Showing papers on "Image quality published in 2011"

FSIM: A Feature Similarity Index for Image Quality Assessment

Abstract: Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.

Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality

Abstract: Our approach to blind image quality assessment (IQA) is based on the hypothesis that natural scenes possess certain statistical properties which are altered in the presence of distortion, rendering them un-natural; and that by characterizing this un-naturalness using scene statistics, one can identify the distortion afflicting the image and perform no-reference (NR) IQA. Based on this theory, we propose an (NR)/blind algorithm-the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index-that assesses the quality of a distorted image without need for a reference image. DIIVINE is based on a 2-stage framework involving distortion identification followed by distortion-specific quality assessment. DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature. DIIVINE is based on natural scene statistics which govern the behavior of natural images. In this paper, we detail the principles underlying DIIVINE, the statistical features extracted and their relevance to perception and thoroughly evaluate the algorithm on the popular LIVE IQA database. Further, we compare the performance of DIIVINE against leading full-reference (FR) IQA algorithms and demonstrate that DIIVINE is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM). A software release of DIIVINE has been made available online: http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation.

Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

Abstract: As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of the l1-norm optimization techniques and the fact that natural images are intrinsically sparse in some domains. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a precollected dataset of example image patches, and then, for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image nonlocal self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.

Information Content Weighting for Perceptual Image Quality Assessment

Abstract: Many state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage structure: local quality/distortion measurement followed by pooling. While significant progress has been made in measuring local image quality/distortion, the pooling stage is often done in ad-hoc ways, lacking theoretical principles and reliable computational models. This paper aims to test the hypothesis that when viewing natural images, the optimal perceptual weights for pooling should be proportional to local information content, which can be estimated in units of bit using advanced statistical models of natural images. Our extensive studies based upon six publicly-available subject-rated image databases concluded with three useful findings. First, information content weighting leads to consistent improvement in the performance of IQA algorithms. Second, surprisingly, with information content weighting, even the widely criticized peak signal-to-noise-ratio can be converted to a competitive perceptual quality measure when compared with state-of-the-art algorithms. Third, the best overall performance is achieved by combining information content weighting with multiscale structural similarity measures.

HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions

Abstract: Visual metrics can play an important role in the evaluation of novel lighting, rendering, and imaging algorithms. Unfortunately, current metrics only work well for narrow intensity ranges, and do not correlate well with experimental data outside these ranges. To address these issues, we propose a visual metric for predicting visibility (discrimination) and quality (mean-opinion-score). The metric is based on a new visual model for all luminance conditions, which has been derived from new contrast sensitivity measurements. The model is calibrated and validated against several contrast discrimination data sets, and image quality databases (LIVE and TID2008). The visibility metric is shown to provide much improved predictions as compared to the original HDR-VDP and VDP metrics, especially for low luminance conditions. The image quality predictions are comparable to or better than for the MS-SSIM, which is considered one of the most successful quality metrics. The code of the proposed metric is available on-line.

Second order total generalized variation (TGV) for MRI

Abstract: Total variation was recently introduced in many different magnetic resonance imaging applications. The assumption of total variation is that images consist of areas, which are piecewise constant. However, in many practical magnetic resonance imaging situations, this assumption is not valid due to the inhomogeneities of the exciting B1 field and the receive coils. This work introduces the new concept of total generalized variation for magnetic resonance imaging, a new mathematical framework, which is a generalization of the total variation theory and which eliminates these restrictions. Two important applications are considered in this article, image denoising and image reconstruction from undersampled radial data sets with multiple coils. Apart from simulations, experimental results from in vivo measurements are presented where total generalized variation yielded improved image quality over conventional total variation in all cases.

Blind contrast enhancement assessment by gradient ratioing at visible edges

Abstract: The contrast of outdoor images acquired under adverse weather conditions, especially foggy weather, is altered by the scattering of daylight by atmospheric particles. As a consequence, different methods have been designed to restore the contrast of these images. However, there is a lack of methodology to assess the performances of the methods or to rate them. Unlike image quality assessment or image restoration areas, there is no easy way to have a reference image, which makes the problem not straightforward to solve. In this paper, an approach is proposed which consists in computing the ratio between the gradient of the visible edges between the image before and after contrast restoration. In this way, an indicator of visibility enhancement is provided based on the concept of visibility level, commonly used in lighting engineering. Finally, the methodology is applied to contrast enhancement assessment and to the comparison of tone-mapping operators.

High quality depth map upsampling for 3D-TOF cameras

Abstract: This paper describes an application framework to perform high quality upsampling on depth maps captured from a low-resolution and noisy 3D time-of-flight (3D-ToF) camera that has been coupled with a high-resolution RGB camera. Our framework is inspired by recent work that uses nonlocal means filtering to regularize depth maps in order to maintain fine detail and structure. Our framework extends this regularization with an additional edge weighting scheme based on several image features based on the additional high-resolution RGB input. Quantitative and qualitative results show that our method outperforms existing approaches for 3D-ToF upsampling. We describe the complete process for this system, including device calibration, scene warping for input alignment, and even how the results can be further processed using simple user markup.

Metal artifact reduction by dual energy computed tomography using monoenergetic extrapolation

Abstract: The aim of the study was to assess the performance and diagnostic value of a dual energy CT approach to reduce metal artefacts in subjects with metallic implants. 31 patients were examined in the area of their metallic implants using a dual energy CT protocol (filtered 140 kVp and 100 kVp spectrum, tube current relation: 3:1). Specific post-processing was applied to generate energies of standard 120 and 140 kVp spectra as well as a filtered 140 kVp spectrum with mean photon energies of 64, 69 and 88 keV, respectively, and an optimized hard spectrum of 95–150 keV. Image quality and diagnostic value were subjectively and objectively determined. Image quality was rated superior to the standard image in 29/31 high energy reconstructions; the diagnostic value was rated superior in 27 patients. Image quality and diagnostic value scores improved significantly from 3.5 to 2.1 and from 3.6 to 1.9, respectively. In several exams decisive diagnostic features were only discernible in the high energy reconstructions. The density of the artefacts decreased from −882 to −341 HU. Dual Energy CT with specific postprocessing can reduce metal artefacts and may significantly enhance diagnostic value in the evaluation of metallic implants.

Image Compression Using Block Truncation Coding

Abstract: The present work investigates image compression using block truncation coding. Two algorithms were selected namely, the original block truncation coding (BTC) and Absolute Moment block truncation coding (AMBTC) and a comparative study was performed. Both of two techniques rely on applying divided image into non overlapping blocks. They differ in the way of selecting the quantization level in order to remove redundancy. Objectives measures were used to evaluate the image quality such as: Peak Signal to Noise Ratio (PSNR), Weighted Peak Signal to Noise Ratio (WPSNR), Bit Rate (BR) and Structural Similarity Index (SSIM).The results have shown that the ATBTC algorithm outperforms the BTC. It has been show that the image compression using AMBTC provides better image quality than image compression using BTC at the same bit rate. Moreover, the AMBTC is quite faster compared to BTC Index Terms—BTC, AMBTC, WPSNR, SSIM.

Virtual monochromatic spectral imaging with fast kilovoltage switching: Improved image quality as compared with that obtained with conventional 120-kVp CT

Abstract: For a given radiation dose, virtual monochromatic spectral images at approximately 70 keV reconstructed from split 80- and 140-kVp data showed less image noise and a higher contrast-to-noise ratio than did 120-kVp CT images.

What makes an image memorable

Abstract: When glancing at a magazine, or browsing the Internet, we are continuously being exposed to photographs. Despite of this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stitch to our minds, and other are forgotten. In this paper we focus on the problem of predicting how memorable an image will be. We show that memorability is a stable property of an image that is shared across different viewers. We introduce a database for which we have measured the probability that each picture will be remembered after a single view. We analyze image features and labels that contribute to making an image memorable, and we train a predictor based on global image descriptors. We find that predicting image memorability is a task that can be addressed with current computer vision techniques. Whereas making memorable images is a challenging task in visualization and photography, this work is a first attempt to quantify this useful quality of images.

IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition

Abstract: In this correspondence, the authors propose an image resolution enhancement technique based on interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform (SWT). DWT is applied in order to decompose an input image into different subbands. Then the high frequency subbands as well as the input image are interpolated. The estimated high frequency subbands are being modified by using high frequency subband obtained through SWT. Then all these subbands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative and visual results are showing the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition

Abstract: In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition performance. While one solution it to use only the ‘best’ of images, current face selection techniques are incapable of simultaneously handling all of the abovementioned issues. We propose an efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an ‘ideal’ face. Image characteristics that affect recognition are taken into account, including variations in geometric alignment (shift, rotation and scale), sharpness, head pose and cast shadows. Experiments on FERET and PIE datasets show that the proposed algorithm is able to identify images which are simultaneously the most frontal, aligned, sharp and well illuminated. Further experiments on a new video surveillance dataset (termed ChokePoint) show that the proposed method provides better face subsets than existing face selection techniques, leading to significant improvements in recognition accuracy.

Focus on time-of-flight PET: the benefits of improved time resolution.

Abstract: TOF PET is characterized by a better trade-off between contrast and noise in the image. This property is enhanced in more challenging operating conditions, allowing for example shorter examinations or low counts, successful scanning of larger patients, low uptake, visualization of smaller lesions, and incomplete data sampling. In this paper, the correlation between the time resolution of a TOF PET scanner and the improvement in signal-to-noise in the image is introduced and discussed. A set of performance advantages is presented which include better image quality, shorter scan times, lower dose, higher spatial resolution, lower sensitivity to inconsistent data, and the opportunity for new architectures with missing angles. The recent scientific literature that reports the first experimental evidence of such advantages in oncology clinical data is reviewed. Finally, the directions for possible improvement of the time resolution of the present generation of TOF PET scanners are discussed.

Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement

Abstract: In this paper, we propose a novel generic image prior-gradient profile prior, which implies the prior knowledge of natural image gradients. In this prior, the image gradients are represented by gradient profiles, which are 1-D profiles of gradient magnitudes perpendicular to image structures. We model the gradient profiles by a parametric gradient profile model. Using this model, the prior knowledge of the gradient profiles are learned from a large collection of natural images, which are called gradient profile prior. Based on this prior, we propose a gradient field transformation to constrain the gradient fields of the high resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. With this simple but very effective approach, we are able to produce state-of-the-art results. The reconstructed high resolution images or the enhanced images are sharp while have rare ringing or jaggy artifacts.

Blind Image Quality Assessment Using a General Regression Neural Network

Abstract: We develop a no-reference image quality assessment (QA) algorithm that deploys a general regression neural network (GRNN). The new algorithm is trained on and successfully assesses image quality, relative to human subjectivity, across a range of distortion types. The features deployed for QA include the mean value of phase congruency image, the entropy of phase congruency image, the entropy of the distorted image, and the gradient of the distorted image. Image quality estimation is accomplished by approximating the functional relationship between these features and subjective mean opinion scores using a GRNN. Our experimental results show that the new method accords closely with human subjective judgment.

Fast Model-Based X-Ray CT Reconstruction Using Spatially Nonhomogeneous ICD Optimization

Abstract: Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed The NH-ICD algorithm has a mechanism that adaptively selects voxels for update First, a voxel selection criterion VSC determines the voxels in greatest need of update Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence In order to speed up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm We examine the performance of the proposed algorithm using several clinical data sets of various anatomy The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries

Three-Dimensional SAR Focusing From Multipass Signals Using Compressive Sampling

Abstract: Three-dimensional synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. It is based on multipass SAR data obtained usually by nonuniformly spaced acquisition orbits. A common 3-D SAR focusing approach is Fourier-based SAR tomography, but this technique brings about image quality problems because of the low number of acquisitions and their not regular spacing. Moreover, attained resolution in elevation is limited by the overall acquisitions baseline extent. In this paper, a novel 3-D SAR data imaging based on Compressive Sampling theory is presented. It is shown that since the image to be focused has usually a sparse representation along the elevation direction (i.e., only few scatterers with different elevation are present in the same range-azimuth resolution cell), it suffices to have a small number of measurements to construct the 3-D image. Furthermore, the method allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span. Tomographic imaging is performed by solving an optimization problem which enforces sparsity through l1-norm minimization. Numerical results on simulated and real data validate the method and have been compared with the truncated singular value decomposition technique.

Coronary CT angiography: image quality, diagnostic accuracy, and potential for radiation dose reduction using a novel iterative image reconstruction technique—comparison with traditional filtered back projection

Abstract: To compare image noise, image quality and diagnostic accuracy of coronary CT angiography (cCTA) using a novel iterative reconstruction algorithm versus traditional filtered back projection (FBP) and to estimate the potential for radiation dose savings. Sixty five consecutive patients (48 men; 59.3 ± 7.7 years) prospectively underwent cCTA and coronary catheter angiography (CCA). Full radiation dose data, using all projections, were reconstructed with FBP. To simulate image acquisition at half the radiation dose, 50% of the projections were discarded from the raw data. The resulting half-dose data were reconstructed with sinogram-affirmed iterative reconstruction (SAFIRE). Full-dose FBP and half-dose iterative reconstructions were compared with regard to image noise and image quality, and their respective accuracy for stenosis detection was compared against CCA. Compared with full-dose FBP, half-dose iterative reconstructions showed significantly (p = 0.001 – p = 0.025) lower image noise and slightly higher image quality. Iterative reconstruction improved the accuracy of stenosis detection compared with FBP (per-patient: accuracy 96.9% vs. 93.8%, sensitivity 100% vs. 100%, specificity 94.6% vs. 89.2%, NPV 100% vs. 100%, PPV 93.3% vs. 87.5%). Iterative reconstruction significantly reduces image noise without loss of diagnostic information and holds the potential for substantial radiation dose reduction from cCTA.

Learning a blind measure of perceptual image quality

Abstract: It is often desirable to evaluate an image based on its quality. For many computer vision applications, a perceptually meaningful measure is the most relevant for evaluation; however, most commonly used measure do not map well to human judgements of image quality. A further complication of many existing image measure is that they require a reference image, which is often not available in practice. In this paper, we present a “blind” image quality measure, where potentially neither the groundtruth image nor the degradation process are known. Our method uses a set of novel low-level image features in a machine learning framework to learn a mapping from these features to subjective image quality scores. The image quality features stem from natural image measure and texture statistics. Experiments on a standard image quality benchmark dataset shows that our method outperforms the current state of art.

Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data

Abstract: Since the human visual system (HVS) is the ultimate assessor of image quality, current research on the design of objective image quality metrics tends to include an important feature of the HVS, namely, visual attention. Different metrics for image quality prediction have been extended with a computational model of visual attention, but the resulting gain in reliability of the metrics so far was variable. To better understand the basic added value of including visual attention in the design of objective metrics, we used measured data of visual attention. To this end, we performed two eye-tracking experiments: one with a free-looking task and one with a quality assessment task. In the first experiment, 20 observers looked freely to 29 unimpaired original images, yielding us so-called natural scene saliency (NSS). In the second experiment, 20 different observers assessed the quality of distorted versions of the original images. The resulting saliency maps showed some differences with the NSS, and therefore, we applied both types of saliency to four different objective metrics predicting the quality of JPEG compressed images. For both types of saliency the performance gain of the metrics improved, but to a larger extent when adding the NSS. As a consequence, we further integrated NSS in several state-of-the-art quality metrics, including three full-reference metrics and two no-reference metrics, and evaluated their prediction performance for a larger set of distortions. By doing so, we evaluated whether and to what extent the addition of NSS is beneficial to objective quality prediction in general terms. In addition, we address some practical issues in the design of an attention-based metric. The eye-tracking data are made available to the research community .

Parallel MR Image Reconstruction Using Augmented Lagrangian Methods

Abstract: Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE) requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity-based regularization criteria can improve image quality, but they demand computation-intensive nonlinear optimization. In this paper, we present novel methods for regularized MRI reconstruction from undersampled sensitivity encoded data-SENSE-reconstruction-using the augmented Lagrangian (AL) framework for solving large-scale constrained optimization problems. We first formulate regularized SENSE-reconstruction as an unconstrained optimization task and then convert it to a set of (equivalent) constrained problems using variable splitting. We then attack these constrained versions in an AL framework using an alternating minimization method, leading to algorithms that can be implemented easily. The proposed methods are applicable to a general class of regularizers that includes popular edge-preserving (e.g., total-variation) and sparsity-promoting (e.g., -norm of wavelet coefficients) criteria and combinations thereof. Numerical experiments with synthetic and in vivo human data illustrate that the proposed AL algorithms converge faster than both general-purpose optimization algorithms such as nonlinear conjugate gradient (NCG) and state-of-the-art MFISTA.

Raw data-based iterative reconstruction in body CTA: evaluation of radiation dose saving potential.

Abstract: To evaluate prospectively, in patients undergoing body CTA, the radiation dose saving potential of raw data-based iterative reconstruction as compared to filtered back projection (FBP). Twenty-five patients underwent thoraco-abdominal CTA with 128-slice dual-source CT, operating both tubes at 120 kV. Full-dose (FD) images were reconstructed with FBP and were compared to half-dose (HD) images with FBP and HD-images with sinogram-affirmed iterative reconstruction (SAFIRE), both reconstructed using data from only one tube-detector-system. Image quality and sharpness of the aortic contour were assessed. Vessel attenuation and noise were measured, contrast-to-noise-ratio was calculated. Noise as image quality deteriorating artefact occurred in 24/25 (96%) HD-FBP but not in FD-FBP and HD-raw data-based iterative reconstruction datasets (p 0.05). Lowest noise was found for HD-raw data-based iterative reconstruction (7.23HU), being 9.4% lower than that in FD-FBP (7.98HU, p 50% while maintaining image quality. Key Points • Raw data-based iterative reconstruction reduces image noise and improves image quality as compared to filtered back projection • At a similar radiation dose, raw data-based iterative reconstruction improves the sharpness of vessel contours • In body CTA a dose reduction of >50% might be possible when using raw data-based iterative reconstructions, while image quality can be maintained

Chest computed tomography using iterative reconstruction vs filtered back projection (Part 2): image quality of low-dose CT examinations in 80 patients.

Abstract: To evaluate the image quality of an iterative reconstruction algorithm (IRIS) in low-dose chest CT in comparison with standard-dose filtered back projection (FBP) CT. Eighty consecutive patients referred for a follow-up chest CT examination of the chest, underwent a low-dose CT examination (Group 2) in similar technical conditions to those of the initial examination, (Group 1) except for the milliamperage selection and the replacement of regular FBP reconstruction by iterative reconstructions using three (Group 2a) and five iterations (Group 2b). Despite a mean decrease of 35.5% in the dose-length-product, there was no statistically significant difference between Group 2a and Group 1 in the objective noise, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios and distribution of the overall image quality scores. Compared to Group 1, objective image noise in Group 2b was significantly reduced with increased SNR and CNR and a trend towards improved image quality. Iterative reconstructions using three iterations provide similar image quality compared with the conventionally used FBP reconstruction at 35% less dose, thus enabling dose reduction without loss of diagnostic information. According to our preliminary results, even higher dose reductions than 35% may be feasible by using more than three iterations.

Computational versus Psychophysical Bottom-Up Image Saliency: A Comparative Evaluation Study

Abstract: The predictions of 13 computational bottom-up saliency models and a newly introduced Multiscale Contrast Conspicuity (MCC) metric are compared with human visual conspicuity measurements. The agreement between human visual conspicuity estimates and model saliency predictions is quantified through their rank order correlation. The maximum of the computational saliency value over the target support area correlates most strongly with visual conspicuity for 12 of the 13 models. A simple multiscale contrast model and the MCC metric both yield the largest correlation with human visual target conspicuity (>;0.84). Local image saliency largely determines human visual inspection and interpretation of static and dynamic scenes. Computational saliency models therefore have a wide range of important applications, like adaptive content delivery, region-of-interest-based image compression, video summarization, progressive image transmission, image segmentation, image quality assessment, object recognition, and content-aware image scaling. However, current bottom-up saliency models do not incorporate important visual effects like crowding and lateral interaction. Additional knowledge about the exact nature of the interactions between the mechanisms mediating human visual saliency is required to develop these models further. The MCC metric and its associated psychophysical saliency measurement procedure are useful tools to systematically investigate the relative contribution of different feature dimensions to overall visual target saliency.

Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments

Abstract: In the research field of image processing, mean squared error (MSE) and peak signal-to-noise ratio (PSNR) are extensively adopted as the objective visual quality metrics, mainly because of their simplicity for calculation and optimization. However, it has been well recognized that these pixel-based difference measures correlate poorly with the human perception. Inspired by existing works , in this paper we propose a novel algorithm which separately evaluates detail losses and additive impairments for image quality assessment. The detail loss refers to the loss of useful visual information which affects the content visibility, and the additive impairment represents the redundant visual information whose appearance in the test image will distract viewer's attention from the useful contents causing unpleasant viewing experience. To separate detail losses and additive impairments, a wavelet-domain decoupling algorithm is developed which can be used for a host of distortion types. Two HVS characteristics, i.e., the contrast sensitivity function and the contrast masking effect, are taken into account to approximate the HVS sensitivities. We propose two simple quality measures to correlate detail losses and additive impairments with visual quality, respectively. Based on the findings in that observers judge low-quality images in terms of the ability to interpret the content, the outputs of the two quality measures are adaptively combined to yield the overall quality index. By conducting experiments based on five subjectively-rated image databases, we demonstrate that the proposed metric has a better or similar performance in matching subjective ratings when compared with the state-of-the-art image quality metrics.

Anti-forensics of digital image compression

Abstract: As society has become increasingly reliant upon digital images to communicate visual information, a number of forensic techniques have been developed to verify the authenticity of digital images. Amongst the most successful of these are techniques that make use of an image's compression history and its associated compression fingerprints. Little consideration has been given, however, to anti-forensic techniques capable of fooling forensic algorithms. In this paper, we present a set of anti-forensic techniques designed to remove forensically significant indicators of compression from an image. We do this by first developing a generalized framework for the design of anti-forensic techniques to remove compression fingerprints from an image's transform coefficients. This framework operates by estimating the distribution of an image's transform coefficients before compression, then adding anti-forensic dither to the transform coefficients of a compressed image so that their distribution matches the estimated one. We then use this framework to develop anti-forensic techniques specifically targeted at erasing compression fingerprints left by both JPEG and wavelet-based coders. Additionally, we propose a technique to remove statistical traces of the blocking artifacts left by image compression algorithms that divide an image into segments during processing. Through a series of experiments, we demonstrate that our anti-forensic techniques are capable of removing forensically detectable traces of image compression without significantly impacting an image's visual quality. Furthermore, we show how these techniques can be used to render several forms of image tampering such as double JPEG compression, cut-and-paste image forgery, and image origin falsification undetectable through compression-history-based forensic means.