Robust regression and outlier detection
Robust regression and outlier detection
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
YUV Correction for Multi-View Video Compression
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Fast Disparity and Motion Estimation for Multi-view Video Coding
IEEE Transactions on Consumer Electronics
Epipolar line estimation and rectification for stereo image pairs
IEEE Transactions on Image Processing
An Epipolar Geometry-Based Fast Disparity Estimation Algorithm for Multiview Image and Video Coding
IEEE Transactions on Circuits and Systems for Video Technology
Multiview Video Coding Using View Interpolation and Color Correction
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive Local Illumination Change Compensation Method for H.264/AVC-Based Multiview Video Coding
IEEE Transactions on Circuits and Systems for Video Technology
Histogram-Based Prefiltering for Luminance and Chrominance Compensation of Multiview Video
IEEE Transactions on Circuits and Systems for Video Technology
Stereo/multiview picture quality: Overview and recent advances
Image Communication
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In multiview video, a number of cameras capture the same scene from different viewpoints. There can be significant variations in the color of views captured with different cameras, which negatively affects performance when the videos are compressed with inter-view prediction. In this letter, a method is proposed for correcting the color of multiview video sets as a preprocessing step to compression. Unlike previous work, where one of the captured views is used as the color reference, we correct all views to match the average color of the set of views. Block-based disparity estimation is used to find matching points between all views in the video set, and the average color is calculated for these matching points. A least-squares regression is performed for each view to find a function that will make the view most closely match the average color. Experimental results show that when multiview video is compressed with Joint Multiview Video Model, the proposed method increases compression efficiency by up to 1.0 dB in luma peak signal-to-noise ratio (PSNR) compared to compressing the original uncorrected video.