Closed-Form Optimization on Saliency-Guided Image Compression for HEVC-MSP

Li, Shengxi; Xu, Mai; Ren, Yun; Wang, Zulin

doi:10.1109/tmm.2017.2721544

Cited by 67 publications

(23 citation statements)

References 53 publications

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“…In [38,41], the performance of the above objective VQA approaches was compared by measuring their correlation with the subjective quality scores. Unfortunately, none of the above VQA approaches considers human behavior on viewing omnidirectional video, which significantly influences the quality of experience (QoE) [17,37].…”

Section: Related Workmentioning

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

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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%

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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“…Many researchers have attempted to predict a saliency map that indicates image components that are more attractable than their neighbors [1][2][3][4]. Since a saliency map reflects human visual attention, it has been expected to contribute to image processing tasks including image re-targeting [5,6], image compression [7,8], and image enhancement [9,10]. The purpose of those studies is prediction of instinctual human visual attention, that is, the common regions of images to humans.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention

Moroto

Maeda

Ogawa

et al. 2020

Sensors

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A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training images, the establishment of a theory using a small number of training images is needed. To tackle this problem, although finding persons who have visual attention similar to that of a target person is effective, all persons have to commonly gaze at many images. Thus, it becomes difficult and unrealistic when considering their burden. On the other hand, this paper introduces a novel adaptive image selection (AIS) scheme that focuses on the relationship between human visual attention and objects in images. AIS focuses on both a diversity of objects in images and a variance of PSMs for the objects. Specifically, AIS selects images so that selected images have various kinds of objects to maintain their diversity. Moreover, AIS guarantees the high variance of PSMs for persons since it represents the regions that many persons commonly gaze at or do not gaze at. The proposed method enables selecting similar users from a small number of images by selecting images that have high diversities and variances. This is the technical contribution of this paper. Experimental results show the effectiveness of our personalized saliency prediction including the new image selection scheme.

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“…The ability to process information mimicking the visual attention mechanism is known as saliency detection in computer vision. As a preprocessing step, saliency detection promotes efficiency in a wide variety of vision-oriented multimedia applications, such as semantic segmentation [1]- [5], image quality assessment [6], [7], image and video compression [8], [9], image retargeting [10], [11], image classification [12]- [14], and image retrieval [15], [16]. Saliency detection can be divided into eye-fixation prediction [17]- [20], which predicts the focus of the human gaze, and salient object detection [21]- [25], which extracts the most salient objects or regions from a scene.…”

Section: Introductionmentioning

confidence: 99%