376,729 research outputs found

    IMPROVING IMAGE QUALITY ASSESSMENT WITH MODELING VISUAL ATTENTION

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    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

    An improved model of binocular energy calculation for full-reference stereoscopic image quality assessment

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    With the exponential growth of stereoscopic imaging in various applications, it has become very demanding to have a reliable quality assessment technique to measure the human perception of stereoscopic images. Quality assessment of stereoscopic visual content in the presence of artefacts caused by compression and transmission is a key component of end-to-end 3D media delivery systems. Despite a few recent attempts to develop stereoscopic image/video quality metrics, there is still a lack of a robust stereoscopic image quality metric. Towards addressing this issue, this paper proposes a full reference stereoscopic image quality metric, which mimics the human perception while viewing stereoscopic images. A signal processing model that is consistent with physiological literature is developed in the paper to simulate the behaviour of simple and complex cells of the primary visual cortex in the Human Visual System (HVS). The model is trained with two publicly available stereoscopic image databases to match the perceptual judgement of impaired stereoscopic images. The experimental results demonstrate a significant improvement in prediction performance as compared with several state-of-the-art stereoscopic image quality metrics

    Deep Multi-Scale Features Learning for Distorted Image Quality Assessment

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    Image quality assessment (IQA) aims to estimate human perception based image visual quality. Although existing deep neural networks (DNNs) have shown significant effectiveness for tackling the IQA problem, it still needs to improve the DNN-based quality assessment models by exploiting efficient multi-scale features. In this paper, motivated by the human visual system (HVS) combining multi-scale features for perception, we propose to use pyramid features learning to build a DNN with hierarchical multi-scale features for distorted image quality prediction. Our model is based on both residual maps and distorted images in luminance domain, where the proposed network contains spatial pyramid pooling and feature pyramid from the network structure. Our proposed network is optimized in a deep end-to-end supervision manner. To validate the effectiveness of the proposed method, extensive experiments are conducted on four widely-used image quality assessment databases, demonstrating the superiority of our algorithm

    Quality Assessment of Resultant Images after Processing

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    Image quality is a characteristic of an image that measures the perceived image degradation, typically, compared to an ideal or perfect image. Imaging systems may introduce some amounts of distortion or artifacts in the signal, so the quality assessment is an important problem.  Processing of images involves complicated steps. The aim of any processing result is to get a processed image which is very much same as the original. It includes image restoration, enhancement, compression and many more. To find if the reconstructed image after compression has lost the originality is found by assessing the quality of the image. Traditional perceptual image quality assessment approaches are based on measuring the errors (signal differences between the distorted and the reference images and attempt to quantify the errors in a way that simulates human visual error sensitivity features. A discussion is proposed here in order to assess the quality of the compressed image and the relevant information of the processed image is found. Keywords: Reference methods, Quality Assessment, Lateral chromatic aberration, Root Mean Squared Error, Peak Signal to Noise Ratio, Signal to Noise Ratio, Human Visual System

    Image Quality Assessment Using Edge Correlation

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    In literature, oriented filters are used for low-level vision tasks. In this paper, we propose use of steerable Gaussian filter in image quality assessment. Human visual system is more sensitive to multidirectional edges present in natural images. The most degradation in image quality is caused due to its edges. In this work, an edge based metric termed as steerable Gaussian filtering (SGF) quality index is proposed as objective measure for image quality assessment. The performance of the proposed technique is evaluated over multiple databases. The experimental result shows that proposed method is more reliable and outperform the conventional image quality assessment method
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