nils-herrmann/clean_scirepeval huggingface dataset:
Visual masking estimation based on structural uncertainty

A model of visual masking, which reveals the visible threshold of human perception, is useful in perceptual based image/video processing. The existing visual masking formulation, which mainly considers luminance contrast, cannot accurately estimate the visible threshold. Recent researches indicate that human perception is highly adaptive to extract orderly structures and is insensitive to disorderly structures. Therefore, we suggest that the structural characteristic is another determining factor for visual masking, and deduce a novel visual masking function based on structural uncertainty. Experimental results demonstrate that the proposed model is more consistent with human perception than the existing visual masking model.
Image Enhancement by Entropy Maximization and Quantization Resolution Upconversion

This article introduces a new contrast enhancement algorithm of tone-preserving entropy maximization. Its design objective is to present the maximal amount of information content in the enhanced image, or being optimal in an information theoretical sense, while preventing the loss of tone continuity. The resulting optimization problem can be graph theoretically modeled as the construction of the K-edges maximum-weight path, and it can be solved efficiently by dynamic programming. Moreover, the proposed algorithm is made more effective by being combined with a preprocess of image restoration that aims to correct quantization errors caused by the analog-to-digital conversion of image signals. Empirical evidences are provided to demonstrate the superior visual quality obtained by the new image enhancement algorithm.
Multiple description video coding against both erasure and bit errors by compressive sensing

We propose a novel multiple description video coding (MDVC) technique for robust video transmission via lossy networks of both packet erasure and bit errors. The new MDVC technique is designed to meet two objectives: ultra fine description granularity and low encoder complexity, which allow resource-deprived video transmitters (e.g., smart phones) to operate in time-varying adverse network conditions. These design goals are met by the signal acquisition and coding strategy of compressive sensing, and by locally adaptive sparse representation of video signals.
Distributed Compressive Sensing for Cloud-Based Wireless Image Transmission

We consider efficient image transmission via time-varying channels. To improve the performance, we propose a new distributed compressive sensing (CS) scheme that can leverage similar images in the cloud. It is featured by channel SNR and bandwidth scalability, high efficiency, and low encoding complexity. For each image, a compressed thumbnail is first transmitted after forward error correction (FEC) and modulation to retrieve similar images and generate a side information (SI) in the cloud. The residual image after subtracting the decompressed thumbnail is then coded and transmitted by CS through a very dense constellation without FEC. The linearly and ratelessly generated CS measurements make it capable of achieving both graceful quality degradation (GD) with the channel SNR and bandwidth scalability in a universal scheme. A mode decision and transform-domain power allocation are introduced for better bandwidth usage and protection against channel errors. At the decoder, a two-step CS decoding is performed to recover the residual signal, where both the local and nonlocal correlations within the image and that with the SI are exploited. Simulations on landmark images and an AWGN channel show that the received image quality gracefully increases with the channel SNR and bandwidth. Furthermore, it outperforms existing schemes both subjectively and objectively by up to 11 dB gains compared with the state-of-the-art transmission scheme with GD, i.e. SoftCast.
Trellis quantization for L∞-constrained compression with integer wavelets

Although integer wavelets have been successfully used in lossless signal compression, they have not been generalized to near-lossless (Linfin constrained) coding. This paper proposes a new technique of trellis quantization in the framework of lifting integer wavelet as a promising way of near-lossless signal compression. This new technique achieves continuous scalability of the wavelet code stream from highly lossy (low bit rates) to near-lossless (respecting a tight error bound on each decoded sample) reconstruction.
Low-Rank Tensor Approximation with Laplacian Scale Mixture Modeling for Multiframe Image Denoising

Patch-based low-rank models have shown effective in exploiting spatial redundancy of natural images especially for the application of image denoising. However, two-dimensional low-rank model can not fully exploit the spatio-temporal correlation in larger data sets such as multispectral images and 3D MRIs. In this work, we propose a novel low-rank tensor approximation framework with Laplacian Scale Mixture (LSM) modeling for multi-frame image denoising. First, similar 3D patches are grouped to form a tensor of d-order and high-order Singular Value Decomposition (HOSVD) is applied to the grouped tensor. Then the task of multiframe image denoising is formulated as a Maximum A Posterior (MAP) estimation problem with the LSM prior for tensor coefficients. Both unknown sparse coefficients and hidden LSM parameters can be efficiently estimated by the method of alternating optimization. Specifically, we have derived closed-form solutions for both subproblems. Experimental results on spectral and dynamic MRI images show that the proposed algorithm can better preserve the sharpness of important image structures and outperform several existing state-of-the-art multiframe denoising methods (e.g., BM4D and tensor dictionary learning).
Edge-based image coding at low bit-rate

We propose a novel edge-based low bit-rate image codec. Although being edge based, the codec does not explicitly code the edge geometry. To avoid spending high bit budget on edges, a background layer of the image is first coded and transmitted, from which the decoder estimates the trajectories of significant edges. The edge regions are then refined by a residual coding technique based on edge dilation and sequential scanning in the edge direction. Experimental results show that the new image coding technique outperforms existing ones in both objective and perceptual quality, particularly at low bit rates.
Observation quality guaranteed layout of camera networks via sparse representation

Nodes layout is a critical issue affecting the quality of service of camera networks. The layout aims at adopting the least number of cameras to cover the whole scene with expected observation quality. This is generally formulated as a non-convex optimization problem which is hard to be solved in polynomial time. In this paper, we propose an efficient near-optimal convex solution for layout guaranteeing satisfactory observation quality based on an anisotropic sensing model of camera. We construct the sensing model based on the physical imaging process. This model provides an accurate measurement of observation quality for the directional nonuniform sensing field of cameras. The layout is treated as selecting the best subset of cameras from a redundant initial layout with numerous cameras. Based on this characteristic and the sensing model, we formulate the layout as a sparse ℓ0 problem which is further relaxed to a convex ℓ1 minimization. Therefore, the near-optimal layout is efficiently obtained via convex optimization. Simulation results confirm the effectiveness of the proposed approaches.
High resolution range profile with promoted sparsity and refined grid

A high resolution range profile (HRRP) method via compressed sensing (CS) with re-weighted 11 minimization is proposed. Since the weighted CS model has a better representation of the true sparse description than that of the general 11 norm based CS model, the proposed compressive HRRP exhibits a remarkable suppression of the noise influence when dealing with a high background noise circumstance. To reduce the computational complexity, a refinement method is drawn. The experiment results demonstrate that, compared with classical CS, our improved CS model can achieve a higher resolution range profile while its computation time is relatively small.
Weighted Rate-Distortion Optimization for Screen Content Coding

Unlike camera-captured video, screen content (SC) often contains a lot of repeating patterns, which makes some blocks used as references much more important than others. However, conventional rate-distortion optimization (RDO) schemes in video coding do not consider the dependence among image blocks, which often leads to a locally optimal parameter selection, especially for SC. In this paper, we present a weighted RDO scheme for SC coding (SCC), in which the repeating characteristics are taken into account when deciding RD tradeoff for each block. For one block, the number being referenced by the current picture and following pictures is estimated and based on the number, we set a proper weight in the RDO process to reflect its importance from a global point of view. To estimate the number being referenced, we propose a hash-based method to approximate the results to avoid the complexity of direct search. Experimental results show that compared with the High Efficiency Video Coding SCC reference software, 10.1%, 14.5%, and 2.2% on average and up to 25.7%, 39.8%, and 4.6% bit saving can be achieved by considering weights provided by our scheme for hierarchical-B, IBBB, and all intra coding structures, respectively. Thanks to our hash-based design, the complexity increase brought by the proposed scheme is marginal.
Design of wavelet based on waveform matching and its application to signal compression

Wavelet transform is widely used in data compression. It is a useful method that constructs matched wavelets according to the features of signal for improving the compression ratio. A waveform matching criterion for constructing matched wavelets used for compressing one-dimensional signals is given. Based on the criteria, an optimal matched wavelet filter is constructed and an example of one-dimension signal compression is given. Compared with other wavelet filters, the matched wavelet is able to improve compression performance for a given signal.
The design of two-channel PR FIR filter bank with linear-phase using evolutionary strategies

A novel method for the design of a two-channel linear-phase perfect reconstruction (PR) FIR filter bank is proposed. In order to ensure the PR property, a lattice architecture has been adopted. Evolutionary strategies (ES) are used to search for an optimal set of coefficients in lattice structure. For increasing the searching speed, an initial set of coefficients is obtained by a constrained optimization method. Some design examples are given to illustrate the proposed method. The results are presented which show that this method can be used to design the filter bank with high stop band attenuation, compared to conventional methods.
Hybrid Distortion Ranking Tuned Bitstream-Layer Video Quality Assessment

No-reference bitstream-layer video quality assessment is very important and practical for monitoring the perceptual experience of end users and facilitating network maintenance. For pervasive Internet Protocol Television and mobile streaming services, in addition to quality degradation due to lossy compression, the unreliable transmission mechanism (i.e., User Datagram Protocol/IP) often leads to quality degradation due to packet loss. Different technical solutions bring in different types of visual artifacts. In this paper, we proposed a hybrid distortion ranking (HDR)-based bitstream-layer quality assessment model, whose artifact combination framework is based on the ranked linear combination operation. The model can predict the perceived quality of a video with sufficient accuracy when the video is distorted by compression artifacts, slicing artifacts, freezing (with frame skipping) artifacts, or their combinations. The core algorithms of the model were adopted into ITU-T Recommendations, P.1202.1 and P.1202.2. Furthermore, with respect to the three different types of artifacts, we compared the proposed no-reference HDR model with some state-of-the-art full-reference perceptual quality assessment models including Video Quality Model (i.e., ITU-T Rec. J.144), structural similarity (SSIM), multiscale SSIM, visual information fidelity, and the widely used metric, peak signal-to-noise ratio. We also compared our HDR model with the top performing no-reference models including Blind/Referenceless Image Spatial Quality Evaluator and video Blind Prediction of Natural Video Quality. The experiment results demonstrate the efficiency of our HDR model.
CDA: a system for understanding the dynamic properties of data processing programs

During software maintenance, it is of critical importance for maintenance staff to understand how a system works and when they make a change to part of the system, what effects this change will have on other parts of the system. It is observed that much of the program understanding process revolves around dynamic properties such as the states, state sequences and state transition operations. Hence, it is necessary to support the understanding process when the staff is reasoning about what could take place when the program is in operation and what could happen if part of the code is modified. CDA is a system for understanding the dynamic properties of large data processing programs. It allows the user to incrementally document their understanding of a program as working hypotheses and abstract operations with CDA comments. It then verifies these hypotheses to determine if they are justified. The justified hypotheses and abstract operations form an incremental specification of the program. Over time, the documentation improves in quality and completeness as new comments are added to justify and check the assumptions underlying new changes made to the code.<<ETX>>
Compressive Sensing via Nonlocal Low-Rank Regularization

Sparsity has been widely exploited for exact reconstruction of a signal from a small number of random measurements. Recent advances have suggested that structured or group sparsity often leads to more powerful signal reconstruction techniques in various compressed sensing (CS) studies. In this paper, we propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into CS of both photographic and MRI images. We also propose the use of a nonconvex log det ( X) as a smooth surrogate function for the rank instead of the convex nuclear norm and justify the benefit of such a strategy using extensive experiments. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using the alternative direction multiplier method technique. Experimental results have shown that the proposed NLR-CS algorithm can significantly outperform existing state-of-the-art CS techniques for image recovery.
Weighted rate-distortion optimization for screen content intra coding

Screen content videos often have mixed content consisting of various types such as natural content, text and graphics in the same picture. To achieve high coding efficiency for this content, new coding tools are developed in the High Efficiency Video Coding (HEVC) standard Screen Content Coding (SCC) extension. Among them, intra block copy (IntraBC) allows a nonlocal intra prediction from the coded region of the same picture. However, the rate-distortion optimization (RDO) scheme of the screen content coding still follows that of the HEVC reference software and each block is optimized locally. The screen content characteristics are not fully utilized in the current RDO scheme. This paper presents a weighted RDO scheme for the intra coding of screen content videos, which measures the importance of each block within the picture first and then larger distortion weights are applied to the blocks with larger importance in the RDO. In this way, a better rate-distortion trade-off can be achieved for the picture instead of the blocks themselves. The experimental results show that up to 5.5% coding efficiency gain can be achieved compared with the reference software.
No-reference image quality assessment with orientation selectivity mechanism

No-reference (NR) image quality assessment (IQA) technology is greatly required in quality-orientated visual signal processing systems. However, without the guidance of the reference information, it is still a great challenge for NR IQA to perform consistent with the subjective perception. Researches on cognitive neuroscience state that the human visual system (HVS) presents substantially orientation selectivity mechanism, within which the visual structures are extracted in the local receptive fields for scene understanding. Inspired by this mechanism, a set of orientation selectivity based visual patterns are designed. By analyzing the quality degradation on those patterns, a novel visual pattern degradation based NR IQA method is proposed. Experimental results on large databases demonstrate that the proposed method outperforms the existing NR IQA methods.
Edge-based dynamic ROI coding with standard compliance

An edge-based region-of-interest (ROI) image coding technique is proposed for low bit-rate visual communication. An image is compactly coded into two semantic levels: background sketch and object textures. The background sketch is a down-sampled version of the input image. The decoder reconstructs the background first by adaptive interpolation and lets users identify the ROI, and then requests the ROI textures to be transmitted. A distinct advantage of the proposed ROI technique is a compact edge-based descriptor of natural object boundaries. The expensive ROI geometry computations are carried out at the encoder only on demand, keeping decoder complexity low to benefit wireless devices. The new system outperforms the dynamic ROI coding of JPEG 2000 in both visuality and PSNR. Furthermore, both background and texture coding can be made compliant with any existing compression standard.
Image denoising based on improved adaptive directional lifting wavelet transform

In this paper we propose a new denoising technique based on improved adaptive directional lifting wavelet transform (ADL). Using this method can separate noise from image signal distinctly by the extraordinary ability of ADL to represent the edges and textures. However, in the smooth regions of a noisy image, ADL is expensive and inaccurate. Therefore, we construct the ADL in an anti-noise way based on pixel pattern classification. Compared with the traditional ADL for image compression, there is no restriction of side-information, so the optimal strategies of direction determination and transformation can be selected by the judgment of different pixel patterns. Experimental results show that the proposed technique outperforms traditional wavelet and lifting scheme in both PSNR and visual quality, especially for the images with rich texture features such as remote sensing images.
Single Shot Dual-Frequency Structured Light Based Depth Sensing

Structured light techniques are widely used for depth sensing. In this paper, we propose a single shot dual-frequency structured light based method to achieve dense depth in dynamic scenes. The projected pattern is a mixture of two different periodical waves whose phases are related to the change of color and intensity respectively, which can avoid the requirement of Fourier spectra separation in other multi-frequency patterns. Gabor filter is adopted to interpolate the phases. The number theory is used to deal with the phase ambiguity in phase based method conveniently and speedily. A dense depth can be achieved because of the phase-based encoding mode. The proposed method is suitable for dense depth acquisition of the moving object. Experimental results show higher accuracy of the proposed method in depth acquisition compared with the Kinect and larger resolution compared with the ToF (Time of Flight) depth camera. Meanwhile, the proposed method can also acquire the depth of the color scenes and is robust to the surface texture of objects.
Robust Foreground Estimation via Structured Gaussian Scale Mixture Modeling

Recovering the background and foreground parts from video frames has important applications in video surveillance. Under the assumption that the background parts are stationary and the foreground are sparse, most of existing methods are based on the framework of robust principal component analysis (RPCA), i.e., modeling the background and foreground parts as a low-rank and sparse matrices, respectively. However, in realistic complex scenarios, the conventional  $\ell _{1}$  norm sparse regularizer often fails to well characterize the varying sparsity of the foreground components. How to select the sparsity regularizer parameters adaptively according to the local statistics is critical to the success of the RPCA framework for background subtraction task. In this paper, we propose to model the sparse component with a Gaussian scale mixture (GSM) model. Compared with the conventional  $\ell _{1}$  norm, the GSM-based sparse model has the advantages of jointly estimating the variances of the sparse coefficients (and hence the regularization parameters) and the unknown sparse coefficients, leading to significant estimation accuracy improvements. Moreover, considering that the foreground parts are highly structured, a structured extension of the GSM model is further developed. Specifically, the input frame is divided into many homogeneous regions using superpixel segmentation. By characterizing the set of sparse coefficients in each homogeneous region with the same GSM prior, the local dependencies among the sparse coefficients can be effectively exploited, leading to further improvements for background subtraction. Experimental results on several challenging scenarios show that the proposed method performs much better than most of existing background subtraction methods in terms of both performance and speed.
Design and implementation of stack filter based on immune memory clonal algorithms with hybrid computation

Stack filters are a class of nonlinear filters for removing noise that is uncorrelated with signal. Their design is formulated as a high nonlinear optimization problem. An improved immune memory clonal selection algorithm (IMCSA) is suitable for designing stack filters. But as many algorithms for designing stack filters have to face that evaluation of each candidate solution is still takes the most computation complexity. By representing the positive Boolean functions (PBF) in objective function, a hybrid computation approach (software and hardware) for the calculation tasks has been proposed. Compared with available methods, the proposed method can improve the efficiencies of design stack filters and the design time is significantly reduced. The procedure of design and implementation for stack filters can be reached at the same time by the method