Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
QuickTime VR: an image-based approach to virtual environment navigation
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Creating full view panoramic image mosaics and environment maps
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Stereo Reconstruction from Multiperspective Panoramas
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Mosaicing: Polarization Panorama
IEEE Transactions on Pattern Analysis and Machine Intelligence
The plenoptic illumination function
IEEE Transactions on Multimedia
A system for real-time panorama generation and display in tele-immersive applications
IEEE Transactions on Multimedia
Automatic feature-based global motion estimation in video sequences
IEEE Transactions on Consumer Electronics
Fast motion estimation using bidirectional gradient methods
IEEE Transactions on Image Processing
Survey of image-based representations and compression techniques
IEEE Transactions on Circuits and Systems for Video Technology
Data compression and transmission aspects of panoramic videos
IEEE Transactions on Circuits and Systems for Video Technology
Affine multipicture motion-compensated prediction
IEEE Transactions on Circuits and Systems for Video Technology
Motion compensation based on tangent distance prediction for video compression
Image Communication
Hi-index | 0.00 |
This paper proposes an affine motion compensated prediction (AMCP) method to predict the complex changes between the successive frames in panoramic video coding. A panoramic video is an image-based rendering (IBR) technique [1] which provides users with a large field of view (e.g. 360 degree) on surrounding dynamic scenes. It includes not only the translational motions but also the non-translational motions, such as zooming and rotation etc. However, the traditional motion compensated prediction is a translational motion compensated prediction (TMCP) which cannot predict nontranslational changes between panoramic images accurately. The AMCP can model the nontranslational motion effects of panoramic video accurately by using six motion coefficients which are estimated by Gauss Newton iterative minimization algorithm [2]. Simulated results show that the gain of coding performance is up to about 1.3 dB when using AMCP compared with TMCP in panoramic video coding.