3D motion estimation for depth image coding in 3D video coding

Kamolrat, B.; Fernando, W.A.C.; Mrak, Marta; Kondoz, A.M.

doi:10.1109/tce.2009.5174461

Cited by 30 publications

(16 citation statements)

References 9 publications

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“…Our algorithm is different from [12,13] in three respects. First, instead of square blocks, the basic process unit of our algorithm is arbitrarily shaped sub-blocks (corresponding to objects with different motion).…”

Section: D Motion Estimationmentioning

confidence: 95%

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3-D Motion Estimation for Visual Saliency Modeling

Wan

Feng

Cheung

et al. 2013

IEEE Signal Process. Lett.

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Abstract-Visual saliency is a probabilistic estimate of how likely a spatial area in an image or video frame is to attract human visual attention relative to other areas. When existing bottom-up saliency models aggregate low-level features to construct a plausible saliency map, only 2D motion cues are used as motion features, even though videos typically capture dynamic 3D scenes. In this paper, we introduce 3D motion into bottom-up saliency modeling for texture-plus-depth videos. We first propose an efficient 3D motion estimation algorithm, which computes a 3D motion vector (3DMV) for each sub-block in the frame. Using the computed 3DMVs, we then derive several saliency channels (called 3DMV channels), which are incorporated into a bottom-up saliency model to obtain enhanced saliency maps. Experiments tracking human gaze show that incorporating our 3DMV channels into bottom-up saliency model significantly improves the accuracy of derived saliency maps.

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Section: D Motion Estimationmentioning

confidence: 95%

“…In the literature, only a few works studied motion estimation for texture-plus-depth videos [12,13]. However, they are straight-forward extensions of 2D motion estimation for video compression [14] and are incapable to recover true 3D motion information.…”

Section: Related Workmentioning

confidence: 99%

3-D Motion Estimation for Visual Saliency Modeling

Wan

Feng

Cheung

et al. 2013

IEEE Signal Process. Lett.

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“…Although motion estimation has been extensively studied for texture videos, only a few works study motion estimation for texture-plusdepth videos [8,9]. However, they are designed for video compression through 2D motion vector sharing [10].…”

Section: Related Workmentioning

confidence: 99%

“…To contain complexity, we restrict the block search to a 3D window consisting of a conventional 2D spatial search window and a 1D depth search window. Different from existing methods [8,9], our method is a joint 3D motion estimation, which uses variable block size adaptive to depth change.…”

Section: D Motion Estimationmentioning

confidence: 99%

3D motion in visual saliency modeling

Wan

Feng

Cheung

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Visual saliency is a probabilistic estimate of how likely a given spatial area in an image or video is to attract human visual attention relative to other areas. Bottom-up saliency models aggregate low-level image features like luminance and color contrast, flicker, 2D motion, etc. to construct a plausible saliency map. In this paper, we introduce 3D motion (object movements towards or away from the observer) into bottom-up video saliency modeling. Given availability of per-pixel depth maps, we first propose a novel algorithm to estimate 3D motion vectors (3DMVs) for arbitrarily shaped sub-blocks in texture-plus-depth videos. We then derive two feature channels from 3DMVs to be incorporated into a widely accepted bottom-up saliency model. Experiments on subjective quality of Region-ofInterest (ROI) based video coding show that our enriched saliency model with 3DMV channels is more accurate in estimating human visual attention.

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“…In the first approach, new compression techniques have been developed, including a platelet-based depth video coding algorithm [3], silhouette-based algorithms [4], and a modification of the existing video codec, such as object-based coding of depth video [5], 3D-motion-estimation-based methods [6], and a lossless-compression technique for a depth video [7].…”

Section: Introductionmentioning

confidence: 99%

Post-processing of 3D Video Extension of H.264/AVC for a Quality Enhancement of Synthesized View Sequences

Bang

Hur

Lee

2014

ETRI J

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Since July of 2012, the 3D video extension of H.264/AVC has been under development to support the multi-view video plus depth format. In 3D video applications such as multi-view and free-view point applications, synthesized views are generated using coded texture video and coded depth video. Such synthesized views can be distorted by quantization noise and inaccuracy of 3D wrapping positions, thus it is important to improve their quality where possible. To achieve this, the relationship among the depth video, texture video, and synthesized view is investigated herein. Based on this investigation, an edge noise suppression filtering process to preserve the edges of the depth video and a method based on a total variation approach to maximum a posteriori probability estimates for reducing the quantization noise of the coded texture video. The experiment results show that the proposed methods improve the peak signal-to-noise ratio and visual quality of a synthesized view compared to a synthesized view without post processing methods.

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3D motion estimation for depth image coding in 3D video coding

Cited by 30 publications

References 9 publications

3-D Motion Estimation for Visual Saliency Modeling

3-D Motion Estimation for Visual Saliency Modeling

3D motion in visual saliency modeling

Post-processing of 3D Video Extension of H.264/AVC for a Quality Enhancement of Synthesized View Sequences

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