Towards a New Quality Metric for 3-D Synthesized View Assessment

Bosc, Emilie; Pépion, Romuald; Callet, Patrick Le; Köppel, Martin; Ndjiki-Nya, Patrick; Pressigout, Muriel; Morin, Luce

doi:10.1109/jstsp.2011.2166245

Cited by 201 publications

(158 citation statements)

References 34 publications

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“…The performances of the reduced MP PSNR are evaluated using IRCC y N/IVC DIBR images database [13] introduced in [1]. It contains frames from 3 multi-view video sequences: Book Arrival (1024 × 768 , 16 cameras with 6.5 cm spacing), Lovebird1 (1024 × 768 , 12 cameras with 3.5 cm spacing) and Newspaper (1024 × 768 , 9 cameras with 5 cm spacing).…”

Section: Resultsmentioning

confidence: 99%

“…One key frame from each synthesized sequence is randomly chosen for the DIBR images database [1]. For these key frames subjective assessment in form of mean opinion scores (MOS) is provided.…”

Section: Resultsmentioning

confidence: 99%

“…The evaluation of DIBR synthesized views from uncompressed data using standard image quality metrics has been discussed in literature [1] for still images from FVV in 2D context. It has been demonstrated that 2D quality metrics originally designed to address image compression distortions are very far to be effective to assess the quality of synthesized views.…”

Section: Introductionmentioning

confidence: 99%

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Multi–Scale Synthesized View Assessment Based on Morphological Pyramids

Sandic-Stankovic¹,

Kukolj

Callet

2016

Journal of Electrical Engineering

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The Depth-Image-Based-Rendering (DIBR) algorithms used for 3D video applications introduce geometric distortions affecting the edge coherency in the synthesized images. In order to better deal with specific geometric distortions in the DIBR synthesized images, we propose full-reference metric based on multi-scale pyramid decompositions using morphological filters. The non-linear morphological filters used in multi-scale image decompositions maintain important geometric information such as edges across different resolution levels. We show that PSNR has particularly good agreement with human judgment when it is calculated between detailed images at higher scales of morphological pyramids. Consequently, we propose reduced morphological pyramid peak signal-to-noise ratio metric (MP-PSNR), taking into account only mean squared errors between pyramids' images at higher scales. Proposed computationally efficient metric achieves significantly higher correlation with human judgment compared to the state-of-the-art image quality assessment metrics and compared to the tested metric dedicated to synthesis-related artifacts.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi–Scale Synthesized View Assessment Based on Morphological Pyramids

Sandic-Stankovic¹,

Kukolj

Callet

2016

Journal of Electrical Engineering

Self Cite

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“…This has boosted the development of view synthesis, which is employed to generate new views from existing views [2]. Depth-image-basedrendering (DIBR) is the most commonly used technique in view synthesis [3], which has great impact on the quality of synthesized views.…”

Section: Introductionmentioning

confidence: 99%

“…Bosc et al [2] first calculated the distance map between reference and synthesized views. Then a threshold was used to obtain critical distorted regions, the mean structural similarity of which was defined as the quality score.…”

Section: Introductionmentioning

confidence: 99%

DIBR-Synthesized Image Quality Assessment via Statistics of Edge Intensity and Orientation

Zhou

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYDepth-image-based-rendering (DIBR) is a popular technique for view synthesis. The rendering process mainly introduces artifacts around edges, which leads to degraded quality. This letter proposes a DIBR-synthesized image quality metric by measuring the Statistics of both Edge Intensity and Orientation (SEIO). The Canny operator is first used to detect edges. Then the gradient maps are calculated, based on which the intensity and orientation of the edge pixels are computed for both the reference and synthesized images. The distance between the two intensity histograms and that between the two orientation histograms are computed. Finally, the two distances are pooled to obtain the overall quality score. Experimental results demonstrate the advantages of the presented method.

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