Video processing with scale-aware saliency: Application to Frame Rate Up-Conversion

Jacobson, Natan; Nguyen, Truong Q.

doi:10.1109/icassp.2011.5946653

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Other papers tend to find large salient regions [22], [29], [30], [31], [32], [33]. Recently, scale-aware saliency [34] has been introduced to alleviate the problem of fixed scale in the spatial domain. We consider both small salient points as well as salient regions.…”

Section: Introductionmentioning

confidence: 99%

Visual Saliency Based on Scale-Space Analysis in the Frequency Domain

Li,

Levine,

et al. 2016

Preprint

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We address the issue of visual saliency from three perspectives. First, we consider saliency detection as a frequency domain analysis problem. Second, we achieve this by employing the concept of non-saliency. Third, we simultaneously consider the detection of salient regions of different size. The paper proposes a new bottom-up paradigm for detecting visual saliency, characterized by a scale-space analysis of the amplitude spectrum of natural images. We show that the convolution of the image amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to such an image saliency detector. The saliency map is obtained by reconstructing the 2-D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform performs the analysis in the frequency domain. Using available databases, we demonstrate experimentally that the proposed model can predict human fixation data. We also introduce a new image database and use it to show that the saliency detector can highlight both small and large salient regions, as well as inhibit repeated distractors in cluttered images. In addition, we show that it is able to predict salient regions on which people focus their attention.

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Section: Introductionmentioning

confidence: 99%

Visual Saliency Based on Scale-Space Analysis in the Frequency Domain

Li,

Levine,

et al. 2016

Preprint

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“…Image saliency predicts the attentional gaze of observers viewing a scene [26], [30]. It has been used as a cue to aid in the performance of both image processing and computer vision ap-plications such as color to gray conversion [12], [3], image detail visibility [28], and motion-compensated frame interpolation [13]. In a domain closer to that of gamut mapping, it has been used to decide whether a black point compensation algorithm is needed when printing an image [18].…”

Section: Introductionmentioning

confidence: 99%

Considering saliency in a perception inspired gamut reduction algorithm

Vázquez-Corral¹,

Zamir²,

Bertalmío³

2014

cic

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Gamut reduction transforms the colors of an input image within the range of a target device. A good gamut reduction algorithm will preserve the experience felt by the viewer of the original image. Saliency algorithms predict the image regions where an observer first focuses. Therefore, there exists a connection between both concepts since modifying the saliency of the image will modify the viewer's experience. However, very little attention has been given to relate saliency and gamut mapping. In this paper we propose to modify a recent gamut reduction algorithm proposed by Zamir et al. [32] in order to better respect the saliency of the original image in the reproduced one. Our results show that the proposed approach presents a gamut-mapped image whose saliency map is closer to that of the original image with a minor loss in the accuracy of perceptual reproduction.

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“…. 이는 인간 시각 시스템 (Human Visual System: HVS)이 율-왜곡 최적화를 통한 전반적인 비디오 화질과 인간이 무의식적으 로 인지하는 시각적 자극이 관련되어 있는 것으로 분석할 수 있다 [2][3][4] . 따라서 [5,6] .…”

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Video Coding Method Using Visual Perception Model based on Motion Analysis

Oh¹,

Kim²

2012

Journal of Broadcast Engineering

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We develop a video processing method that allows the more advanced human perception oriented video coding. The proposed method necessarily reflects all influences by the rate-distortion based optimization and the human visual perception that is affected by the visual saliency, the limited space-time resolution and the regional moving history. For reflecting the human perceptual effects, we devise an online moving pattern classifier using the Hedge algorithm. Then, we embed the existing visual saliency into the proposed moving patterns so as to establish a human visual perception model. In order to realize the proposed human visual perception model, we extend the conventional foveation filtering method. Compared to the conventional foveation filter only smoothing less stimulus video signals, the developed foveation filter can locally smooth and enhance signals according to the human visual perception without causing any artifacts. Due to signal enhancement, the developed foveation filter more efficiently transfers the bandwidth saved at smoothed signals to the enhanced signals. Performance evaluation verifies that the proposed video processing method satisfies the overall video quality, while improving the perceptual quality by 12%~44%.

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Video processing with scale-aware saliency: Application to Frame Rate Up-Conversion

Cited by 6 publications

References 9 publications

Visual Saliency Based on Scale-Space Analysis in the Frequency Domain

Visual Saliency Based on Scale-Space Analysis in the Frequency Domain

Considering saliency in a perception inspired gamut reduction algorithm

Video Coding Method Using Visual Perception Model based on Motion Analysis

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