Adaptive color image compression based on visual attention

Ouerhani, Nabil; Bracamonte, Javier; Hügli, Heinz; Ansorge, Michael; Pellandini, Fausto

doi:10.1109/iciap.2001.957045

Cited by 47 publications

(27 citation statements)

References 7 publications

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“…Note that the model is purely data-driven and does not require any a priori knowledge about the scene. This model has been used in numerous computer vision applications including image compression [18], robot navigation [19], and image segmentation [20,21]. However, and despite the fact that it is inspired by psychological theories, the theoretical grounding of the saliency-based model has not been fully assessed.…”

Section: Introductionmentioning

confidence: 99%

Empirical Validation of the Saliency-based Model of Visual Attention

Ouerhani¹,

Wartburg²,

Hügli³

et al. 2003

ELCVIA

120

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Visual attention is the ability of the human vision system to detect salient parts of the scene, on which higher vision tasks, such as recognition, can focus. In human vision, it is believed that visual attention is intimately linked to the eye movements and that the fixation points correspond to the location of the salient scene parts. In computer vision, the paradigm of visual attention has been widely investigated and a saliencybased model of visual attention is now available that is commonly accepted and used in the field, despite the fact that its biological grounding has not been fully assessed. This work proposes a new method for quantitatively assessing the plausibility of this model by comparing its performance with human behavior. The basic idea is to compare the map of attention -the saliency map -produced by the computational model with a fixation density map derived from eye movement experiments. This human attention map can be constructed as an integral of single impulses located at the positions of the successive fixation points. The resulting map has the same format as the computer-generated map, and can easily be compared by qualitative and quantitative methods. Some illustrative examples using a set of natural and synthetic color images show the potential of the validation method to assess the plausibility of the attention model.

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Section: Introductionmentioning

confidence: 99%

Empirical Validation of the Saliency-based Model of Visual Attention

Ouerhani¹,

Wartburg²,

Hügli³

et al. 2003

ELCVIA

120

View full text Add to dashboard Cite

show abstract

“…The interesting portion, namely the salient region of an image in this paper, usually contains the most crucial information for image analysis and understanding. In recent years, the salient region detection techniques have been widely used in multimedia analysis and computer vision communities, such as image compression [1], image cropping [2], adaptive image display [3], image browsing [4] on small size screen devices. Unfortunately, the existing methods are far from satisfactory.…”

Section: Introductionmentioning

confidence: 99%

Learning to detect salient region of image under weak supervision

Cheng

2011

2011 IEEE International Conference on Multimedia and Expo

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Salient region of an image usually contains the crucial information for image analysis and understanding. Most conventional approaches learn the saliency by utilizing the low-level features, which ignore the participation of human. In this paper, we propose an effective and robust approach to detect the salient region of an image by combining the bottom-up and top-down cues. The proposed method not only consider the low-level attention features, but also take human into the loop for better understanding of human attention. Furthermore, we build an asymmetrical graph model to integrate these bottomup and top-down cues into an energy function of saliency. A compact but exact saliency region can be obtained by minimizing posterior energy function. The compact constraint and global minimization manner of the asymmetrical graph cuts guarantee the good performance of saliency extraction. Extensive experiments demonstrate the proposed method is promising.

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“…visual attention 모델을 사용해 saliency 맵을 구하고, 비디오의 관심 영역에 saliency 맵의 시각정보를 적용한 Z. Li, et al의 연구가 있다 [7]. 이들은 시각적인 관심 위치 이 제안한 방법이 있다 [12]. 이 방법은 특징 맵들을 center-surround mechanism을 통해 더 중요함을 밝혔다 [1].…”

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Saliency Map Based Color Image Compression for Visual Quality Enhancement of Image

Jung¹

2017

Journal of Korea Multimedia Society

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A color image compression based on saliency map was proposed. The proposed method provides higher quality in saliency blocks on which people's attention focuses, compared with non-saliency blocks on which the attention less focuses at a given bitrate. The proposed method uses 3 different quantization tables according to each block's saliency level. In the experiment using 6 typical images, we compared the proposed method with JPEG and other conventional methods. As the result, it showed that the proposed method (Qup=0.5*Qx) is about 3.1 to 1.2 dB better than JPEG and others in saliency blocks in PSNR at the almost similar bitrate. In the comparison of result images, the proposed one also showed less error than others in saliency blocks.

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Adaptive color image compression based on visual attention

Abstract: Abstract

Cited by 47 publications

References 7 publications

Empirical Validation of the Saliency-based Model of Visual Attention

Empirical Validation of the Saliency-based Model of Visual Attention

Learning to detect salient region of image under weak supervision

Saliency Map Based Color Image Compression for Visual Quality Enhancement of Image

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