nils-herrmann/clean_scirepeval huggingface dataset:
Subexpression sharing in filters using canonic signed digit multipliers

A common way of implementing constant multiplication is by a series of shift and add operations. As is well known, if the multiplier is represented in Canonical Signed Digit (CSD) form, then the number of additions (or subtractions) used will be a minimum. This paper examines methods for optimizing the design of CSD multipliers, and in particular the gains that can be made by sharing subexpressions. In the case where several multipliers are present in a network of operators, for instance in an FIR filter, the savings achieved by identifying common subexpressions can be as much as 50% of the total number of operators. The asymptotic frequency of the most common subexpression is analyzed mathematically, and it is shown that sharing the two most common subexpressions can be expected to lead to a 33% saving of the number of additions.
Mode-dependent intra frame interpolation for H.264/AVC compressed video

In this paper, a mode-dependent intra frame interpolation method is proposed for H.264/AVC compressed video. The intra prediction mode information is taken into account in the interpolation filter design. For each intra prediction mode, an optimal Wiener filter is trained based on the representative video sequences. Therefore the trained filter is adaptive to the intra prediction mode. Furthermore, the quantization parameter is also explored as context information for filter selection. Extensive experiments demonstrate that the proposed method achieves better performance than the traditional methods such as Bicubic, Bilinear, LAZA and NEDI, while keeping low computational complexity.
Wyner–Ziv-Based Multiview Video Coding

Utilizing video correlations among views would definitely improve multiview video compression in terms of coding efficiency, which usually requests an expensive system to collect videos from different cameras and jointly compress them. Thanks to recent developments on distributed video coding, this paper proposes a new multiview video coding scheme based on Wyner-Ziv (WZ) coding technique, in which the complicated temporal and interview correlation exploration process is shifted from the encoder side to the decoder side so that broadband raw data traffic and high intensive computation for jointly encoding can be avoided. At the encoder side, a wavelet-based WZ scheme is proposed to compress video of every camera. Furthermore, in order to better utilize correlation in wavelet domain, all coefficients are organized as that done in SPIHT bit plane by bit plane. At the decoder side, a more flexible prediction technique that can jointly utilize temporal and view correlations is proposed to generate side information. Finally, experimental results show the proposed scheme significantly outperforms the conventional intra-frame coding for better random access and is even close to the inter-frame coding for better efficiency. Furthermore, compressed data is much robust when it is transmitted over an error-prone channel.
Two-pass reconstruction in distributed video coding

In this paper, we propose a novel two-pass reconstruction algorithm for the Wyner-Ziv (WZ) frames in distributed video coding (DVC), in which the traditional reconstructed WZ frame is utilized to perform motion estimation to obtain a more accurate motion field. During the motion estimation, the block, as well as its neighboring pixels are concerned. An overlapped block motion compensation is subsequently performed with the help of the motion field, consequently, an enhanced prediction for the WZ frame can be obtained, based on which an improved reconstruction can be achieved. Simulation results show that both the objective and subjective quality of WZ frames can be improved significantly.
Directional intra frame interpolation for HEVC compressed video

Image interpolation is one of the most elementary imaging research topics. A number of image interpolation methods have been developed and tested on uncompressed images in the literature. However, a lot of videos have already been stored or have to be transmitted in compressed format due to the storage limitation or the bandwidth limitation. The existed image interpolation methods may not be efficient when directly applied to compressed images or videos. Inspired by the success of the intra prediction in HEVC and the edge-directed image interpolation methods, a directional intra frame interpolation for HEVC compressed video is proposed. The main idea is to use the directional prediction information in compressed low-resolution video bitstreams to estimate the associated high-resolution video. For intra frames, the prediction direction information is taken into account as context in the directional interpolation. When a pixel is decompressed with a small prediction residual, the interpolation is performed along its block direction. The interpolation weight for each block direction is off-line trained by the Wiener filter based on the representative video sequences. For each pixel with a large prediction residual, a piecewise autoregressive model is used as a regularization term into the interpolation function. Extensive experiments demonstrate that the proposed method achieves better performance than the traditional methods such as Bicubic, KR, LAZA, NEDI and SAI.
Saliency Detection: A Self-Adaption Sparse Representation Approach

Saliency detection is essential to visual attention modelling and various computer vision tasks. Representation and measurement are two important issues for saliency models. Good representation and reasonable measurement are both critical issues in modelling visual saliency mechanism. For every input image, we obtain a self-adaptive dictionary that describes the image content effectively and image prior that forces sparsity in every location in the image using the K-SVD algorithm. For saliency measurement, background firing rate (BFR) is defined for each sparse features and it is followed by feature activation rate (FAR) computation to measure the bottom-up visual saliency.
Arbitrary-sized motion detection in screen video coding

In real-time screen remoting system, frame rate is one of essential factors that affect user experience. Therefore, how to compress diversity of screen contents fast and efficiently is a key issue. Existing video codecs such as H.264 are always used in such a system for screen compression. However, arbitrary-sized regions with large motion always exist in typical screen content videos, which lead to a lower encoding speed and higher bit-rate, thereby decrease the frame rate. This paper proposes an efficient motion detection algorithm, which is fast and efficient for large motion regions. In specific, a region-based motion detection is used to find motion vectors instead of traditional block based motion estimation. The motion vectors are then utilized by H.264 encoder for normal motion compensated prediction. Experimental results show that the proposed algorithm can reduce both encoding time and bit-rate significantly.
Improved intra transform skip mode in HEVC

Recent years we have witnessed a striking rise of the compound video, which is generated by computers. The High Efficiency Video Coding standard (HEVC) adopts the intra transform skip mode to improve the coding efficiency of compound video. In this paper, we improve the intra transform skip mode from two aspects. Firstly, we study the statistical character of the intra prediction residual and propose a separate coefficient scan order for the intra transform skip mode. Secondly, we analyze the probability of the intra transform skip mode for different coding unit (CU) sizes and prediction unit (PU) sizes, and then propose one limitation to reduce the applied range of intra transform skip mode. The experimental results show that the first method can achieve 1.6%, 1.1%, and 0.7% luma coding gains on average in terms of BD-rate for class F under HE10-AI, HE10-RA, and HE10-LB conditions respectively; the second method can bring 9% encoder run time reduction with almost the same BD-rate changes. The proposed methods have been partially adopted into HEVC standard.
Simplified AMVP for High Efficiency Video Coding

In High Efficiency Video Coding (HEVC), advanced motion vector prediction (AMVP) is adopted to predict current motion vector by utilizing a competition-based scheme from a given candidate set, which include both the spatial and temporal motion vectors. In order to enhance the practicability of the AMVP, a simplified AMVP is proposed. Firstly, by analyzing the importance of the spatial and temporal candidates, we reduce the number of the candidates involved in the competition set and simplify the redundancy checking process, which will decrease the complexity of the decoder as well as improve the robustness of the decoder. Secondly, we simplify the zero motion adding process which will occur only when the number of existing candidates is less than the predefined number. Experimental results show that the proposed scheme provides no loss in random access and low delay conditions. These two simplifications have been proposed and adopted into the HEVC standard.
Sparsity-Based Image Error Concealment via Adaptive Dual Dictionary Learning and Regularization.

In this paper, we propose a novel sparsity-based image error concealment (EC) algorithm through adaptive dual dictionary learning and regularization. We define two feature spaces: the observed space and the latent space, corresponding to the available regions and the missing regions of image under test, respectively. We learn adaptive and complete dictionaries individually for each space, where the training data are collected via an adaptive template matching mechanism. Based on the piecewise stationarity of natural images, a local correlation model is learned to bridge the sparse representations of the aforementioned dual spaces, allowing us to transfer the knowledge of the available regions to the missing regions for EC purpose. Eventually, the EC task is formulated as a unified optimization problem, where the sparsity of both spaces and the learned correlation model are incorporated. Experimental results show that the proposed method outperforms the state-of-the-art techniques in terms of both objective and perceptual metrics.
High-quality image interpolation via local autoregressive and nonlocal 3-D sparse regularization

In this paper, we propose a novel image interpolation algorithm, which is formulated via combining both the local autoregressive (AR) model and the nonlocal adaptive 3-D sparse model as regularized constraints under the regularization framework. Estimating the high-resolution image by the local AR regularization is different from these conventional AR models, which weighted calculates the interpolation coefficients without considering the rough structural similarity between the low-resolution (LR) and high-resolution (HR) images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize the interpolated HR image, which provides a way to modify these pixels with the problem of numerical stability caused by AR model. In addition, a new Split-Bregman based iterative algorithm is developed to solve the above optimization problem iteratively. Experiment results demonstrate that the proposed algorithm achieves significant performance improvements over the traditional algorithms in terms of both objective quality and visual perception.
Context-based 2D-VLC for video coding

It is well observed that usually the run-length of successive zero coefficients becomes longer and the magnitude of non-zero coefficients gets smaller while the DCT subband frequency increases. Therefore, the probability distributions of level/run combinations should be different at different DCT positions. Based on this observation, an efficient context based 2D-VLC entropy coder, especially designed for 8/spl times/8 DCT, is proposed to exploit this context characteristic. The key element of the proposed coder is to design multiple 2D-VLC tables and each can be used context-adaptively to match different level/run combination probability distributions better. Another key element is the usage of exponential-Golomb codes, which keep the multiple tables with low memory requirement. In terms of coding efficiency, the experimental results show that the proposed method can gain up to 0.23 dB when compared to the one-table-for-one-block coding method.
Quality of experience assessment for stereoscopic images

Stereoscopic image quality assessment has been widely studied in last decades; however, the research on 3D quality of experience (QoE) is proposed recently. As a part of human stereo perception, 3D QoE plays an important role to stereoscopic image quality assessment. In this paper, an objective metric is proposed based on the hypothesis that binocular vision system is sensitive to the structure of low-level features and its discrepancy between the two view images of a stereoscopic image pair. Specifically, the correlation between the left and right views of a stereoscopic image pair could reflect the QoE. To represent the structure of low-level features, in each view of the stereoscopic image pair, the phase congruency (PC) and the saliency map are employed as the primary and secondary features to compose a feature map. To compute the correlation between the two views, a local matching function is suggested to weight the discrepancy between the two feature maps and generate a local quality. Then these local quality values are combined to derive a single quality score. The proposed metric is evaluated on one public subjective assessment database. The experimental results indicate that our metric exhibits good performance.
Compressed Vision Information Restoration Based on Cloud Prior and Local Prior

In wireless communication, compressed vision information may suffer from kinds of degradation, which dramatically influences the final visual quality. In this paper, a compressed vision information restoration method is proposed based on two explored vision priors: 1) the cloud prior and 2) the local prior. The cloud prior can be obtained from the nature images set in the cloud, and fields of experts is used to formulate the statistical character of the nature image contents as a high order Markov random field. The local prior is achieved from the degraded image itself, and K-SVD is adopted to model the sparse and redundant representation characters of nature images. These priors are effectively comprised in the proposed vision information restoration method. The relation between the quantization parameter and the optimal configuration of the prior models is further analyzed. In addition, an enhanced quantization constrained projection algorithm is proposed to refine the high frequency components. We extend this paper to compressed video restoration for H.264/AVC and the experiment results demonstrate that the proposed scheme can reproduce higher quality images compared with conventional H.264/AVC.
Reference image based method of region of interest enhancement for haze image

Different from general algorithms of haze removal and low lighting image enhancement, which only use the information of image to process, this paper adds a reference image to get more information for the algorithm and focuses on enhancing region of interest of an image based on the reference one. With the reference image, the haze one can be divided into Region of Interest (RoI) and Region of no Interest (non-RoI). Furthermore, the reference image can provide more useful information for computing the transmission map and atmospheric light. For the non-RoI region, a more robust transmission map and minimizing reconstruction error cost function based method to estimate atmospheric light has been proposed. Because the atmospheric light is a global variable, the optimized one is also suitable for the RoI region. With the global optimized atmospheric light, an optimized transmission map can be got for the RoI region. The RoI region can be enhanced via the optimal transmission map and atmosphere light. Theoretical analysis gives eloquent proof proving that the proposed method is definitely better than the traditional dark-channel-prior-based methods due to our better transmission map and atmosphere light. Extensive experiments also show the expected results.
A new model for image de-noising of partial differential equation

An important feature of scale space is controlled by a partial differential equation(PDE), thus we derive a new method for image de-noising by applying properties of scale space. We give the concrete derivation of the new model and make corresponding experiments. The experimental results indicate that the new method can recover the image well comparing with other classic de-noising methods. Additionally, the new method is also effective in salt and pepper noise. So the new method not only has good effect on image de-noising, but also has superiority in theory.
Low-Complexity Encoder Framework for Window-Level Rate Control Optimization

There exists a tradeoff between visual quality smoothness and buffer constraint in rate control. Thus, a window model and a window-level rate control algorithm were proposed to handle such a tradeoff recently. However, the extra computational complexity and encoding delay were introduced due to a pre-analysis process in the window-level rate control. In this paper, the window model is first improved for a more convenient usage in practical encodings. Second, a new low-complexity encoder framework is proposed based on an open-loop motion estimation (ME). Third, a new memory-efficient algorithm is proposed to eliminate the drift error of open-loop ME. Fourth, the window-level rate control algorithm is improved to adapt to the proposed encoder framework. The experimental results show that the visual quality smoothness is much improved by the proposed algorithm against the traditional one of H.264/AVC. In addition, the improvement of the window-level rate control is better than the previous one in terms of both bit control accuracy and visual quality smoothness. Besides, the proposed encoder could be used to any other rate control algorithms with pre-analysis to reduce computational complexity.
Spatial-temporal recovery for hierarchical frame based video compressed sensing

In this paper, the hierarchical frame based video compressed sensing (CS) framework is proposed, which outperforms the traditional framework through the better exploitation of frames correlation with reference frames, the unequal sample subrates setting among frames in different layers and the reduction of the error propagation. By considering the spatial and temporal correlations of the video sequence, a spatial-temporal sparse representation based recovery is proposed for this framework. The similar blocks in both the current frame and these recovered reference frames are composed as a spatial-temporal group, which is defined as the unit of the sparse representation. By exploiting the low dimensional subspace description of each group, the video CS recovery is converted as a low-rank matrix approximation problem, which can be solved by exploiting the hard thresholding and the gradient descent. Experimental results show that the proposed method achieves better performance against both the state-of-art still-image CS recovery algorithms and the existing residual domain based video CS reconstruction approaches.
Gradient based fast mode decision algorithm for intra prediction in HEVC

High Efficiency Video Coding (HEVC) is an ongoing video compression standard and a successor to H.264/AVC. It aims to provide significantly improved compression performance as compared to all existing video coding standards. In the intra prediction of HEVC, this is being achieved by providing up to 35 intra modes with larger coding unit. The optimal mode is selected by a rough mode decision (RMD) process from all of the available modes first, and then through the rate-distortion optimization (RDO) process for the final decision. Because the fact that every coding unit with different sizes is traversed in both procedures makes it very time-consuming, a gradient based fast mode decision algorithm is proposed in this paper to reduce the computational complexity of HEVC. Prior to intra prediction, gradient directions are calculated and a gradient-mode histogram is generated for each coding unit. Based on the distribution of the histogram, only a small part of the candidate modes are chosen for the RMD and the RDO processes. As compared to the default encoding scheme in HEVC test model HM 4.0, experimental results show that the fast intra mode decision scheme provides almost 20% time savings in all intra low complexity cases on average with negligible loss of coding efficiency.
Fuzzy Synthetic Assessment of Air Traffic Controllers Situational Awareness Based on Short-term Memory Measurement

This paper presents an approach for evaluating air traffic controllers situational awareness (SA). In the approach, the relationship between situational awareness and short-term memory is analyzed, based on which, the factors for fuzzy synthetic assessment of controllers situational awareness are determined. And then, weighting coefficient is given for each factor by applying Analytic Hierarchy Process (AHP), and membership function for each factor is obtained in accordance with air traffic control rules and expertise, upon that the fuzzy assessment model is built. At last, an example is presented to demonstrate the proposed approach is useful and effective in selection, training and work load evaluation for air traffic controllers.
Local adaptive learning and fusion for side information interpolation in distributed video coding

Motivated by theoretical analysis of the curve fitting problem based on equivalent kernel, in this paper we propose a local adaptive learning and fusion model for side information interpolation in distributed video coding. In the proposed model, each pixel in the interpolated frame is approximated as the linear combination of samples within a local spatio-temporal window using kernel parameters as weight. The size of training window can be adaptive to the motion characteristic of video, from samples in which the kernel parameters can be locally learned. In order to further improve the quality of interpolated frames, we introduce a belief-projection based fusion strategy with adaptive weights for multiple interpolated results which are with the same time index. Experimental results demonstrate that the proposed learning and fusion model is effective in performance for side information interpolation in distributed video coding.
Diagnosis of bearing incipient faults using fuzzy logic based methodology

With the increasing demand of stable running condition of mechanical products and low maintenance costs of machinery devices, fault detection and diagnosis attracted considerable interests, early fault diagnosis is desirable for accuracy and appropriate assessment, due to the fact that it could provide fault information as soon as possible and prevent fast deteriorating of the failure. In this paper, a methodology is presented for incipient defect diagnosis of deep grove ball bearings through energy spectrum extracted by wavelet packet transform, differential defect features selected based on principal component analysis and feature fusion by fuzzy logic algorithm for defect diagnosis. The application results demonstrate the accuracy and effectiveness of the proposed method.