Spatial-Temporal Consistency in Video Disparity Estimation

ICASSP 2011

Ramsin Khoshabeh , Stanley H. Chan,

Truong Q. Nguyen

Outline

• Introduction• Proposed Method– Image-based disparity map estimation– Temporal consistency

• Experimental Result

Introduction

• Stereo disparity estimation is an integral problem associated with 3D content delivery

• Two type of existing algorithm – Local – Global : minimizing energy function

• Even applying the best of existing methods to individual frames of stereo sequences yields temporally inconsistent disparity maps

(fast, lack the accuracy)

(slow)

Introduction

• The goal is to present a method to generate accurate and spatio-temporally consistent disparity maps from complex stereo video sequences.

Proposed Method

• Use Image-based technique– Video disparity problem in space-time is

computationally impractical– But we lose the consistency between consecutive

frames noisy• Improve the temporal consistency

Proposed MethodImage-based disparity map estimation

• In this step, disparity maps are computed for each frame individually.

• Use a global method using Hierarchical Belief Propagation (HBP) for inferencing.

• Energy function : P : set of pixels in an imageL : finite set of labelsA labeling f assigns a label fp ϵ L to each pixel p ϵ P

Data cost Discontinuity costHow well the labeling fit the node

Proposed MethodImage-based disparity map estimation

• Discontinuity cost enforces the assumption that labels should vary slowly.

• Except for significant changes along object boundaries

Proposed MethodImage-based disparity map estimation

• Data cost is computed over a large window for each pixel using locally adaptive support weights [11]

[11] K. J. Yoon and I. S. Kweon, “Locally Adaptive Support-Weight Approach for Visual Correspondence Search,” in CVPR, 2005.

Strength of grouping by similarity

Strength of grouping by proximity

: Color difference: Spatial distance

only points with a high probability of belonging to the same object contribute significantly to the cost calculation

Proposed MethodImage-based disparity map estimation

• use the method of [5] to minimize the energy over the entire image in a coarse-to-fine manner.

• Use the hierarchy to reduce the number of message passing iterations .

[5] P. Felzenszwalb and D. Huttenlocher, “Efficient Belief Propagation for Early Vision,” in CVPR, 2004, pp. 261–268.

Proposed Methodtemporal consistency

• Disparity should be a piecewise smooth function in time, except for discontinuities at object borders

• Consider the sequence of disparity maps as a space-time volume

x

yt

Proposed Methodtemporal consistency

TV-norm

Add (βx, βy, βt) so that we can control the relative emphasis

Total variation

Forward difference

[14] S. H. Chan, R. Khoshabeh, K. B. Gibson, P. E. Gill, and T. Q. Nguyen,“An augmented lagrangian method for total variation video restoration,” in ICASSP, May 2011

allows us to handle both spatial and temporal consistency simultaneously,by tuning the parameters (βx, βy, βt)

Experimental Result

Spatial noise Temporally inconsistencies

Remove errorPreserve object edges

Experimental Result

Experimental Result

• Add Gaussian noise to simulate real sequences• Bad pixel is defines as any pixel that has an estimated

disparity that |Dest – Dreal | > threshold(set at 1)

Experimental Result

Evaluate the efficacy of proposed TV method in improving arbitrary disparity estimates.