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

Half of an image is enough for quality assessment

Preprin

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

Quality Assessment of In-the-Wild Videos

Quality assessment of in-the-wild videos is a challenging problem because of the absence of reference videos and shooting distortions. Knowledge of the human visual system can help establish methods for objective quality assessment of in-the-wild videos. In this work, we show two eminent effects of the human visual system, namely, content-dependency and temporal-memory effects, could be used for this purpose. We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network. For content-dependency, we extract features from a pre-trained image classification neural network for its inherent content-aware property. For temporal-memory effects, long-term dependencies, especially the temporal hysteresis, are integrated into the network with a gated recurrent unit and a subjectively-inspired temporal pooling layer. To validate the performance of our method, experiments are conducted on three publicly available in-the-wild video quality assessment databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm, respectively. Experimental results demonstrate that our proposed method outperforms five state-of-the-art methods by a large margin, specifically, 12.39%, 15.71%, 15.45%, and 18.09% overall performance improvements over the second-best method VBLIINDS, in terms of SROCC, KROCC, PLCC and RMSE, respectively. Moreover, the ablation study verifies the crucial role of both the content-aware features and the modeling of temporal-memory effects. The PyTorch implementation of our method is released at https://github.com/lidq92/VSFA.Comment: 9 pages, 7 figures, 4 tables. ACM Multimedia 2019 camera ready. -> Update alignment formatting of Table

Comparing apples and oranges: assessment of the relative video quality in the presence of different types of distortions

<p>Abstract</p> <p>Video quality assessment is essential for the performance analysis of visual communication applications. Objective metrics can be used for estimating the relative quality differences, but they typically give reliable results only if the compared videos contain similar types of quality distortion. However, video compression typically produces different kinds of visual artifacts than transmission errors. In this article, we focus on a novel subjective quality assessment method that is suitable for comparing different types of quality distortions. The proposed method has been used to evaluate how well different objective quality metrics estimate the relative subjective quality levels for content with different types of quality distortions. Our conclusion is that none of the studied objective metrics works reliably for assessing the co-impact of compression artifacts and transmission errors on the subjective quality. Nevertheless, we have observed that the objective metrics' tendency to either over- or underestimate the perceived impact of transmission errors has a high correlation with the spatial and temporal activity levels of the content. Therefore, our results can be useful for improving the performance of objective metrics in the presence of both source and channel distortions.</p

Attention integrated hierarchical networks for no-reference image quality assessment

Quality assessment of natural images is influenced by perceptual mechanisms, e.g., attention and contrast sensitivity, and quality perception can be generated in a hierarchical process. This paper proposes an architecture of Attention Integrated Hierarchical Image Quality networks (AIHIQnet) for no-reference quality assessment. AIHIQnet consists of three components: general backbone network, perceptually guided neck network, and head network. Multi-scale features extracted from the backbone network are fused to simulate image quality perception in a hierarchical manner. The attention and contrast sensitivity mechanisms modelled by an attention module capture essential information for quality perception. Considering that image rescaling potentially affects perceived quality, appropriate pooling methods in the non-convolution layers in AIHIQnet are employed to accept images with arbitrary resolutions. Comprehensive experiments on publicly available databases demonstrate outstanding performance of AIHIQnet compared to state-of-the-art models. Ablation experiments were performed to investigate the variants of the proposed architecture and reveal importance of individual components.publishedVersio