The objective quality assessment of stereo image

Yun, Nan; Feng, Zhiyong; Yang, Jiachen; Lei, Jianjun

doi:10.1016/j.neucom.2012.06.059

Cited by 18 publications

(7 citation statements)

References 17 publications

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“…In other words, the absolute disparity information could reflect the depth information of 3D images. Based on the simple mathematical model shown in Eq.5, the absolute disparity map greatly reduces the computational complexity of depth perception information extraction [61]. Based on the above discussion, we improve the model of 3D visual saliency map in the work [58] by using the absolute disparity map information to extract the depth feature to build the 3D saliency map, and then apply it to provide quality prediction for 3D images.…”

Section: D Visual Saliency Mapmentioning

confidence: 98%

“…However, some 3D IQA Databases are either not publicly available or absent of subjective assessment value. To solve this problem, the SVBL 3D IQA Database (Stereo Image Database of Stereo Vision and BioOptics Laboratory from School of Electronic and Information Engineering, Tianjin University [73]) is used to further verify the proposed method. The SVBL 3D IQA Database contains 360 distorted stereopairs and 30 original stereopairs.…”

Section: Stereoscopic Image Quality Databasementioning

confidence: 99%

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Stereoscopic image quality assessment method based on binocular combination saliency model

et al. 2016

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Section: D Visual Saliency Mapmentioning

confidence: 98%

Section: Stereoscopic Image Quality Databasementioning

confidence: 99%

Stereoscopic image quality assessment method based on binocular combination saliency model

et al. 2016

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“…The independent components coefficients S ip of an image quality patch are changed Hash address codes according to the Hash function in the Eqs. (3)- (6). Then, the units pointed by the address codes in the Hash lookup table are found, and the hamming distances are calculated between the placed independent components in the units and the S ip .…”

Section: Indexing the Quality Featuresmentioning

confidence: 99%

“…No-reference image quality assessment technique is proposed to monitor the image quality of processing systems and overcome the difficulty of reference images being absent [3]. This technique is deeply studied by many scholars [4][5][6][7][8]. The quality features are important factors that are extracted to represent the properties of image quality in the technique of no-reference image quality assessment [9].…”

Section: Introductionmentioning

confidence: 99%

No-reference image quality assessment based on quality patches in real time

Zhang

Yang

Huang

et al. 2018

J Image Video Proc.

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Image quality assessment methods quantify the quality of an image that is highly correlated with human-perceived image quality. In this paper, the image quality is represented by the image patches that are selected by applying the spatial correlation of pixel-pairs. The quality image patches are selected from the database LIVE2, and quality feature detectors are learned by running the FastICA algorithm on the patches. Then being quality features, the independent component coefficients are found out from each quality image patch. A Hash lookup table is built by the binarization of the independent component coefficients of quality image patches, and the Hash table can be indexed by the binary code of the independent component coefficients of a tested image. The results of proposed approach were compared with results from other methods of image quality assessment and with the subjective image quality evaluated scores. And the experimental results expressed that the proposed metric method of no-reference image quality assessment could accurately measure the distorted degree of images. The Pearson linear correlation coefficient (PCC) achieves a high value 0.949 for the accuracy of evaluating results. The Spearman order correlation (SRC) achieves a high value 0.996 for the monotonicity of evaluating results. And the root mean square error (RMSE) is 5.917 for the subjective consistency of evaluating results. Extra evaluating aerial images, the proposed method obtained the result that the foggy weather led to the worst quality.

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“…When people observe stereoscopic images displayed on a 3DTV, they may experience visual discomfort and poor 3D effects [7,8,9]. To solve these problems, many studies have been carried out [9,10,11,12], and the corresponding evaluation theories and systems have been established based on comparisons between the original stereo images and post processing images [13,14,15]. Normally the captured 3D images are thought to be standard and ideal, while the improper parameter settings of the image sources during the capturing process may cause an uncomfortable stereo effect.…”

Section: Introductionmentioning

confidence: 99%

Objective Evaluation Criteria for Shooting Quality of Stereo Cameras over Short Distance

Chu¹,

Yang²,

Liu³

2015

RADIOENGINEERING

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Abstract. Stereo cameras are the basic tools used to obtain stereoscopic image pairs, which can lead to truly great image quality. However, some inappropriate shooting conditions may cause discomfort while viewing stereo images. It is therefore considerably necessary to establish the perceptual criteria that can be used to evaluate the shooting quality of stereo cameras. This article proposes objective quality evaluation criteria based on the characteristics of parallel and toed-in camera configurations. Considering the different internal structures and basic shooting principles, this paper focuses on short-distance shooting conditions and establishes assessment criteria for both parallel and toed-in camera configurations. Experimental results show that the proposed evaluation criteria can predict the visual perception of stereoscopic images and effectively evaluate stereoscopic image quality.

show abstract

The objective quality assessment of stereo image

Cited by 18 publications

References 17 publications

Stereoscopic image quality assessment method based on binocular combination saliency model

Stereoscopic image quality assessment method based on binocular combination saliency model

No-reference image quality assessment based on quality patches in real time

Objective Evaluation Criteria for Shooting Quality of Stereo Cameras over Short Distance

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