Image quality assessment based on the space similarity decomposition model

An optimized CNN-based quality assessment model for screen content image

Quality assessment of images with multiple distortions based on phase congruency and gradient magnitude

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The automatic assessment of perceived image quality is crucial in the field of image processing. To achieve this idea, we propose an image quality assessment (IQA) method for blurriness. The features of gradient and singular value were extracted in this method instead of the single feature in the traditional IQA algorithms. According to the insufficient size of existing public image quality assessment datasets to support deep learning, machine learning was introduced to fuse the features of multiple domains, and a new no-reference (NR) IQA method for blurriness denoted Feature fusion IQA(Ffu-IQA) was proposed. The Ffu-IQA uses a probabilistic model to estimate the probability of each edge detection blur in the image, and then uses machine learning to aggregate the probability information to obtain the edge quality score. After that uses the singular value obtained by singular value decomposition of the image matrix to calculate the singular value score. Finally, machine learning pooling is used to obtain the true quality score. Ffu-IQA achieves PLCC scores of 0.9570 and 0.9616 on CSIQ and TID2013, respectively, and SROCC scores of 0.9380 and 0.9531, which are better than most traditional image quality assessment methods for blurriness.

In this paper, we introduce a visual pattern degradation based full-reference (FR) image quality assessment (IQA) method. Researches on visual recognition indicate that the human visual system (HVS) is highly adaptive to extract visual structures for scene understanding. Existing structure degradation based IQA methods mainly take local luminance contrast to represent structure, and measure quality as degradation on luminance contrast. In this paper, we suggest that structure includes not only luminance contrast but also orientation information. Therefore, we analyze the orientation characteristic for structure description. Inspired by the orientation selectivity mechanism in the primary visual cortex, we introduce a novel visual pattern to represent the structure of a local region. Then, the quality is measured as the degradations on both luminance contrast and visual pattern. Experimental results on Five benchmark databases demonstrate that the proposed visual pattern can effectively represent visual structure and the proposed IQA method performs better than the existing IQA metrics.

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.

Based on the characteristics of wavelet coefficients of images and theil inequality coefficient, a novel image quality assessment is proposed in this paper. Firstly, the reference image and the distorted images are decomposed into several levels by means of wavelet transform respectively. Secondly, the approximation and detail coefficients of reference image are the reference sequences and the approximation and detail coefficients of the distorted images are the comparative sequences respectively. And the included angle cosine values are calculated between the reference sequences and the comparative sequences respectively. Moreover, image quality assessment matrix of every distorted image can be constructed based on the theil inequality coefficient values and image quality can be assessed. The algorithm makes full use of perfect integral comparison mechanism of theil inequality coefficient and the well matching of discrete wavelet transform with multichannel model of human visual system. Experimental results show that the proposed algorithm can not only evaluate the integral and detail quality of image fidelity accurately but also bears more consistency with the human visual system than the traditional method PSNR.

Robust structural similarity index measure for images with non-Gaussian distortions

Introduction[18F]fluorodeoxyglucose ([18F]FDG) brain PET is used clinically to detect small areas of decreased uptake associated with epileptogenic lesions, e.g., Focal Cortical Dysplasias (FCD) but its performance is limited due to spatial resolution and low contrast. We aimed to develop a deep learning-based PET image enhancement method using simulated PET to improve lesion visualization.MethodsWe created 210 numerical brain phantoms (MRI segmented into 9 regions) and assigned 10 different plausible activity values (e.g., GM/WM ratios) resulting in 2100 ground truth high quality (GT-HQ) PET phantoms. With a validated Monte-Carlo PET simulator, we then created 2100 simulated standard quality (S-SQ) [18F]FDG scans. We trained a ResNet on 80% of this dataset (10% used for validation) to learn the mapping between S-SQ and GT-HQ PET, outputting a predicted HQ (P-HQ) PET. For the remaining 10%, we assessed Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Squared Error (RMSE) against GT-HQ PET. For GM and WM, we computed recovery coefficients (RC) and coefficient of variation (COV). We also created lesioned GT-HQ phantoms, S-SQ PET and P-HQ PET with simulated small hypometabolic lesions characteristic of FCDs. We evaluated lesion detectability on S-SQ and P-HQ PET both visually and measuring the Relative Lesion Activity (RLA, measured activity in the reduced-activity ROI over the standard-activity ROI). Lastly, we applied our previously trained ResNet on 10 clinical epilepsy PETs to predict the corresponding HQ-PET and assessed image quality and confidence metrics.ResultsCompared to S-SQ PET, P-HQ PET improved PNSR, SSIM and RMSE; significatively improved GM RCs (from 0.29 ± 0.03 to 0.79 ± 0.04) and WM RCs (from 0.49 ± 0.03 to 1 ± 0.05); mean COVs were not statistically different. Visual lesion detection improved from 38 to 75%, with average RLA decreasing from 0.83 ± 0.08 to 0.67 ± 0.14. Visual quality of P-HQ clinical PET improved as well as reader confidence.ConclusionP-HQ PET showed improved image quality compared to S-SQ PET across several objective quantitative metrics and increased detectability of simulated lesions. In addition, the model generalized to clinical data. Further evaluation is required to study generalization of our method and to assess clinical performance in larger cohorts.

In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings.

We study non-reference image and video quality assessment methods, which are of great importance for computational video editing. The object of our work is image quality assessment (IQA) applicable for fast and robust frame-by-frame multipurpose video quality assessment (VQA) for short videos.

Image quality assessment (IQA) is one of the hot research areas in the field of image processing. For the reason that human being is the final receiver of the image, the image quality assessment should match the characteristics of human visual system. In this paper, we propose a novel method of image quality assessment which uses the visual selective attention of human visual system. For an image of a certain category, our method firstly detects the object in it and then calculate the saliency of the object. Lastly we use the combination of the detector’s score and the saliency as the image quality assessment. Experiments on some images of Pascal VOC dataset and INRIA dataset show that our method does well in image quality assessment.

The current rate of success in launching satellites and advances in onboard and ground image processing have led to a dramatic increase in the scale of remote sensing image data. This has resulted in considerable research on how to provide the best quality of experience to end users. However, subjective image quality assessment (IQA) is time-consuming, cumbersome, expensive and cannot be implemented automatically using computers. Thus, subjective IQA may not be suitable for application requirements. In this paper, we design and construct a usability-based subjective remote sensing IQA database. A corresponding no-reference IQA method is also proposed. The new IQA method uses scale-invariant feature transforms to form a dictionary and then a support vector machine to obtain an IQA model. The experimental results show that the new subjective IQA database is highly suited to the task, and the quality predictions of the new IQA method correlate well with human subjective scores in the new database.

The emergence of fully volumetric CT imaging systems and statistical / iterative reconstruction methods present an immediate need for rigorous methods of image quality assessment. Major themes arising in the widespread utilization of such technologies include: 1.) the reduction of radiation dose in a manner that maintains imaging performance with respect to the imaging task; 2.) the need for quantitative standards in image quality assessment appropriate to various vendor hardware and reconstruction systems; 3.) imaging performance metrics that provide meaningful descriptors of fully 3D spatial resolution and noise characteristics; 4.) the dependence of these metrics on acquisition technique, reconstruction parameters, and their applicability (or lack thereof) to novel reconstruction methods that may defy conventional metrics founded in assumptions of linearity and shift invariance; and 5.) methods for physical measurement (e.g., phantoms) and theoretical analysis of image quality for these new and emerging CT technologies and their implications for dose reduction.Learning Objectives:1. Understand the methods and metrics of image quality assessment for fully volumetric (cone‐beam) CT imaging using 3D filtered backprojection, including analysis of the 3D NPS, NEQ, and detectability index.2. Understand the limitations and assumptions of such metrics with respect to linearity, stationarity, and novel reconstruction techniques.3. Gain an understanding of how rigorous assessment of imaging performance can guide technique optimization, dose reduction, and the development of new CT technologies and applications.4. Understand the factors of image quality assessment in nonlinear statistical / model‐based 3D reconstruction techniques, including PSF and covariance estimation in nonstationary systems.5. Gain perspective on image quality assessment standards, including methods for dose and image quality measurement on different vendor platforms and the implications for dose reduction.

Image quality assessment (IQA) is a fundamental yet constantly developing task for computer vision and image processing. Most IQA evaluation mechanisms are based on the pertinence of subjective and objective estimation. Each image distortion type has its own property correlated with human perception. However, this intrinsic property may not be fully exploited by existing IQA methods. In this paper, we make two main contributions to the IQA field. First, a novel IQA method is developed based on a local linear model that examines the distortion between the reference and the distorted images for better alignment with human visual experience. Second, a distortion-specific compensation strategy is proposed to offset the negative effect on IQA modeling caused by different image distortion types. These score offsets are learned from several known distortion types. Furthermore, for an image with an unknown distortion type, a convolutional neural network-based method is proposed to compute the score offset automatically. Finally, an integrated IQA metric is proposed by combining the aforementioned two ideas. Extensive experiments are performed to verify the proposed IQA metric, which demonstrate that the local linear model is useful in human perception modeling, especially for individual image distortion, and the overall IQA method outperforms several state-of-the-art IQA approaches.