nils-herrmann/clean_scirepeval huggingface dataset:
Structural uncertainty based just noticeable difference estimation

Just noticeable difference (JND) reveals the minimum visible threshold of the human visual system (HVS), which is useful in visual redundancy reduction. Existing JND models estimate the visible threshold with luminance adaptation and contrast masking. As a result, the smooth and edge regions are effectively estimated, while the disorderly texture regions are always underestimated. The disorderly texture regions possess a large amount of disorderly structures and the HVS cannot fully perceive them. Therefore, in this work, we suggest to consider the disorder degree of structure for JND threshold estimation. According to the correlation among neighboring pixels, the uncertain information is extracted, and the disorder degree of structure is computed, which we called structural uncertainty. Then, taking the effect of background luminance, contrast, and structural uncertainty into account, a novel JND model is deduced. Experimental results demonstrate that the proposed JND can accurately estimate the visible thresholds of different image regions. Moreover, the proposed JND is adopted to remove visual redundancy for JPEG compression, which saves about 14% bit rate while keeping the perceptual quality.
Signal-adapted directional lifting scheme for image compression

In this paper, we propose a new adaptive lifting scheme that not only locally adapts the filtering directions to the orientations of image features, but also adapts the lifting filters to the statistic properties of image signal. The proposed approach refines previous adaptive directional lifting-based wavelet transform (ADL) by combining directional lifting and adaptive lifting filters to form a unified framework. The image signal is first segmented into regions of textures and edges with close directional features. The lifting filters are then effectively designed. The prediction step is designed to minimize the prediction error of the image signal, and the update step is designed to minimize the reconstruction error. Significant improvements on objective and subjective quality over conventional 2-D wavelet transform and previous ADL transform are achieved.
Compressive sensing-based multiple description image coding for wireless channel

A new theory, known as Compressive Sensing (CS), has recently proved that sparse or compressible signal can be accurately reconstructed from very highly undersampled data by solving by solving a convex LI optimization problem. This paper, based on such new theory, presents a novel multiple description coding (MDC) method to combat packet loss. According to compressive sensing framework, our method has an attractive property that the reconstruction error only depends on the received measurement number but not on which measurements are received. Another advantage of our method is that it is a balanced MDC scheme with fine description granularity and low encoding complexity.
Bottom–Up Visual Saliency Estimation With Deep Autoencoder-Based Sparse Reconstruction

Research on visual perception indicates that the human visual system is sensitive to center-surround (C-S) contrast in the bottom-up saliency-driven attention process. Different from the traditional contrast computation of feature difference, models based on reconstruction have emerged to estimate saliency by starting from original images themselves instead of seeking for certain ad hoc features. However, in the existing reconstruction-based methods, the reconstruction parameters of each area are calculated independently without taking their global correlation into account. In this paper, inspired by the powerful feature learning and data reconstruction ability of deep autoencoders, we construct a deep C-S inference network and train it with the data sampled randomly from the entire image to obtain a unified reconstruction pattern for the current image. In this way, global competition in sampling and learning processes can be integrated into the nonlocal reconstruction and saliency estimation of each pixel, which can achieve better detection results than the models with separate consideration on local and global rarity. Moreover, by learning from the current scene, the proposed model can achieve the feature extraction and interaction simultaneously in an adaptive way, which can form a better generalization ability to handle more types of stimuli. Experimental results show that in accordance with different inputs, the network can learn distinct basic features for saliency modeling in its code layer. Furthermore, in a comprehensive evaluation on several benchmark data sets, the proposed method can outperform the existing state-of-the-art algorithms.
Joint rate-distortion optimization for H.264/AVC intra coding based on cluster computing

This paper presents a joint rate-distortion optimization coding scheme for H.264/AVC intra coding, which uses cluster computing technologies to jointly optimize coding parameters among different image blocks. We identify two kinds of dependencies, inter sub-block dependency and inter macroblock dependency, ignored in the ordinary rate-distortion optimization and a joint RD cost is designed in the optimization process to consider those dependencies. We show that with our proposed scheme, a significant better coding performance can be achieved with the standard-compliant bitstreams. Experiment results show up to 5% bits saving compared to the H.264/AVC reference software.
A Sparse Representation-Based DOA Estimation Algorithm With Separable Observation Model

Conventional sparse representation (SR)-based direction-of-arrival (DOA) estimation algorithms suffer from high computational complexity. To be specific, a wide angular range and a large-scale array will enlarge the scale of the spatial observation matrix, which results in huge computation cost for DOA estimation. In this letter, a new efficient DOA estimation algorithm based on the separable sparse representation (SSR-DOA for short) is derived, in which a separable structure for spatial observation matrix is introduced to reduce the complexity. Besides, a dual-sparsity strategy is engaged to make the algorithm tractable. Experimental results show that high resolution performance can be obtained efficiently by the proposed algorithm.
A Novel Just Noticeable Difference Model via Orientation Regularity in DCT Domain

Just noticeable difference (JND) model plays an important role in removing perceptual redundancies for image/video compression. However, the existing subband-based JND models have two limitations: one is the evaluation of contrast masking (CM) effect is not comprehensive; the other is that the operation within cross domain is computational complexity. In this paper, we propose a new orientation regularity-based JND model to solve these problems in the discrete cosine transform (DCT) domain. Inspired by the structure regularity extraction in the human brain, we suggest that orientation features play an important role in the analysis of visual content. By deducing the DCT function, the orientation information is first analyzed with the DCT coefficients along different directions, and the orientation regularity is calculated with the coefficient distribution. Then, according to the orientation regularity and the frequency texture energy, the DCT blocks are classified into five types, i.e., smooth, orderly edge, disorderly edge, orderly texture, and disorderly texture. By combining these block types with their human visual system sensitivities, a more accurate CM model is proposed in a DCT domain. Finally, by incorporating the contrast sensitivity function and luminance adaptation effect, a novel DCT-based JND model is established. Since the proposed model introduces a more accurate CM model and performs only in a DCT domain, it is more efficient and concise than the state-of-the-art DCT-based JND models in theory and practice. The experimental results also show that the proposed JND model has a superior performance without cross domain.
Design of nonsubsampled directional filter banks with arbitrary number of channels

This paper studies a novel method for creating M-channel nonsubsampled directional filter banks (DFBs). It is based on the pseudo-polar Fourier transform (PPFT) which evaluates the Fourier transform at points along rays equispaced in slope. We take some modifications on the arrangement of the euqispaced rays, making the two-dimensional (2D) PPFT grid to the Cartesian 2D discrete-Fourier transform grid. Then we apply one-dimensional (1D) filter banks (FBs) to the arranged PPFT along the slope direction. This can perform the decomposition of images into arbitrary directionally-oriented subbands. By using this method, the design of DFBs is reduced to that of 1D FBs, leading to the low design complexity and good design flexibility. Furthermore, by combining the proposed DFBs with Laplacian pyramid, we can achieve a multiscale and multidirection system. Experimental result is given to illustrate the proposed approach.
Nonlocal Sparse and Low-Rank Regularization for Optical Flow Estimation

Designing an appropriate regularizer is of great importance for accurate optical flow estimation. Recent works exploiting the nonlocal similarity and the sparsity of the motion field have led to promising flow estimation results. In this paper, we propose to unify these two powerful priors. To this end, we propose an effective flow regularization technique based on joint low-rank and sparse matrix recovery. By grouping similar flow patches into clusters, we effectively regularize the motion field by decomposing each set of similar flow patches into a low-rank component and a sparse component. For better enforcing the low-rank property, instead of using the convex nuclear norm, we use the log det (·) function as the surrogate of rank, which can also be efficiently minimized by iterative singular value thresholding. Experimental results on the Middlebury benchmark show that the performance of the proposed nonlocal sparse and low-rank regularization method is higher than (or comparable to) those of previous approaches that harness these same priors, and is competitive to current state-of-the-art methods.
Compressive sensing recovery of dynamic MRI via nonlocal low-rank regularization

Compressive sensing (CS) based dynamic MRI techniques have been proposed to improve the imaging speed and spatiotemporal resolution. However, existing CS recovery methods haven't exploited the rich redundancy among the spatial and temporal dimensions. In this paper, we address the CS recovery of dynamic MRI from partially sampled k-t space using the nonlocal low-rank regularization (NLR). To exploit the nonlocal redundancy in the spatial-temporal dimension, the dynamic MRI sequence is divided into overlapping 3D patches along both the spatial and temporal directions. We exploit the fact that the matrix that consists of a sufficient number of similar patches is low-rank. To effectively approximate the low-rank matrix, the non-convex surrogate function logdet (·) is used instead of the convex nuclear norm. Experimental results show that our proposed method can outperform existing state-of-the-art dynamic MRI reconstruction methods.
An improved model of pixel adaptive just-noticeable difference estimation

A pixel-wise adaptive model for estimating the just-noticeable difference (JND) in spatial domain is proposed in this paper. As the human visual system (HVS) can be considered as a multichannel system, we assume that there exist two channels in the HVS, which deliver luminance adaption factor and texture masking factor, respectively. Both channels affect the JND threshold in a cooperative manner. The texture regions are with abundant redundancy and can tolerate much noise. The disorder degree and spatial masking of the texture are considered to estimate the texture masking effect, for deducing such JND threshold that coincides with the HVS. Finally, the luminance adaptation factor and texture masking factor are combined nonlinearly. Various experiments confirm the improved model has a better visual effect than models proposed before.
Retrieval of Atmospheric Aerosol and Surface Properties Over Land Using Satellite Observations

It is difficult to retrieve aerosol and surface reflectance properties over land, simultaneously, from satellite observations. In the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol algorithm, 2.1-μm observations are essential to determine land surface reflectances. However, observations at 2.1 μm are not widely available for other sensors. To get rid of 2.1-μm observations in aerosol retrievals, we suggest a new method that makes use of the priori relationships among surface reflectances at different wavelengths in terms of the surface normalized difference vegetation index (sNDVI), which is defined by the surface reflectances at 0.87 and 0.67 μm. It has advantages to parameterize surface reflectances due to its independence from atmospheric condition. Based on these newly established surface reflectance relationships and the MODIS observations at 0.47, 0.67, 0.87, and 1.64 μm, we solve four variables simultaneously with the new method: aerosol optical depth (AOD) at 0.55 μm, proportion of fine mode AOD, surface reflectance at 0.67 μm, and sNDVI. The preliminary retrieval tests indicate that this method can successfully retrieve the aerosol properties and land cover features over East Asia. A comparison of the retrieved AODs at 0.55 μm with the AERONET observations shows a root-mean-square error (RMSE) of about 0.16, at the Beijing and Xianghe sites, in August 2007. The accuracy is the same order of magnitude as the MODIS aerosol algorithm (RMSE = 0.14) during the same period. The results demonstrate that the quality of aerosol retrieval in the new method is comparable to that of the MODIS aerosol algorithm over the testing area.
Compressive modulation in digital communication

Bandwidth efficiency is one of the most important indicators to measure different modulation schemes in digital communication systems. The waveforms of existing modulation schemes are all separated in time domain, making it difficult for them to improve in bandwidth efficiency. Compressive Sensing (CS) theory shows that it is possible to reconstruct original signals in aliasing measurements. In this paper, we propose a Compressive Modulation scheme by combining CS theory and traditional BPSK pattern. Theoretic analysis and experimental results show that the bandwidth efficiency can be highly improved by using the proposed scheme.
Adaptive patch matching for motion compensated prediction

Motion compensated prediction (MCP) plays an important role in video coding due to its great capability of reducing temporal redundancy. In this paper, we propose a new MCP scheme by adaptive patch matching with the full use of the reconstructed pixels surrounding the current block (referred to as the template inside the patch) aiming at achieving a more accurate prediction than conventional MCP. The proposed scheme not only takes advantage of the temporal correlation but also efficiently exploits the spatial correlation between the current block and its template inside the patch. An adaptive linear combination of the current block and its template in motion estimation is designed to generate an optimal prediction while maintaining the local variation of the current block. Accordingly, a modification of the rate-distortion criterion is introduced to select the combined prediction. Experimental results show that our proposed APM achieves improved coding performance compared with H.264/AVC.
Super-resolution imager via compressive sensing

In this paper, we propose a novel imager that can acquire super-resolution (SR) images with significantly fewer sensors. The theoretical basis of this imager is compressive sensing (CS) theory, which calls for a measurement matrix with good properties for effective reconstruction, such as RIP . Such a property indicates that entries of the received signal are effectively aliased. In our imager we use an optic effect called spherical aberration to achieve such aliased measurement of light intensity (the signal), thus realizing an ideal measurement matrix. The original image can then be efficiently reconstructed through the filternating Direction Method (ADM) . The implementation of the proposed imager needs only replace an ordinary lens with a spherical lens of large curvature, with almost no additional cost, in contrast with existing complex systems, such as the single pixel camera using the micro-mirror device . Simulation results show that despite its simplicity, the performance of the proposed imager is comparable with traditional CS models (most of which are difficult for physical implementation). Further, since the lens is a linear shift-invariant (LSI) system, FFT can be incorporated into the ADM algorithm to accelerate the reconstruction , adding to its advantage over some other CS-based imagers.
Nonbinary LDPC Codes on Cages: Structural Property and Code Optimization

A (v,g)-cage is a (not necessarily unique) smallest v-regular graph of girth g. On such a graph, a nonbinary (2,v)-regular low-density parity-check (LDPC) code can be defined such that the Tanner graph has girth 2g and the code length achieves the minimum possible. In this paper, we focus on two aspects of this class of codes, structural property and code optimization. We find that, in addition to those found previously, many cages can be used to construct structured LDPC codes. We show that all cages with even girth can be structured as protograph-based codes, many of which have block-circulant Tanner graphs. We also find that four cages with odd girth can be structured as protograph-based codes with block-circulant Tanner graphs. For code optimization, we develop an ontology-based approach. All possible inter-connected cycle patterns that lead to low symbol-weight codewords are identified to put together the ontology. By doing so, it becomes handleable to estimate and optimize distance spectrum of equivalent binary image codes. We further analyze some known codes from the Consultative Committee for Space Data Systems recommendation and design several new codes. Numerical results show that these codes have reasonably good minimum bit distance and perform well under iterative decoding.
Just Noticeable Difference Estimation for Images With Free-Energy Principle

In this paper, we introduce a novel just noticeable difference (JND) estimation model based on the unified brain theory, namely the free-energy principle. The existing pixel-based JND models mainly consider the orderly factors and always underestimate the JND threshold of the disorderly region. Recent research indicates that the human visual system (HVS) actively predicts the orderly information and avoids the residual disorderly uncertainty for image perception and understanding. Thus, we suggest that there exists disorderly concealment effect which results in high JND threshold of the disorderly region. Beginning with the Bayesian inference, we deduce an autoregressive model to imitate the active prediction of the HVS. Then, we estimate the disorderly concealment effect for the novel JND model. Experimental results confirm that the proposed JND model outperforms the relevant existing ones. Furthermore, we apply the proposed JND model in image compression, and around 15% of bit rate can be reduced without jeopardizing the perceptual quality.
A novel extracellular spike detection algorithm based on sparse representation

Identification of spikes in the extracellular recording signals is a technical challenge because of large amounts of background noise and contributions of many neurons to recorded signals. In this paper, a novel method based on sparse representation is proposed for high accuracy and robust spike detection. Considering the diversity of spikes, a universal dictionary is first learned for giving a sparse representation to various spike signals. In addition, in order to improve the robustness to noise, we propose to use sparse coefficients as features for the discrimination of spikes. Finally, the number and locations of spike events in the recorded signal are determined through a thresholding process. Experimental results on both synthesized extracellular neural recordings and real data demonstrate that the proposed method performs much better than the existing methods in terms of both robustness and flexibility.
Hierarchical Feature Degradation Based Blind Image Quality Assessment

Though blind image quality assessment (BIQA) is highly demanded for many image processing systems, it is extremely difficult for BIQA to accurately predict the quality without the guide of the reference image. In this paper, we introduce a novel BIQA method with hierarchical feature degradation (HFD). Since the human brain presents hierarchical procedure for visual recognition, we suggest that different levels of distortion generate different degradations on hierarchical features, and propose to consider the degradations on both the low and high level features for quality assessment. Inspired by the orientation selectivity (OS) mechanism in the primary visual cortex, an OS based local visual structure is designed for low-level visual content extraction. Meanwhile, according to the feature integration function of deep neural networks, the deep semantics is extracted with the residual network for high-level visual content representation. Next, by analyzing the degradation on both the local structure and the deep semantics, a HFD based memory (prior knowledge) is learned to represent the generalized quality degradation. Finally, with the guidance of the HFD based memory, a novel HFD-BIQA model is built. Experimental results on the publicly available databases demonstrate the quality prediction accuracy of the proposed HFD-BIQA, and verify that the HFD-BIQA performs highly consistent with the subjective perception1.
Improved Harris corner detector algorithm for image co-registration

Image registration is the process of removing the offset between two or more images of the same location from different angles. One of the two images is the master image and the other is the slave image. In this paper an effective algorithm is proposed to align the two images; this algorithm is based on combining both area-based methods and feature-based methods. Cross-correlation function is used to detect the coarse offset between the master image and the slave image, while the fine offset between the two images is detected by the Harris corner detector  and Scale-Invariant Feature Transform (SIFT) which is proposed by Lowe (1999). Random Sample Consensus (RANSAC) algorithm  is used to estimate the transformation model. The proposed algorithm is evaluated by using the coherence map. The results show that the proposed algorithm is more effective than using the automatic image registration methods alone .
Intra frame coding with template matching prediction and adaptive transform

For natural images, there are usually repeating similar contents but hard to be well predicted locally. Prediction using template matching is an effective technology to exploit such a non-local correlation. In this paper, we propose an alternative scheme to further exploit the non-local correlation. In the proposed scheme, template matching is also used to search for probable similar references to the current block to be coded. We then use these references to train an adaptive transform, which most likely reflects the statistical characteristic of the current block. The proposed scheme can further exploit correlation between the current block and more possible references. Compared to the scheme that only integrates prediction by template matching, the proposed scheme shows improvement about 0.45dB PSNR increase or 9.3% bit saving on average, which leads to 1dB's gain or 19.5% bit saving on average compared to the state-of-the-art scheme without using template matching.