carries out prediction in the residual domain. In this way, the prediction scheme of the lossy coder can be retained while realizing the coding gains associated with sample-based prediction. The proposed scheme improves lossless intra coding performance in HEVC Main Profile by an average of 6.5%.
An OWE-based Algorithm for Line Scratches Restoration in Old Movies

Line scratch is the primary artifact in old films. In this paper a new algorithm for line scratch detection and removal is proposed. First, we establish an effective model to represent line scratches in the domain of over-complete wavelet expansion (OWE), which offers more precise position description for scratches than traditional downsampled wavelet transform. We then use it to detect and locate line scratches accurately. After that, an adaptive restoration method is adopted to remove line scratches by replacing the wavelet coefficients in line scratches of each scale with new interpolated wavelet coefficients of corresponding width computed from the line scratch model. Experiments show that the proposed method can detect more line artifacts with less false detection and remove the line scratches effectively as compared with the classic algorithm.
A similarity index in the complex wavelet domain

It is known that in the field of image synthesis, multi-frame image processing can produce better results than just single-image enhancement techniques. Such a technique uses similarity assessment to select the images for synthesis. In this paper, we propose a new image similarity assessment index by using complex wavelet. It is found that the proposed index is robust to small rotations and translations as well as large intensity and contrast changes. Therefore, it can be widely applied in the field of image synthesis, especially high dynamic range imaging.
Collaborative image processing algorithm for detail refinement and enhancement via multi-light images

In this paper, we introduce a new collaborative image processing algorithm to enhance contours and surface details of a scene through combination of multi-light images, which capture the same scene with fixed view point but different lighting conditions. Firstly, a detail layer that contains all contents of the multi-light images is generated through a gradient domain method and a quadratic filter. A new shadow detection algorithm is introduced to remove the artifacts from the detail layer. Subsequently, a base layer is constructed by using one input image. To further increase the visibility of details in dark areas, a simple tool is presented to brighten all dark areas of the base layer. Finally, the detail layer is multiplied to the base layer to synthesize the enhanced image that contains the desirable details. This enhanced image can present an elaborate description of the real scene. The proposed scheme also gives some interactivities that allow users to easily change the appearance of the enhanced image according to their preferences.
Instant Color Matching for Mobile Panorama Imaging

This paper presents an efficient color matching approach to address the photometric inconsistency problem that commonly exists in panoramic images. Color correction, as the first step, is to adjust the color and luminance of source images so that the differences between adjacent images can be minimized. Color blending is used after the color correction to further smooth the color transition between adjacent images. With the first image being selected as a basis image, the proposed approach can start the color matching and stitching process once the second image is captured. The proposed approach is simple and is very suitable for panoramic imaging on mobile devices. Experimental results demonstrate that the color transitions between neighboring images are smooth without visible seams and the color tone of the final image is kept as close as the basis image.
Block INTER mode decision for fast encoding of H.264

The paper presents a fast block INTER mode decision algorithm to improve significantly the time efficiency of the encoder in H.264. It makes use of the spatial homogeneity of a video object's textures and the temporal stationarity characteristics inherent in video sequences. Specifically, the homogeneity decision of a block is based on edge information, and MB differencing is used to judge whether the MB is time-stationary. Based on the above analysis, only parts of inter prediction modes are chosen for RDO (rate distortion optimization) calculation. Experimental results show that the new scheme is able to achieve a reduction of 30% encoding time on average, with a negligible average PSNR loss of only 0.03 dB and a mere 0.6% bit rate increase compared with the original H.264 reference software.
Noise reduced high dynamic range tone mapping using information content weights

In this paper, we propose a noise reduced tone mapping method based on information content weights, where the perceptually unimportant pixels are smoothed during the decomposition in two steps. First, a saliency-based information content weight is introduced to give high fidelity to the data term based on the ratio of the local pixel power and the overall noise power in the base layer decomposition. Then, the detail layer is subtracted using the mutual information-based information content weight from the original image luminance and the clean base layer. Experiments show the effectiveness of the proposed method in the improvements of both signal-to-noise ratio and visual quality.
Exploiting deep convolutional network and patch-level CRFs for indoor semantic segmentation

Recent advances in semantic segmentation have mostly been achieved by utilizing deep convolutional neural networks (CNNs). In this paper, a novel indoor semantic segmentation method is proposed by integrating CNN and patch-level Conditional random fields (CRF). Multi-scale images are sent to CNN to capture objects of different sizes as well as to extract features at multiple scales. Patch-level CRF is constructed to further refine the object boundaries localization accuracy. Extensive experiments on the publicly available NYU V2 database demonstrate that the proposed method could obtain state-of-the-art accuracy in terms of four evaluation metrics.
A novel prediction scheme for lossless compression of audio waveform

For compression of audio waveform, prediction is one of the important key components. We propose a novel multi-stage adaptive linear predictor (MSALP) for the prediction of high fidelity audio waveform. The MSALP achieves higher prediction gain compared with conventional linear predictor (LP). Besides, the MSALP uses less number of coefficients. The MSALP is embedded into a lossless audio waveform compression system. As a result, the system compression ratio is obviously improved. By selecting known lossless audio waveform compression algorithms, some comparison results are presented.
MRF: a framework for source and destination based bandwidth differentiation service

In this paper, we shall generalize the concepts of fairness, TCP-friendliness and TCP-compatibility such that more source adaptation schemes can be designed to support diverse applications over the Internet. A simple but efficient framework, in the form of a monotonic response function (MRF), is proposed for the analysis and the design of memoryless window-based source adaptation protocols by using these concepts. We first derive a necessary and sufficient condition for step-wise convergence to the weighted fairness. It is then used to construct increase-decrease policies. The requirements of our increase-decrease policy are less conservative than those of the CYRF (Choose Your Response Function) that was proposed in . Our MRF is suitable for transmission control protocol (TCP) and user datagram protocol (UDP), and can be used to design TCP-friendly and multimedia-friendly source adaptation schemes. Meanwhile, our MRF can be applied to provide bandwidth differentiation service without any change to the router of the existing Internet.
Rate Control for Spatial/CGS Scalable Extension of H.264/AVC

This paper presents a rate control scheme for H.264/AVC spatial/ coarse-gain-SNR (CGS) scalable extension. A switched model is proposed to predict mean absolute difference (MAD) either from the previous frame of the current layer or from the current frame of the previous layer. This way, abrupt MAD fluctuations could be predicted properly in the enhancement layer. Moreover, a sum bits R-Q model is formulated to describe the relationship between the total amount of bits for texture and non-texture information and quantization parameter (QP) so as to reduce the negative effect caused by inaccurate non-texture bits estimation of the previous rate control schemes. With the relation between PSNR and QP value of H.264/AVC, our proposed sum bits R-Q model could further optimize the QP calculation at the MB level. With our algorithm, the rate control for all the coarse-grain-scalability (CGS) and spatial enhancement layer could be realized, and the encoder could achieve fixed bitrate encoding for each scalable layer. Our method encodes the sequence only once and the buffer is well controlled to prevent it from overflowing and underflowing.
Adaptive frame layer rate control for H.264

This paper proposes an adaptive frame layer rate control scheme for H.264 by introducing a linear model to predict the mean absolute difference (MAD) of current frame by that of previous one. The target bit rate for each frame is computed by adopting a fluid flow traffic model and linear tracking theory. The corresponding quantization parameter is computed by using a quadratic rate-distortion model. The rate distortion optimization (RDO) is then performed for all macroblocks (MBs) in the current frame by the quantization parameter. Both constant bit rate (CBR) and variable bit rate (VBR) cases are studied. The average PSNR is improved up to 0.75 dB compared to an encoder with fixed quantization parameter.
Fast Mode Decision for Coarse Grain SNR Scalable Video Coding

Scalable video coding (SVC) is an on-going standard and the current working draft (WD) is an extension of H.264/AVC. In the WD, exhaustive search technique is employed to select the best coding mode for each macroblock (MB). This technique achieves highest possible coding efficiency, but it results in higher computational complexity. To overcome this, we propose a novel fast mode decision scheme for coarse grain SNR scalability (CGS) in SVC. In this scheme, the mode distribution relationships between the base layer and enhancement layers are employed to reduce the candidate mode set at enhancement layers. The experimental results show that the proposed scheme provides significant reduction in computational complexity with negligible coding loss
Content adaptive bilateral filtering

Bilateral filter is perhaps the most popular and simplest edge-preserving local filter. However, it may produce halos near some edges due to unwanted smoothing of the edges. In this paper, a content adaptive bilateral filter is proposed to overcome this problem. Both spatial similarity and range similarity parameters of the proposed bilateral filter are adaptive to the content of an image to be filtered rather than being fixed as in the existing bilateral filter. The proposed filter preserves edges and smoothes flat areas better than the existing bilateral filter. It is applied to design a local tone mapping algorithm for high dynamic range images as well as a noise reduction algorithm for low dynamic range color images. Experimental results show that the resultant tone mapping algorithm produces images with better visual quality and at the same time halo artifacts are avoided from appearing in the tone mapped image. The resultant noise reduction algorithm can also produce images with higher structural similarity index values and better visual quality. Noise in smooth regions are more reduced and edges are preserved better in the de-noised image.
Superpixel based patch match for differently exposed images with moving objects and camera movements

A challenging problem for high dynamic range (HDR) imaging is to reconstruct a ghosting-free HDR image for an HDR scene with moving objects. In this paper, a superpixel based patch match algorithm is proposed to synchronize differently exposed images with moving objects and camera movements according to a selected reference image. The concept of superpixel is adopted to divide the reference image into atomic regions, and a new bilateral weight is introduced for the computation of matching cost to reduce outliers. All the input images are synchronized by using the estimated optical flow. The synchronized images are further refined via a weighted guided image filter with the reference image as the guidance image. Experimental results show that the proposed algorithm can remove the scene variations in the presence of moving objects and camera movements. A ghost-free HDR image can then be reconstructed for differently exposed images with moving objects and camera movement by using an existing exposure fusion algorithm.
A novel SNR refinement scheme for scalable video coding

Cross SNR layer motion estimation/motion compensation (ME/MC) scheme is proposed in this paper for the SNR scalability of scalable video coding. Both the motion information and residual information are refined at the enhancement layers by our cross SNR layer ME/MC scheme. A good trade-off between motion information and residual information can be obtained at all bit rates. The coding efficiency is improved by up to more than 1 dB at high bit rate.