A wavelet-based image quality metric for the assessment of 3D synthesized views

Bosc, Emilie; Battisti, Federica; Carli, Marco; Callet, Patrick Le

doi:10.1117/12.2002410

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…Solh et al [34] presented a metric for quantifying the geometric and photometric distortions in multiview acquisition. Bosc et al [3] suggested a method to asses the quality of virtual synthesized views in the context of multiview video. Battisti et al [2] proposed more sophisticated framework for evaluating the quality of depth image based rendering techniques by comparing the statistical features of wavelet subbands and used image registration and skin detection steps for additional optimization.…”

Section: Previous Workmentioning

confidence: 99%

Towards a Quality Metric for Dense Light Fields

Adhikarla¹,

Vinkler²,

Sumin³

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

104

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Light fields become a popular representation of threedimensional scenes, and there is interest in their processing, resampling, and compression. As those operations often result in loss of quality, there is a need to quantify it. In this work, we collect a new dataset of dense reference and distorted light fields as well as the corresponding quality scores which are scaled in perceptual units. The scores were acquired in a subjective experiment using an interactive light-field viewing setup. The dataset contains typical artifacts that occur in light-field processing chain due to light-field reconstruction, multi-view compression, and limitations of automultiscopic displays. We test a number of existing objective quality metrics to determine how well they can predict the quality of light fields. We find that the existing image quality metrics provide good measures of light-field quality, but require dense reference light-fields for optimal performance. For more complex tasks of comparing two distorted light fields, their performance drops significantly, which reveals the need for new, light-field-specific metrics.

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Section: Previous Workmentioning

confidence: 99%

Towards a Quality Metric for Dense Light Fields

Adhikarla¹,

Vinkler²,

Sumin³

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

104

View full text Add to dashboard Cite

show abstract

“…Bosc et al [25], [36] proposed to measure the wrong displacement of the object edge in synthesized view, which is induced by the DIBR process. In [37], [38], a wavelet decomposition based synthesized view quality assessment method was presented. Before quality evaluation, a registration procedure in wavelet domain is used to align the content of the synthesized image and the reference image.…”

Section: B Objective Studymentioning

confidence: 99%

“…This process is under the assumption that imperceptible object shift introduced by the synthesis process will not affect the visual quality. Tsai et al [39] proposed to compensate the object shift effect with the similar assumption to [37], [38] and computed the noise at object boundaries of a synthesized image with depth distortion. However, the methods in [36]- [39] are all IQA methods not involving the studies about the significant temporal distortion.…”

Section: B Objective Studymentioning

confidence: 99%

Subjective and Objective Video Quality Assessment of 3D Synthesized Views With Texture/Depth Compression Distortion

Liu

Zhang

et al. 2015

IEEE Trans. on Image Process.

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The quality assessment for synthesized video with texture/depth compression distortion is important for the design, optimization, and evaluation of the multi-view video plus depth (MVD)-based 3D video system. In this paper, the subjective and objective studies for synthesized view assessment are both conducted. First, a synthesized video quality database with texture/depth compression distortion is presented with subjective scores given by 56 subjects. The 140 videos are synthesized from ten MVD sequences with different texture/depth quantization combinations. Second, a full reference objective video quality assessment (VQA) method is proposed concerning about the annoying temporal flicker distortion and the change of spatio-temporal activity in the synthesized video. The proposed VQA algorithm has a good performance evaluated on the entire synthesized video quality database, and is particularly prominent on the subsets which have significant temporal flicker distortion induced by depth compression and view synthesis process.

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“…Particularly, the wavelets are used in 3D imaging, for instance, in multiview video compression [4], in image coding [5], in image fusion [6], as a quality metric [7], etc. The disparity of stereoscopic images can be estimated [8], the shape of the photographed/synthesized objects can be analysed, and the depth can be extracted using the wavelet analysis of the integral images [9][10][11]. In this paper, we propose the technique to eliminate the texture effect.…”

Section: Introductionmentioning

confidence: 99%

Depth Analysis of Greyscale Integral Images Using Continuous Multiview Wavelet Transform

Saveljev

Palchikova

2018

International Journal of Optics

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The wavelet analysis of the integral images can be used in extraction of the depth of the photographed/synthesized 3D objects. The result of the analysis, however, depends on the colour/texture of the object and thus can be ambiguous. In this paper, we propose to normalize the image before processing in order to avoid such an ambiguity, and to extract the depth without regard to the colour/texture. The proposed technique is verified in multiple integral/plenoptic images and can be applied to multiview and light-field displays as well.

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A wavelet-based image quality metric for the assessment of 3D synthesized views

Cited by 12 publications

References 24 publications

Towards a Quality Metric for Dense Light Fields

Towards a Quality Metric for Dense Light Fields

Subjective and Objective Video Quality Assessment of 3D Synthesized Views With Texture/Depth Compression Distortion

Depth Analysis of Greyscale Integral Images Using Continuous Multiview Wavelet Transform

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