3D motion estimation for depth information compression in 3D-TV applications

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

doi:10.1049/el:20081455

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…However, as shown in [4], [9], the application of 3D-BM introduces extra bits for coding motion vector of the z-component. Operation at a frame level, a good performance is achieved only at a high bit rate region where saving bits due to small prediction error exceeds extra bits due to additional motion vector.…”

Section: Adaptive Selection Of 2d-3d Motion Vectersmentioning

confidence: 99%

“…Due to such special characteristics, it is not optimal to code a depth video with traditional video coding techniques. In [4], under block-based video coding, three-dimensional block matching (3D-BM) was introduced for more accurate motion estimation in depth video coding; however, the overall performance of such a motion estimation algorithm provides good results compared to traditional two-dimensional block matching (2D-BM) only at high bit rate. Significant performance improvement is obvious in sequences which have high motion in a depth direction.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive motion-estimation-mode selection for depth video coding

Kamolrat

Fernando

Mrak

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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An effective representation of 3D video in future 3D-TV systems consists of monoscopic video (colour component) and associated per-pixel depth information (depth component). As depth component indicates relative distance between objects within the scene and a camera, pixel values change not only when objects move in vertical and horizontal directions but also when they move in a depth direction. Instead of predicting motion of objects in two directions as appearing in traditional video codecs, three-dimensional block matching (3D-BM) achieves more accurate motion estimation in depth video coding. However overall performance of 3D-BM exceeds that of the traditional two-dimensional block matching (2D-BM) only at high bit rate. In this paper, an adaptive 2D-3D BM selection algorithm is introduced to compromise performance of 2DBM and 3D-BM. The Lagrangian optimisation algorithm is applied to select motion estimation mode at a block level. The experiment results reveal that the proposed adaptive motion-estimation-mode selection can improve the performance of 3D-BM at low bit rate while advantages of 3D-BM are preserved at high bit rate

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Section: Adaptive Selection Of 2d-3d Motion Vectersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive motion-estimation-mode selection for depth video coding

Kamolrat

Fernando

Mrak

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

Self Cite

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“…With the rapid development of modern sensors and big data technology, image matching has been applied to many computer vision applications, such as multiple sensor fusion [1,2], 3D reconstruction [3], and depth information estimation [4,5]. Nevertheless, when the intensity value of an image pair contains an obvious nonlinear variation, image matching becomes very challenging.…”

Section: Introductionmentioning

confidence: 99%

Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes

Wang

2018

Mathematical Problems in Engineering

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This paper presents a new feature descriptor that is suitable for image matching under nonlinear intensity changes. The proposed approach consists of the following three steps. First, a binary local patch clustering transform response is employed as the transform space. The value of the new space exhibits a high similarity after changes in intensity. Then, a random binary pattern coding method extracts raw feature histograms from the new space. Finally, the discrimination of the proposed feature descriptor is enhanced by using a multiple spatial support region-based binning method. Experimental results show that the proposed method is able to provide a more robust image matching performance under nonlinear intensity changes.

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Compression

2013

3dtv

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3D motion estimation for depth information compression in 3D-TV applications

Cited by 8 publications

References 3 publications

Adaptive motion-estimation-mode selection for depth video coding

Adaptive motion-estimation-mode selection for depth video coding

Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes

Compression

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