Subjective quality analyses of stereoscopic images in 3DTV system

Zhou, Junming; Jiang, Gangyi; Mao, Xiangying; Yu, Mei; Shao, Feng; Peng, Zongju; Zhang, Yun

doi:10.1109/vcip.2011.6115913

Cited by 48 publications

(19 citation statements)

References 5 publications

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“…Currently, there are two categories for existing 3D IQDs. One is symmetric IQD [55] where the left/right views of the stereoscopic image are symmetrically distorted. The other category is the asymmetric IQD [9] where the left/right views of the stereoscopic image are degraded with different distortion types and levels.…”

Section: Resultsmentioning

confidence: 99%

Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment

Wang

Liu

et al. 2016

Neurocomputing

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Section: Resultsmentioning

confidence: 99%

Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment

Wang

Liu

et al. 2016

Neurocomputing

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“…6. The 30 pieces of natural stereoscopic images include 19 stereoscopic images in NBU 3D IQA database [20] and 11 pieces of randomly selected stereoscopic frames from several stereoscopic test sequences, such as the 1st/100th/158th/247th/493rd frame of the Balloons sequence, the 2nd/47th/92nd/126th/178th/277th frame of the Newspaper sequence. The reference stereoscopic image was obtained by using symmetric intra coding with the base QP REF selection of QP for subjective experiment are shown in Table 3.…”

Section: The Reliability and Effectiveness Test For Tavt Modelmentioning

confidence: 99%

A novel texture-based asymmetric visibility threshold model for stereoscopic video coding

Jiang

et al. 2018

J Image Video Proc.

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Asymmetric stereoscopic video coding is becoming increasingly popular, as it can reduce the bandwidth required for stereoscopic 3D delivery without degrading the visual quality. Based on the perceptual theory of binocular suppression, the left and right views of stereoscopic video are coded with different levels of quality. However, existing asymmetric perceptual coding approaches on stereoscopic video mainly focus on the threshold of whole image distortion. It is not so reasonable to use a single unified rather than adaptable perception threshold for a random natural stereoscopic image as the texture complexity typically varies in different blocks of image. In this paper, we generated an asymmetrically distorted stereoscopic image set with different texture densities and conducted a large number of subjective perceptual experiments. A strong correlation between the asymmetrical visibility threshold and texture complexity is revealed from the subjective experiments, and a texture-based asymmetrical visibility threshold model (TAVT) is established. Then, the model is extended to the hierarchical B picture (HBP) coding architecture and an asymmetric stereoscopic video coding method is proposed based on the TAVT model. Experimental results show that the proposed method can effectively reduce the unnecessary perceptual redundancy without visual quality degradation. Especially, it is more efficient for high bitrate configuration.

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“…NBU 3D IQA Database [67]: LIVE 3D IQA Database Phase II [33]: The LIVE 3D IQA Database Phase II consists of 120 symmetrically distorted stereoscopic pairs and 240 asymmetrically distorted stereoscopic pairs generated from 8 reference stereoscopic images, respectively. Five types of distortions, JPEG, JP2K, Gblur, WN and FF, are symmetrically and asymmetrically applied to the left and right reference stereoscopic images at various levels for the database.…”

Section: A Databases and Performance Measuresmentioning

confidence: 99%

Full-Reference Quality Assessment of Stereoscopic Images by Learning Binocular Receptive Field Properties

Shao

Wang

et al. 2015

IEEE Trans. on Image Process.

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109

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Quality assessment of 3D images encounters more challenges than its 2D counterparts. Directly applying 2D image quality metrics is not the solution. In this paper, we propose a new full-reference quality assessment for stereoscopic images by learning binocular receptive field properties to be more in line with human visual perception. To be more specific, in the training phase, we learn a multiscale dictionary from the training database, so that the latent structure of images can be represented as a set of basis vectors. In the quality estimation phase, we compute sparse feature similarity index based on the estimated sparse coefficient vectors by considering their phase difference and amplitude difference, and compute global luminance similarity index by considering luminance changes. The final quality score is obtained by incorporating binocular combination based on sparse energy and sparse complexity. Experimental results on five public 3D image quality assessment databases demonstrate that in comparison with the most related existing methods, the devised algorithm achieves high consistency with subjective assessment.

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Subjective quality analyses of stereoscopic images in 3DTV system

Cited by 48 publications

References 5 publications

Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment

Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment

A novel texture-based asymmetric visibility threshold model for stereoscopic video coding

Full-Reference Quality Assessment of Stereoscopic Images by Learning Binocular Receptive Field Properties

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