nils-herrmann/clean_scirepeval huggingface dataset:
Perceptual watermarking robust to JPEG compression attack

A new transparent and robust watermarking method against JPEG compression attack based on a perceptual approach is proposed. The main idea is to embed the watermark into regions where the blocking artifact has no effect on the watermark. These regions are identified by using a spatial prediction model of blocking effect. A JND (Just-Noticeable-Difference) model is then used to control the embedding strength in order to ensure robustness and transparency of the watermarking. The performance of the proposed method is evaluated on a set of images and some common “signal processing" attacks.
Pyramidal perceptual filtering using Moon and Spencer contrast

A perceptual multiresolution filtering method (Moon and Spencer, 1945) based on human perception models is proposed. The main idea is to detect and remove the irrelevant structures at different scales using the just-noticeable contrast notion and luminance adaptation. The processing is done in the Laplacian and Gaussian pyramid decompositions of the image. The consistency of the method is demonstrated on a gray-level image.
Image quality assessment based on wave atoms transform

Image quality assessment is still an active field of research. The main objective of the developed image quality metric is to offer an index of quality that is consistent with the human subjective judgment of image quality. Despite the great number of developed metrics, there is still a need for image analysis tools that is able to extract the most perceptual relevant characteristics of an image. The goal of this work is then to propose a more advanced analysis and representation tools to extract more effective features that could be incorporated in the design of the image quality metric. In this paper, we propose a novel objective metric based on wave atoms transform. This new transform is half multiscale and half multi-directional. It offers a better representation of images containing oscillatory patterns and textures than the others known transforms . In this work, we propose a new full reference image quality metric based on wave atom transform and exploiting some properties of the human visual system. The consistency of the proposed metric with subjective evaluation is performed on LIVE database. The obtained correlation of this metric with the MOS provided by the database is better than other known metrics confirming thus the efficiency of this new image quality measure in predicting image quality.
A comprehensive performance evaluation of objective quality metrics for contrast enhancement techniques

In this paper, we present a comprehensive analysis and comparison of state-of-the-art Contrast Enhancement (CE) evaluation metrics. In this work, we developed a new database consisting of 182 images. The subjective experiments were performed to obtain a preference rating from different observers for the enhanced images using six CE methods selected from different representative categories. The quality of the enhanced images is measured with most commonly used CE evaluation metrics. We provide the ranking of images based on perceptual preference as well as objective quality metrics scores. We show that some of the metrics used for the enhancement evaluation are not consistent with the human subjective scores. This new database, named as Contrast Enhancement Evaluation Database (CEED2016), is made publicly available to the research community at http://wwwl2ti.univ-paris13.fr/site/index.php/en/CEED2016/ and is expected to be a contribution to the area of Image Quality Assessment (IQA) and particularly for image quality enhancement evaluation.
Perceptual non local mean (P-NLM) denoising

In this paper we propose a denoising technique based on nonlocal means using an image similarity measure. The idea is to use the SVD-based image quality metric as a measure of neighborhood similarity. This measure is then used in the computation of the spatial Gaussian weighting kernel. We also develop an optimization computation scheme using a parallel architecture in order to accelerate the filtering process on different machines or different cores on the same machine. The obtained results are very promising.
Vehicle Tracking by non-Drifting Mean-shift using Projective Kalman Filter

Robust vehicle tracking is essential in traffic monitoring because it is the groundwork to higher level tasks such as traffic control and event detection. This paper describes a new technique for tracking vehicles with mean-shift using a projective Kalman filter. The shortcomings of the mean-shift tracker, namely the selection of the bandwidth and the initialization of the tracker, are addressed with a fine estimation of the vehicle scale and kinematic model. Indeed, the projective Kalman filter integrates the non-linear projection of the vehicle trajectory in its observation function resulting in an accurate localization of the vehicle in the image. The proposed technique is compared to the standard Extended Kalman filter implementation on traffic video sequences. Results show that the performance of the standard technique decreases with the number of frames per second whilst the performance of the projective Kalman filter remains constant.
Deterministic techniques for multichannel blind image deconvolution

In this paper, we address four deterministic methods for blind multichannel identification in a blind image restoration fr amework. These methods are: SubSpace method (SS), Minimum Noise Subspace (MNS) and Symmetric MNS (SMNS) methods, Cross Relation method (CR) and Least Squares Smoothing method (LSS). The latter is a new method that is introduced, here, for the first time, as an extension, from the 1-D to the 2-D case, of the least squares method by L. Tong et al. (1999). For each method, we detail its basic principle and provide a summary of its corresponding algorithm. In the noise free case, all the methods developed here offer a perfect channel identification. In the noisy case, these methods hav e a different behavior and their performance are compared in terms of channel identification by means of MSE.
A visual sensitivity model based stereo image watermarking scheme

We present a visual sensitivity model based method for watermarking high definition (HD) stereo images in DCT domain. Our proposal is devoted to make the stereo image watermarking scheme as robust and invisible as possible. Visual sensitivity refers to the ability of human observers to detect distortion in visual field. In order to achieve the best tradeoff point of robustness and transparency of the watermarked images, the watermarking scheme is based on the visual sensitivity model to determine the perceptual adjustment on watermark insertion. The performance of the proposed method has been tested under various attacks producing very promising robustness results while retaining the watermark transparency.
An information based framework for performance evaluation of image enhancement methods

We propose a new framework for evaluating image enhancement methods. In this work, we focus on contrast enhancement, and use the joint probability and the mutual information derived from the image co-occurrence matrix to determine the proposed index. We show that the second order entropy and the mutual information can be used jointly for tracking and evaluating the effect of contrast enhancement. More importantly, we show that the variations of the mutual information mimic, in a consistent way, human perceptual sensitivity to visual appearance of image changes due to contrast enhancement. The proposed approach is seen as a completely new framework for evaluating image enhancement methods, validated by a number of experiments. Finally, we also show that the proposed metric can even be used to detect image enhancement thresholds at which unpleasant artifacts start to be perceived by viewers.
Coding artifact reduction using adaptive post-treatment

This paper presents a post-processing method for reducing blocking and ringing artifacts resulting from lossy coding techniques based on the Discrete Cosine Transform (DCT) such as JPEG, H.261, H.263 or MPEG. These coding artifacts appear at low and medium bit rate and strongly affect the image quality. The proposed method is based on a constrained least-squares method using adaptive smoothing post-processing. The objective is to reduce blocking artifact and edge noise while preserving image details. To evaluate the visual image quality, a new blockiness visibility measure is proposed. The method has been tested on some degraded images and video sequences. The obtained results are satisfying and clearly demonstrate the efficiency of the proposed method.
Image denoising using Bilateral filter in high dimensional patch-space

This paper proposes a new noise filtering method inspired by Bilateral filter (BF) and non-local means (NLM) filter. The main idea here is to perform the BF in a multidimensional patch-space using an anisotropic kernel. The filtered multidimensional signal is then transformed back onto the image spatial domain to yield the desired enhanced image. The proposed method is compared to the state-of-art. The obtained results are highly promising.
A robust-transparent watermarking scheme based on perceptual modeling

In this paper, we present a generic watermarking framework that employs perceptual modeling to embed a watermark in such a way that it cannot be perceived. Our approach consists in building computational models which take into account the most common properties of the HVS (Human Visual System) that can be exploited for watermarking. We describe two ways a perceptual model can be incorporated into a watermarking system to control the imperceptibility of the watermark: the implicit and the explicit embedding scheme. Both of schemes are designed in the pyramid transform domain proposed by Burt and Adelson. In the implicit embedding, the watermark is only inserted into imperceptible regions defined using a visibility map. In the explicit embedding, the watermark is embedded in all Laplacian coefficients with strength determined by a JND (Just-Noticeable-Distortion) threshold. The obtained results are twofold. Firstly, the explicit embedding performs better than the implicit one. Secondly, watermarking schemes based on HVS modeling have advantages over schemes that do not take perceptual aspect into consideration. These advantages are demonstrated in terms of imperceptibility and robustness via various experiments.
A new patch-based approach for noise filetring

A common framework is proposed for Gaussian, unilateral, bilateral and non-local means filters. A new denoising method is then introduced for removing additive independent Gaussian noise. It is adapted and applied for filtering signal-dependent noise such as film grain noise. The main idea is to transform the signal-dependent into independent additive Gaussian noise using a nonlinear transformation prior to filtering. The obtained results clearly demonstrate the efficiency of the proposed method.
An audiodisplay tool for visually impaired people: the sound screen system

The aim of this contribution is to present a flexible realtime system designed for vision rehabilitation using sensory substitution. In this version, the developed substitutive sensory system is restricted to graphical information. The system is based on some findings in psychoacoustics obtained from actual experiments. It is based on the auditive localization of virtual sound sources. This system consists of four loudspeakers located at the four comers of a square in the vertical plane in front of the listener. The basic idea is to construct an input-output mapping which converts graphical input information into localized sound source. The shapes of graphical forms are then converted into sounds produced by moving a virtual source. The displacement in azimuth and elevation on the plan defined by the four loudspeakers of this virtual sound source allows different shapes to be drawn. The temporal integration by the listener of successive positions occupied by this virtual sound source describes the global graphic object. The system has been successfully evaluated through experiments involving a small group of sighted and blind subjects.
Vector quantization for image compression based on fuzzy clustering

In this paper a codebook design for image compression based on the fuzzy c-means (FCM) algorithm is presented. The codebook design from training vectors is viewed as a fuzzy clustering problem of unlabeled data points into clusters. Due to computational cost of FCM to generate the codebook, a fast version (FFCM), which operates on the image histogram, is used to obtain a good initial codebook to start the FCM algorithm. Experimental results are presented to illustrate the performance of the proposed compression method.
Image analysis using anisotropic local contrast

This article describes a contrast measure inspired by findings from human vision. It takes into account the frequency and orientation selectivity of cortical cells in the mammalian visual system. The input image is decomposed into different spatial frequency bands and orientations using Gabor filters. Then, a local directional contrast measure is computed for each spatial frequency band by introducing a competition mechanism among the different orientations. The visual information conveyed by this new contrast measure, called local directional bandlimited contrast (LDBC), has been analyzed on real and synthetic images. Results based on a limited set of images are presented which confirm the consistency of this new measure with the subjective perception of contrasts in complex natural images.
Vehicle Tracking Using Projective Particle Filter

This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating the linear fractional transformation. This provides a better coverage of the feature space and yields a finer estimate of the posterior density. Experiments conducted on traffic video surveillance sequences show that the variance of the estimated trajectory is reduced, resulting in more robust tracking.
A new adaptive nonlinear anisotropic diffusion for noise smoothing

The authors propose an adaptive nonlinear filtering method based on the nonlinear anisotropic diffusion equation. This new method has the advantage of flexibility and adaptability. Furthermore, a new degradation effect, not signaled before, we call the "pinhole effect", which may result in anisotropic diffusion is introduced and analyzed. A robust solution to this effect is proposed and evaluated through experimental data. The performance of the method is demonstrated on real and synthetic images.
A multiscale and multi-orientation contrast enhancement method for digital mammography

A multidirectional and multiscale contrast enhancement method for digital mammography images is proposed. The main idea is to enhance selectively the contrast of the sub-bands according to their position and orientation in the frequency domain. The image is then decomposed into different scales and orientations using the steerable pyramid transform. The contrast in each selected band is enhanced with a nonlinear mapping function. The enhanced image is then obtained by a pyramid reconstruction of these contrasted bands. This results in a significant improvement of the mammography features visibility. To evaluate the quality of the enhanced images, a new objective quality measure based on the spectral energy analysis is used.
Image quality assessment using a neural network approach

In this paper, we propose a neural network approach to image quality assessment. In particular, the neural network measures the quality of an image by predicting the mean opinion score (MOS) of human observers, using a set of key features extracted from the original and test images. Experimental results, using 352 JPEG/JPEG2000 compressed images, show that the neural network outputs correlate highly with the MOS scores, and therefore, the neural network can easily serve as a correlate to subjective image quality assessment. Using 10-fold cross-validation, the predicted MOS values have a linear correlation coefficient of 0.9744, a Spearman ranked correlation of 0.9690, a mean absolute error of 3.75%, and an rms error of 4.77%. These results compare very favorably with the results obtained with other methods, such as the structural similarity index of Wang et al. [2004].
Markov random fields for abnormal behavior detection on highways

This paper introduces a new paradigm for abnormal behavior detection relying on the integration of contextual information in Markov random fields. Contrary to traditional methods, the proposed technique models the local density of object feature vector, therefore leading to simple and elegant criterion for behavior classification. We develop a Gaussian Markov random field mixture catering for multi-modal density and integrating the neighborhood behavior into a local estimate. The convergence of the random field is ensured by online learning through a stochastic clustering algorithm. The system is tested on an extensive dataset (over 2800 vehicles) for behavior modeling. The experimental results show that abnormal behavior for a pedestrian walking, running and cycling on the highway, is detected with 82% accuracy at the 10% false alarm rate, and the system has an overall accuracy of 86% on the test data.
A new reference-free image quality index for blur estimation in the frequency domain

A new reference-free image quality index based on spectral analysis is proposed. The main idea is based on exploiting the limitations of the Human Visual System (HVS) in blur detection,. The proposed method consists of adding blur to the test image and measuring its impact. The impact is measured using radial analysis in the frequency domain. The efficiency of the proposed method is tested objectively by comparing it to some well known algorithms and in terms of correlation with subjective scores.
Design of an image distortion measure using spatial/spatial-frequency analysis

It is generally acknowledged that the image quality measures based or inspired by some findings on the human visual system (HVS) mechanisms are more appealing than classical measures such as the PSNR or MSE. Many HVS-based image quality assessment methods have been previously proposed. However, because of the complexity and the influence of the great number of observation parameters on the HVS-based image quality metrics there is no standard measure yet. In this paper, the image distortion metric proposed in A. Beghdadi et al. (2003) is reviewed and some improvements are suggested. This metric makes use of the joint spatial/spatial-frequency representation and applies a relatively simple nonlinear energy detection procedure. This simple distortion measure does not take into account any well-established model for the masking effect. The consistency of the proposed measure with subjective human evaluation is demonstrated on some standard image distortions.
Rate distortion optimal bit allocation for stereo image coding

Many research works have been developed for stereo image compression purpose where most of them aim at encoding a reference image, a residual one and a disparity map. While the disparity field is often losslessly encoded, we are mainly interested in this paper in the bit allocation problem between the reference and residual images. Generally, the bit allocation is expressed as an optimization problem which involves the computation of the operational rate-distortion (RD) functions for all the wavelet subbands and for different quantization steps. However, this strategy is computationally intensive. To solve this problem, we consider the uniform scalar quantization of the wavelet subbands of both images modeled by a Generalized Gaussian distribution. Thanks to recent approximations of the entropy and distortion functions, we develop an optimal and fast bit allocation method. The obtained results confirm the efficiency of the proposed bit allocation method in the context of stereo image coding.
A New Look to Multichannel Blind Image Deconvolution

The aim of this paper is to propose a new look to MBID, examine some known approaches, and provide a new MC method for restoring blurred and noisy images. First, the direct image restoration problem is briefly revisited. Then a new method based on inverse filtering for perfect image restoration in the noiseless case is proposed. The noisy case is addressed by introducing a regularization term into the objective function in order to avoid noise amplification. Second, the filter identification problem is considered in the MC context. A new robust solution to the degradation matrix filter is then derived and used in conjunction with a total variation approach to restore the original image. Simulation results and performance evaluations using recent image quality metrics are provided to assess the effectiveness of the proposed methods.
Image quality metrics based multifocus image fusion

We describe an innovative methodology for block based multifocus image fusion in the spatial domain. The main idea is to use blind or no reference blur metrics for image fusion applications. Two blur quality metrics based on edge content information and radon wigner ville based mean directional entropy, developed in the paper are used as activity measures. Fusion is performed by calculating blur based activity measure for each sub window in the source images and using maximum selection criterion according to the magnitude of these metrics. Furthermore, the defined metrics can be used for quantitative performance assessment of multifiocus fused images. The results of the proposed algorithm are compared to some existing image fusion methods.
Median-based post-processing for VQ image transmission over noisy channels

For image transmission over noisy channels, the VQ paradigm is a strong competitor. We propose a post-processing scheme that is able to improve the perceptual quality of the VQ reconstructed image, for various types of noisy channels, when it is combined with an appropriate index assignment. For binary symmetric channels, we use the index assignment resulting from the Linde, Buzo and Gray (1980) LBG design of the codebook, which ensures a low channel distortion. For the more challenging case of memory channels we consider index assignments specifically designed. We show that a significant perceptual improvement can be achieved with our post-processing scheme both for memoryless and memory channels.
Iterative blocking artefact reduction based on local contrast information

Block based transform coding is the most popular technique for image and video compression. However, images compressed using block based algorithms exhibit highly visible blocking artifacts. We propose an iterative algorithm to reduce these artifacts using a map computed by summation of the horizontal and vertical profiles of the gradient vector magnitude. This map is then used as an input to a recursive filter to reduce the blocking effect. The results of the proposed method have been compared to some recent and efficient deblocking algorithms. Experimental results show that the proposed method is simple and yields a subjective quality that is nearly free of blocking artifacts.