Improved depth map filtering for 3D-TV systems

Silva, Varuna De; Fernando, W.A.C.; Kodikaraarachchi, H.; Kondoz, A.M.

doi:10.1109/icce.2011.5722787

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Next, the proposed method aligns I C and I D . For image alignment, existing methods [15, 16] use joint bilateral filter [17], whereas the proposed method uses depth‐adaptive joint bilateral filter [16] that considers depth discontinuity. This helps stable alignment when an area has depth discontinuity and no significant gradient edges in I C .…”

Section: Proposed Segmentation Methods Using Colour and Depth Imagesmentioning

confidence: 99%

Segmentation with saliency map using colour and depth images

Lee

Park

2015

IET Image Processing

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Section: Proposed Segmentation Methods Using Colour and Depth Imagesmentioning

confidence: 99%

Segmentation with saliency map using colour and depth images

Lee

Park

2015

IET Image Processing

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“…Based on the BF [90], which is one of the most popular local filtering methods, Silva et al [145] propose an adaptive JBF, whereby the depth map is guided by the corresponding texture view sequence for eliminating the compression artefacts. Similar to [145], Liu et al [146, 147] propose a TF by using the depth map and the corresponding colour view image in the range domain of the filter to improve the quality of depth maps and mitigate the compression artefacts. All of these mentioned methods are colour‐guided pixel‐based methods.…”

Section: Depth Map Artefacts Reductionmentioning

confidence: 99%

Depth map artefacts reduction: a review

et al. 2020

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“…The resulting graph for the selected model obtained from the curve-fitting process is depicted in Figure 5. matching (HRM) [31] based depth map; (b) segment tree (ST) [32] based depth map; (c) rapid semi global matching (RSGM) [33] based depth map; (d) RSGM with joint bilateral filter (RSGM_FIL) [34,35] based depth map.…”

Section: Dec Measurement-based No-reference Quality Evaluation Modelmentioning

confidence: 99%

No-Reference Depth Map Quality Evaluation Model Based on Depth Map Edge Confidence Measurement in Immersive Video Applications

et al. 2019

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When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance.

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Improved depth map filtering for 3D-TV systems

Cited by 10 publications

References 5 publications

Segmentation with saliency map using colour and depth images

Segmentation with saliency map using colour and depth images

Depth map artefacts reduction: a review

No-Reference Depth Map Quality Evaluation Model Based on Depth Map Edge Confidence Measurement in Immersive Video Applications

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