IMPROVING IMAGE QUALITY ASSESSMENT WITH MODELING VISUAL ATTENTION

Visual attention is an important attribute of the human visual system (HVS), while it has not been explored in image quality assessment adequately. This paper investigates the capabilities of visual attention models for image quality assessment in different scenarios: twodimensional images, stereoscopic images, and Digital Cinema setup. Three bottom-up attention models are employed to detect attention regions and find fixation points from an image and compute respective attention maps. Different approaches for integrating the visual attention models into several image quality metrics are evaluated with respect to three different image quality data sets. Experimental results demonstrate that visual attention is a positive factor that can not be ignored in improving the performance of image quality metrics in perceptual quality assessment. Index Terms — Visual attention, saliency, fixation, image quality metri

Visual Comfort Assessment for Stereoscopic Image Retargeting

In recent years, visual comfort assessment (VCA) for 3D/stereoscopic content has aroused extensive attention. However, much less work has been done on the perceptual evaluation of stereoscopic image retargeting. In this paper, we first build a Stereoscopic Image Retargeting Database (SIRD), which contains source images and retargeted images produced by four typical stereoscopic retargeting methods. Then, the subjective experiment is conducted to assess four aspects of visual distortion, i.e. visual comfort, image quality, depth quality and the overall quality. Furthermore, we propose a Visual Comfort Assessment metric for Stereoscopic Image Retargeting (VCA-SIR). Based on the characteristics of stereoscopic retargeted images, the proposed model introduces novel features like disparity range, boundary disparity as well as disparity intensity distribution into the assessment model. Experimental results demonstrate that VCA-SIR can achieve high consistency with subjective perception

Towards a reliable collection of eye-tracking data for image quality research: challenges, solutions and applications

Image quality assessment potentially benefits from the addition of visual attention. However, incorporating aspects of visual attention in image quality models by means of a perceptually optimized strategy is largely unexplored. Fundamental challenges, such as how visual attention is affected by the concurrence of visual signals and their distortions; whether visual attention affected by distortion or that driven by the original scene only should be included in an image quality model; and how to select visual attention models for the image quality application context, remain. To shed light on the above unsolved issues, designing and performing eye-tracking experiments are essential. Collecting eye-tracking data for the purpose of image quality study is so far confronted with a bias due to the involvement of stimulus repetition. In this paper, we propose a new experimental methodology to eliminate such inherent bias. This allows obtaining reliable eye-tracking data with a large degree of stimulus variability. In fact, we first conducted 5760 eye movement trials that included 160 human observers freely viewing 288 images of varying quality. We then made use of the resulting eye-tracking data to provide insights into the optimal use of visual attention in image quality research. The new eye-tracking data are made publicly available to the research community

Perceptual Quality Assessment Based on Visual Attention Analysis

Most existing quality metrics do not take the human attention analysis into account. Attention to particular objects or regions is an important attribute of human vision and perception system in measuring perceived image and video qualities. This paper presents an approach for extracting visual attention regions based on a combination of a bottom-up saliency model and semantic image analysis. The use of PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural SIMilarity) in extracted attention regions is analyzed for image/video quality assessment, and a novel quality metric is proposed which can exploit the attributes of visual attention information adequately. The experimental results with respect to the subjective measurement demonstrate that the proposed metric outperforms the current methods

Joint Deep Image Restoration and Unsupervised Quality Assessment

Deep learning techniques have revolutionized the fields of image restoration and image quality assessment in recent years. While image restoration methods typically utilize synthetically distorted training data for training, deep quality assessment models often require expensive labeled subjective data. However, recent studies have shown that activations of deep neural networks trained for visual modeling tasks can also be used for perceptual quality assessment of images. Following this intuition, we propose a novel attention-based convolutional neural network capable of simultaneously performing both image restoration and quality assessment. We achieve this by training a JPEG deblocking network augmented with "quality attention" maps and demonstrating state-of-the-art deblocking accuracy, achieving a high correlation of predicted quality with human opinion scores.Comment: 4 Pages, 2 figures, 3 table

Towards the next generation of video and image quality metrics: Impact of display, resolution, contents and visual attention in subjective assessment

International audienceTwo decades of research in video and image quality assessment has led to the design of subjective assessment protocols and objective metrics. In order to get good performances, most of research works have restricted their focus of interest on SD format or below and on distortion stemming from coding artifacts or transmission error. Considering up-coming services such as HDTV or scalable video coding, next generation of quality metric should take into account more factors that affect the end user quality of experience. In this paper, a review of factors is proposed considering subjective quality assessment. The four studied factors include display, resolution, content and visual attention. Each factor reveals open issues in quality assessment

Glaucoma and cigarette smoking: a review of narrative reviews

Background: Glaucoma is an optic neuropathy associated with visual field changes for which high intra-ocular pressure is a major risk factor. Emerging research indicates that modifiable factors, among which the cigarette smoke, besides IOP may be associated with the presence of glaucoma. Objective: The objective of the study was to perform a review of narrative reviews to examine on the relationship between cigarette smoking and glaucoma. Methods: The results of all narrative reviews in the scientific literature about glaucoma and tobacco smoking were analyzed. A quality assessment was performed according to an easy and convenient tool for the quality assessment of narrative reviews for systematic reviews (International Narrative Systematic assessment) the INSA tool. Literature searches were performed using PubMed. Results: 20 studies about relation between glaucoma and smoke were collected, no restriction language was applied. 15 of these studies have been excluded. We selected among them 5 reviews. With the INSA tool we measured the quality of the 5 selected narrative reviews. Studies that had a highest score with the INSA tool were two: A. Coleman et al. “Risk Factors for Glaucoma Needing More Attention” and R. Salowe et al. “Primary Open-Angle Glaucoma in Individuals of African Descent: A Review of Risk Factors”. Conclusion: The narrative reviews analyzed underline that there is no definitive association between cigarette smoking