nils-herrmann/clean_scirepeval huggingface dataset:
Objective perceptual evaluation of halftoning using image quality metrics

Many color and gray-tone image quality metrics have been developed during the last decades. Unfortunately, these metrics could not be directly applied to halftone images. They failed to reliably estimate the distortion resulting from the halftoning process. Here, we propose to adapt some well-known image quality metrics to evaluate halftone images. One of the most important and primary characteristics of the Human Visual System is its optical lowpass filtering effect which transforms the halftoned images into perceptually continuous images. Therefore, we focus our study on the design of the HVS lowpass filter. It is demonstrated through theoretical developments and experimental studies that the Gaussian filter is a good candidate for mimicking the perceptual lowpass filter. It is then shown that by incorporating this filter in the design of some image quality metrics, a comprehensive and relevant comparison of halftoning techniques could be achieved.
Degraded color document image enhancement based on NRCIR

An automatic algorithm of degraded color document image enhancement is proposed based on our previous work on Natural Rendering of Color Image using Retinex (NRCIR) with respects to document image characteristics. In the proposed work, an adaptive workflow is designed to enhance both luminance and chrominance contrast of document image while maintaining degradations within tolerance and hue-shift minimized. Tests with degraded document image databases are effectuated, and the results show an encouraging performance of the proposed method.
A critical look to some contrast enhancement evaluation measures

Contrast enhancement is one of the most studied problems in image processing. A plethora of approaches has been proposed in the literature for image enhancement since the pioneer work of Kovasznay and Joseph in 1955  and the famous contribution of Gabor in 1965 on image deblurring . However, very few works have been dedicated to contrast enhancement evaluation. This is mainly due to the fact that image enhancement is primarily related to subjective aspects of human perceptual vision. The intent of this paper is to provide a first critical review to some contrast enhancement evaluation measures and propose a new one. An objective comparison of these measures on various color real images processed by some neighborhood based methods is provided. This work is considered as a first step towards the development of a unifying framework for image enhancement evaluation. This could be also used to control the side effect that may result from any image enhancement such as contrast enhancement, denoising, tone mapping and other similar image processing tools.
Fingerprint Identification using Radon Transform

In this paper, a new method for fingerprint identification based on Radon transform is proposed as an alternative to minutiae-based approaches. Since existing methods based on these local characteristics "minutiae" have attained a level of success rate saturation, this paper aims to open a new gateway by proposing a different approach for analyzing fingerprints. This approach offers a fine analysis from global characteristics via the generated Radon profiles. The basic idea is to exploit the directional properties of Radon transform in order to extract new global features from fingerprint images, which could be considered as a set of oriented patterns. A global structural similarity based on these directional features is then derived. This new approach does not necessitate any pre-processing and treatments as in classical fingerprint identification methods. To evaluate the performance of the proposed method, some fingerprints images are used. The obtained results confirm the efficiency of the method in terms of flexibility, simplicity and quality of identification.
A new image quality estimation approach for JPEG2000 compressed images

No Reference (NR) Image Quality Metrics (IQMs) for JPEG2000 compressed images consider generally the ringing effect as the dominant degradation. However, for a certain bite rate, blur degradation appears and becomes the most annoying. We propose here to estimate the quality of JPEG2000 compressed images by first identifying the dominant distortion and then selecting the appropriate metric (blur or ringing). The degradation identification is here realized using an Artificial Neural Networks (ANN). The performances of the proposed method are evaluated in terms of classification accuracy and correlation with subjective scores.
On the analysis of background subtraction techniques using Gaussian Mixture Models

In this paper, we conduct an investigation into background subtraction techniques using Gaussian Mixture Models (GMM) in the presence of large illumination changes and background variations. We show that the techniques used to date suffer from the trade-off imposed by the use of a common learning rate to update both the mean and variance of the component densities, which leads to a degeneracy of the variance and creates “saturated pixels”. To address this problem, we propose a simple yet effective technique that differentiates between the two learning rates, and imposes a constraint on the variance so as to avoid the degeneracy problem. Experimental results are presented which show that, compared to existing techniques, the proposed algorithm provides more robust segmentation in the presence of illumination variations and abrupt changes in background distribution.
A new image smoothing method based on a simple model of spatial processing in the early stages of human vision

The difficulty of preserving edges is central to the problem of smoothing images. The main problem is that of distinguishing between meaningful contours and noise, so that the image can be smoothed without loss of details. Substantial efforts have been devoted to solving this difficult problem, and a plethora of filtering methods have been proposed in the literature. Non-linear filters have proved to be more efficient than their linear counterparts. Here, a new nonlinear filter for noise smoothing is introduced. This filter is based on the psychophysical phenomenon of human visual contrast sensitivity. Results on real images are presented to demonstrate the validity of our approach compared to other known filtering methods.
Color Image Assessment Using Spatial Extension to CIE DE2000

A full-reference method of color image quality assessment using a spatial extension of CIE DE2000 and cortex transform decomposition is proposed. Our test shows that CIE DE2000 tends to overestimate the visibility of distortion without emphasizing structural information due to lack of spatial vision consideration, whereas visible difference predictor shows also its insufficiency in predicting relatively smaller differences. Inspired by VDP and previous S-CIELAB works, we try to extract the most important visible differences using the proposed approach, and the map of distortion resulted shows a better correlation to the subjective qualities.
Combining and selecting indicators for image quality assesment

Image quality assessment is an important component in every image processing system where the last link of the chain is the human observer. This domain is of increasing interest, in particular in the context of image compression where coding scheme optimization is based on the distortion measure. Many objective image quality measures have been proposed in the literature and validated by comparing them to the Mean Opinion Score (MOS). We propose in this paper an empirical study of several indicators and show how one can improve the performances by combining them. We learn a regularized regression model and apply variable selection techniques to automatically find the most relevant indicators. Our technique enhances the state of the art results on two publicly available databases.
Multi-line fitting using polynomial phase transforms and downsampling

A new signal processing method is developed for solving the multiline fitting problem in a two dimensional image. We first reformulate the former problem in a special parameter estimation framework such that a first order or a second order polynomial phase signal structure is obtained. Then, the previously developed algorithms in that formalism (and particularly the downsampling technique for high resolution frequency estimation) can be exploited to produce accurate estimates for line parameters. This method is able to estimate the parameters of parallel lines with different offsets and handles the quantization noise effect which can not be done by the sensor array processing technique introduced by Aghajan et al. (1993). Simulation results are presented to demonstrate the usefulness of the proposed method.
Detection and restoration of color-timing echo artifact for HD digital cinema films

A novel method for automatically detecting and restoring the color timing echo artifact which may result in the internegative/interpositive printing process is proposed. This degradation appears at shoot change, either on the top of the last frame of a shot or on the bottom of the subsequent frame of the next shot. In our work, the degraded region is firstly detected and then restored by using the histogram specification (HS) based method. To avoid discontinuities between the restored region and the remaining of the treated frame due to abrupt modification in a large region, the degraded zone is decomposed into several overlapped small blocks. Then a block matching algorithm is used and a block similarity metric based on ordering has also been proposed for motion estimation under luminance variation. The proposed method is tested on several color high definition (HD) films with highly promising results.
Spatio-temporal salient feature extraction for perceptual content based video retrieval

Video retrieval performance depends on many factors that may impact the output results in some respects. Among these factors, the selected features and the similarity function play prominent roles in the retrieval process. In this paper we propose a feature selection (FS) technique for content based video retrieval (CBVR). This scheme consists of several steps. First, the salient objects within video sequence are extracted through a segmentation process. These objects are described by spatio-temporal normalized features. Finally, during the query procedure, the derived features are compared to the recorded features database using Hausdorff distance matching. This study is carried out on a news video database. The performance of the proposed scheme in terms of recall and precision is evaluated and compared to existing algorithms. The experimental results clearly demonstrate that the proposed features are more accurate and robust for CBVR, than the basic spatio-temporal features.
A universal Full Reference image Quality Metric based on a neural fusion approach

We present in this paper a new global Full-Reference (FR) image quality metric (IQM) based on the fusion of several conventional FR metrics using an ANN learning algorithm. The fusion is shown to result in improved performance compared to individual FR metrics. Indeed, existing FR metrics can provide excellent results for specific degradations but poor results for others. Here, we propose to overcome this limitation by first improving the performance of existing FR metrics across different degradations through a ranking process. Then, using an Artificial Neural Network, we fuse the best-performing measures into a single metric called Global Index Quality Metric (G-IQM). The experimental results using the TID 2008 image database demonstrate that this new G-IQM metric achieves consistent image quality evaluation results with subjective evaluation.
Efficient Inter-View Bit Allocation Methods for Stereo Image Coding

In this paper, we present efficient bit allocation methods for stereo image coding purpose. Since the common idea behind most of the existing stereo compression schemes consists of encoding a reference and residual images as well as a disparity map, we mainly focus on the bit allocation issue between the reference and residual images. Generally, this problem is solved in an empirical manner by looking for the optimal rates leading to the minimum distortion value. Thanks to recent approximations of the entropy and distortion functions, we propose accurate and fast bit allocation schemes appropriate for the open-loop- and closed-loop-based stereo coding structures. Experimental results show the benefits which can be drawn from the proposed bit allocation methods.
A New Interpretation and improvement of the Nonlinear Anisotropic Diffusion for Image Enhancement

The purpose of the article is to give an analysis of the anisotropic diffusion (AD) and propose adaptive nonlinear filtering based on a judicious choice of the conductance function (CF) and the edgeness threshold. A new undesirable effect, which we call the "pinhole effect" may result when AD is introduced for the first time. A robust solution to this effect is proposed and evaluated through experimental data. The evolution of the diffused signal is analyzed through a physical model using the optical flow technique (OFT). The overall strategy is evaluated through experimental results obtained on synthetic and actual images.
Color Mismatch Compensation Method Based on a Physical Model

A new method for detecting and correcting color-timing mismatch in digital film is proposed. This method is based on a physical model which accounts for the absorption of light by the different layers of the film. The whole process consists of five sequential stages: shot change detection, registration, degraded zone detection, and unreliable motion detection and correction. One of the main issues in this paper is how to align the degraded and reference frames in the case of varying illumination conditions. A new transformation, based on the proposed physical model, is derived to make the data term of the optical flow framework robust to color change. The degraded regions are then detected and restored by using the computed flow field. The performance of the proposed method is evaluated through extensive tests on both simulated and real high definition films. The obtained results are very promising and confirm the efficiency of the proposed method.
Blind multichannel image deconvolution with regularization

Blind image deconvolution is a very active research field. It has been proved to be useful in many situations related with image processing: microscopy imaging, remote sensing and astronomical imaging. Several methods were developed in a single-channel framework. Unfortunately, these methods encounter some problems even in noiseless case. Recently, a Mutually Referenced Equalizers (MRE) method was proposed in a multichannel framework which ensures perfect Point Spread Functions (PSF) recovery in the noise free case. In this paper we propose to extend the MRE method to the noisy case by means of a regularization procedure. We also evaluate the restoration performance using an objective image quality assessment measure.
Perceptual Watermarking Using Pyramidal JND Maps

A new pyramidal JND (just noticeable difference) model is presented. The idea is to use this JND to determine the optimum strength for embedding the watermark providing an invisible and robust watermarking scheme. The image is first decomposed into a multiresolution representation using the pyramidal decomposition. Then, a perceptual model is proposed to compute the JND value for each pixel at each Laplacian level. This model takes into account three main characteristics of the human visual system (HVS), namely: contrast sensitivity, luminance adaptation and contrast masking. The performance of the proposed technique is evaluated in terms of transparency, using subjective and objective tests, and robustness to different common attacks.
A new scheme for no reference image quality assessment

In this paper, we propose to overcome one of the limitations of No Reference (NR) Image Quality Metrics (IQMs). Indeed, this kind of metrics is generally distortion-based and can be used only for a specific degradation such as ringing, blur or blocking. We propose to detect and identify the type of the degradation contained in the image before quantifying its quality. The degradation type is here identified using a Linear Discriminant Analysis (LDA) classifier. Then, the NR-IQM is selected according to the degradation type. We focus our work on the more common artefacts and degradations: blocking, ringing, blur and noise. The efficiency of the proposed method is evaluated in terms of correct classification across the considered degradations and artefacts.
Comparative study of endoscopic image enhancement techniques

Endoscopic image enhancement has become a popular research area after the success of minimally invasive interventions and the innovation of new technological diagnosis tools such as the wireless capsule endoscopy. In spite of the great advances achieved in image processing and enhancement, only few techniques can be adapted for endoscopic images. This can be explained by the specifications of the acquisition process and the special characteristics of the endoscopic environment. This paper presents a comparative study of image enhancement techniques for endoscopic images. We propose then a new enhancement process producing better results in terms of endoscopic stereo matching and wireless endoscopy images classification.
A perceptual pyramidal watermarking technique

This paper presents a new perceptual image watermarking scheme based on the Laplacian pyramid (LP) decomposition and a visibility map constructed using a human visual contrast model. This map is computed of each level of the pyramid in order to determine the auspicious regions for embedding the watermark. The spread spectrum technique is used to embed the watermark in some levels of the LP. The watermarked image is then constructed from the Laplacian images. The algorithm performances are evaluated in terms of watermark invisibility, using an objective image quality perceptual measure, the structural similarity index measure (SSIM), and robustness to different attacks of Stirmark such as Jpeg compression, low-pass filtering, additive noise and cropping.
Trajectory feature fusion for human action recognition

This paper addresses the problem of human action detection/recognition by investigating interest points (IP) trajectory cues and by reducing undesirable small camera motion. We first detect speed up robust feature (SURF) to segment video into frame volume (FV) that contains small actions. This segmentation relies on IP trajectory tracking. Then, for each FV, we extract optical flow of every detected SURF. Finally, a parametrization of the optical flow leads to displacement segments. These features are concatenated into a trajectory feature in order to describe the trajectory of IP upon a FV. We reduce the impact of camera motion by considering moving IPs beyond a minimum motion angle and by using motion boundary histogram (MBH). Feature-fusion based action recognition is performed to generate robust and discriminative codebook using K-mean clustering. We employ a bag-of-visual-words Support Vector Machine (SVM) approach for the learning /testing step. Through an extensive experimental evaluation carried out on the challenging UCF sports datasets, we show the efficiency of the proposed method by achieving 83.5% of accuracy.
Image quality measure based on curvelet transform

This paper presents a new image quality assessment based on the curvelet decomposition. The curvelet transform is introduced for its ability to represent the curves of images at different scales and directions. Unlike existing methods, the proposed image quality assessment takes into account the geometrical content of the image at different scales and directions. The proposed measure has been tested on LIVE database and was compared to other image quality assessments. The obtained results expressed in terms of correlation with subjective evaluation on the database demonstrate the efficiency of the proposed image quality metric.
Stereo image quality assessment using a binocular just noticeable difference model

This paper presents a novel full-reference Stereo Image Quality Assessment (SIQA) measure based on well understood characteristics of the human visual system (HVS), namely contrast sensitivity and frequency and directional selectivity. Additionally, the proposed metric takes into account the stereo interplay between the two views, where one view may affect our perception of the overall quality of the stereo image pair. Therefore, a Binocular Just Noticeable Difference (BJND) model is used to compute the distortion visibility threshold, and the binocular suppression theory is considered in the proposed metric. The scored 3D LIVE IQA database is used to evaluate the correlation of the proposed metric with the DMOS subjective score provided by the database. The obtained experimental results show that the proposed metric correlates much better with the DMOS score than the state-of-the-art metrics do.
Statistical Modeling of Image Degradation Based on Quality Metrics

A plethora of Image Quality Metrics (IQM) has been proposed during the last two decades. However, at present time, there is no accepted IQM able to predict the perceptual level of image degradation across different types of visual distortions. Some measures are more adapted for a set of degradations but inefficient for others. Indeed, the efficiency of any IQM has been shown to depend upon the type of degradation. Thus, we propose here a new approach for predicting the type of degradation before using IQMs. The basic idea is first to identify the type of distortion using a Bayesian approach, then select the most appropriate IQM for estimating image quality for that specific type of distortion. The performance of the proposed method is evaluated in terms of classification accuracy across different types of degradations.
Vector quantization with edge reconstruction

This paper proposes a vector quantization (VQ) scheme that improves the quality of the reconstructed image by correcting quantization artifacts. The basic idea of the coding scheme is to treat separately the blocks containing edges (edge blocks), as they contain important perceptual information. The edge blocks are identified using an edge detection algorithm and are labeled by an escape index, and their reconstruction is done via an interpolation procedure that exploits the spatial correlation in the image. The remaining blocks (smooth blocks) are coded using standard VQ.
A new approach for spatial audio quality assessment

The rising interest toward multichannel audio systems capable of conveying and creating spatial and immersive audio scenes puts focus on how the quality of such systems should be evaluated. Through the study of the existing audio quality assessment techniques, we highlight the missing aspects regarding the analysis of spatial cues, and we introduce our approach to resolve that using a natural, inexpensive and fast gesture-based technique for subjective assessment, built around the Kinect sensor. The results are quite satisfying with an overall accuracy of less than 5°, and enable us to establish more reliable subjective and objective methods. A benchmark of some well-known HRTF databases using this protocol is presented to illustrate the usefulness and efficiency of this approach.
An HVS-inspired approach for image segmentation evaluation

A new approach for evaluating image segmentation methods is proposed. It exploits some image quality assessment measures inspired from the human visual system (HVS) mechanisms. The main idea is to consider image segmentation as a coding process. This approach is used for evaluating some grey-level thresholding methods. It could be easily extended to the evaluation of region-based segmentation methods. The obtained results confirm that this new approach is very promising and open new methodology for image segmentation evaluation.