Which saliency weighting for omni directional image quality assessment?

Rai, Yashas; Callet, Patrick Le; Guillotel, Philippe

doi:10.1109/qomex.2017.7965659

Cited by 72 publications

(41 citation statements)

References 8 publications

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“…Most recently, some omnidirectional image/video datasets have been established to collect the HM data [3,44,47] and EM data [26] of subjects. Given these datasets, it is possible to analyze and model human behavior [27,47] on viewing omnidirectional image/video. Additionally, some works [1,5,32] have been recently proposed to predict human's HM and EM on omnidirectional video, similar to saliency prediction on 2D video.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the human behavior of HM and EM is rather important in determining visual quality of omnidirectional video. In fact, there are many latest works [1,3,5,26,27,32,44] concerning the human behavior of HM and EM in watching omnidirectional image/video. Along with these approaches, some omnidirectional image/video datasets were established to collect HM or EM data.…”

Section: Introductionmentioning

confidence: 99%

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Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

107

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Omnidirectional video enables spherical stimuli with the 360 × 180 • viewing range. Meanwhile, only the viewport region of omnidirectional video can be seen by the observer through head movement (HM), and an even smaller region within the viewport can be clearly perceived through eye movement (EM). Thus, the subjective quality of omnidirectional video may be correlated with HM and EM of human behavior. To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset provides not only the subjective quality scores of sequences but also the HM and EM data of subjects. By mining our dataset, we find that the subjective quality of omnidirectional video is indeed related to HM and EM. Hence, we develop a deep learning model, which embeds HM and EM, for objective VQA on omnidirectional video. Experimental results show that our model significantly improves the state-of-the-art performance of VQA on omnidirectional video. CCS CONCEPTS• Human-centered computing → Virtual reality; KEYWORDSOmnidirectional video, visual quality assessment, human behavior ACM Reference Format:

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

107

View full text Add to dashboard Cite

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“…47 Moreover, when shown in equirectangular projection, the 360°contents are non-Euclidean. 48 To address these problems, many studies on 360°images/videos in viewing databases, 49,50 image quality assessment, 51 and saliency detection, 52 have been conducted in recent years.…”

Section: Related Saliency Modelsmentioning

confidence: 99%

Saliency detection based on structural dissimilarity induced by image quality assessment model

Mou

2019

J. Electron. Imag.

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The distinctiveness of image regions is widely used as the cue of saliency. Generally, the distinctiveness is computed according to the absolute difference of features. However, according to the image quality assessment (IQA) studies, the human visual system is highly sensitive to structural changes rather than absolute difference. Accordingly, we propose the computation of the structural dissimilarity between image patches as the distinctiveness measure for saliency detection. Similar to IQA models, the structural dissimilarity is computed based on the correlation of the structural features. The global structural dissimilarity of a patch to all the other patches represents saliency of the patch. We adopted two widely used structural features, namely the local contrast and gradient magnitude, into the structural dissimilarity computation in the proposed model. Without any post-processing, the proposed model based on the correlation of either of the two structural features outperforms 11 state-of-the-art saliency models on three saliency databases. Source code of the proposed model can be downloaded at https://github.com/yangli-xjtu/SDS.

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“…As a future extension of our work, more accurate fixations maps can be computed based on the prediction of the eye gaze fixation from head fixation locations, applying the work of Rai et al 37 for instance. In addition, the selection of the threshold indicating if the angular velocity of observer's head prevents subjects focus of attention should be optimized.…”

Section: Toggling Processingmentioning

confidence: 99%

Measuring quality of omnidirectional high dynamic range content

Perrin

Bist

Cozot

et al. 2017

Applications of Digital Image Processing XL

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Although HDR content processing, coding and quality assessment have been largely addressed in the last few years, little to no work has been concentrating on how to assess quality in HDR for 360• or omnidirectional content. This paper is an attempt to answer to various questions in this direction. As a minimum, a new data set for 360• HDR content is proposed and a new methodology is designed to assess subjective quality of HDR 360• content when it is displayed on SDR HMD after applying various tone mapping operators. The results are then analyzed and conclusions are drawn.

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Which saliency weighting for omni directional image quality assessment?

Cited by 72 publications

References 8 publications

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

Saliency detection based on structural dissimilarity induced by image quality assessment model

Measuring quality of omnidirectional high dynamic range content

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