Improving image quality assessment with modeling visual attention

You, Junyong; Perkis, Andrew; Gabbouj, Moncef

doi:10.1109/euvip.2010.5699102

Cited by 12 publications

(5 citation statements)

References 12 publications

(22 reference statements)

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“…This method is based on the difference between the center and the outline of the image. It is a form of extraction that has a quality that attracts the attention of human beings, as studied by Jia et al [14] and You et al [15]. The feature descriptors presented next are generated in images with and without the extracted salience in order to assess its usefulness in detecting images that present strangeness on human perception.…”

Section: B Pre-processing Datamentioning

confidence: 99%

Can we estimate the perceived comfort of virtual human faces using visual cues?

Molin

Nomura

Dalmoro

et al. 2021

2021 IEEE 15th International Conference on Semantic Computing (ICSC)

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The sense of strangeness (or discomfort) perceived in certain virtual characters, discussed in Uncanny Valey (UV) theory, can be a key factor in our perceptual and cognitive discrimination. Understanding how this strangeness happens is essential to avoid it in the process of modeling virtual humans. In this paper, we investigate the relationship between images features and the discomfort that human beings can perceive. We extract image features based on Hu Moments (Hum) and Histogram Oriented Gradient (Hog). The saliency detection is also extracted in the specific parts of the virtual face. Finally, a model using Support Vector Machine (SVM) to provide binary classification is suggested. The results indicate accuracy of around 80% in the image estimation process comparing with subjective classification. As a contribution, some areas may benefit from this study for avoiding the creation of characters that may cause strangeness, such as the games, conversational agents and cinema industry.

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Section: B Pre-processing Datamentioning

confidence: 99%

Can we estimate the perceived comfort of virtual human faces using visual cues?

Molin

Nomura

Dalmoro

et al. 2021

2021 IEEE 15th International Conference on Semantic Computing (ICSC)

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“…[43][44][45][46][47] There are also mechanisms available that can objectively estimate the ROI. [48][49][50][51][52] It is therefore already possible to implement the functionality that would optimize the encryption of different regions of images while predicting how it will affect the overall quality. This can be practical for saving content on mobile devices where memory-space is limited, or when making content for the web to save bandwidth.…”

Section: Discussionmentioning

confidence: 99%

Studying the effect of optimizing image quality in salient regions at the expense of background content

Alers

Redi

Liu

et al. 2013

J. Electron. Imaging

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. (2013). Studying the effect of optimizing image quality in salient regions at the expense of background content. Journal of Electronic Imaging, 22(4), 043012-1/10. [043012]. DOI: 10.1117/1.JEI.22.4.043012 General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract. Manufacturers of commercial display devices continuously try to improve the perceived image quality of their products. By applying postprocessing techniques on the incoming signal, they aim to enhance the quality level perceived by the viewer. These postprocessing techniques are usually applied globally over the whole image but may cause side effects, the visibility and annoyance of which differ with local content characteristics. To better understand and utilize this, a three-phase experiment was conducted where observers were asked to score images that had different levels of quality in their regions of interest and in the background areas. The results show that the region of interest has a greater effect on the overall quality of the image than the background. This effect increases with the increasing quality difference between the two regions. Based on the subjective data we propose a model to predict the overall quality of images with different quality levels in different regions. This model, which is constructed on empirical bases, can help craft weighted objective metrics that can better approximate subjective quality scores.

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“…Nevertheless, such approaches can lead to significantly improved performance in the case of non-uniformly located distortions such as those due to transmission impairments [67]. Alternative weighting methods have been introduced for compression artifacts with varying success [68], [69]. In ref.…”

Section: Applications Of Visual Attention Models In Image and VImentioning

confidence: 99%

Visual Attention and Applications in Multimedia Technologies

Callet

Niebur

2013

Proc. IEEE

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Making technological advances in the field of human-machine interactions requires that the capabilities and limitations of the human perceptual system are taken into account. The focus of this report is an important mechanism of perception, visual selective attention, which is becoming more and more important for multimedia applications. We introduce the concept of visual attention and describe its underlying mechanisms. In particular, we introduce the concepts of overt and covert visual attention, and of bottom-up and top-down processing. Challenges related to modeling visual attention and their validation using ad hoc ground truth are also discussed. Examples of the usage of visual attention models in image and video processing are presented. We emphasize multimedia delivery, retargeting and quality assessment of image and video, medical imaging, and the field of stereoscopic 3D images applications.

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Improving image quality assessment with modeling visual attention

Cited by 12 publications

References 12 publications

Can we estimate the perceived comfort of virtual human faces using visual cues?

Can we estimate the perceived comfort of virtual human faces using visual cues?

Studying the effect of optimizing image quality in salient regions at the expense of background content

Visual Attention and Applications in Multimedia Technologies

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