Multiple-view Video Coding Using Depth Map in Projective Space

Yorozu, Nina; Uematsu, Yuko; Saitō, Hideo

doi:10.1007/978-3-642-10520-3_62

Cited by 1 publication

(2 citation statements)

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“…We refer to this algorithm as Multi-view Matching Pursuit (MVMP) 2 . We can immediately see that MVMP is a special case of the class of MP algorithms that we call the constrained MP.…”

Section: Joint Sparse Approximation With Consistent Geometrymentioning

confidence: 99%

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MVMP: Multi-view Matching Pursuit with geometry constraints

Tošić

Ortega

Frossard

2010

2010 IEEE International Conference on Image Processing

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Sets of multi-view images that capture plenoptic information from different viewpoints are typically related by geometric constraints. The proper analysis of these constraints is key to the definition of consistent compact representations of such images. We propose an algorithm for joint sparse approximation of multi-view images driven by epipolar geometry considerations. We extend greedy pursuit algorithms, such that the representation of multi-view images into linear combination of geometric atoms is able to balance approximation error and geometric consistency. We further add a rate penalty constraint that favors representations with small entropy towards efficient coding applications. Experimental results illustrate the trade-off between approximation, geometry and rate constraints in the representation of stereo omnidirectional images. In particular, we show that geometry constraints lead to a consistent description of the correlation among views, which is particularly beneficial for scene analysis or view interpolation applications. At the same time, we show that the rate constraint leads to compact representations, possibly to the detriment of geometry consistency.

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“…We refer to this algorithm as Multi-view Matching Pursuit (MVMP) 2 . We can immediately see that MVMP is a special case of the class of MP algorithms that we call the constrained MP.…”

Section: Joint Sparse Approximation With Consistent Geometrymentioning

confidence: 99%

“…parallel arrangements. Other coding solutions based on dense depth map or visual correspondences matching are often limited in capturing the true 3D geometry in arbitrary camera arrangements [2], or face the difficult problem of rate allocation between texture and disparity or depth information [3].…”

Section: Introductionmentioning

confidence: 99%