Lifting-based invertible motion adaptive transform (LIMAT) framework for highly scalable video compression.
We propose a new framework for highly scalable video compression, using a lifting-based invertible motion adaptive transform (LIMAT). We use motion-compensated lifting steps to implement the temporal wavelet transform, which preserves invertibility, regardless of the motion model. By contrast, the invertibility requirement has restricted previous approaches to either block-based or global motion compensation. We show that the proposed framework effectively applies the temporal wavelet transform along a set of motion trajectories. An implementation demonstrates high coding gain from a finely embedded, scalable compressed bit-stream. Results also demonstrate the effectiveness of temporal wavelet kernels other than the simple Haar, and the benefits of complex motion modeling, using a deformable triangular mesh. These advances are either incompatible or difficult to achieve with previously proposed strategies for scalable video compression. Video sequences reconstructed at reduced frame-rates, from subsets of the compressed bit-stream, demonstrate the visually pleasing properties expected from low-pass filtering along the motion trajectories. The paper also describes a compact representation for the motion parameters, having motion overhead comparable to that of motion-compensated predictive coders. Our experimental results compare favorably to others reported in the literature, however, our principal objective is to motivate a new framework for highly scalable video compression.
Secker
Andrew
A
School of Electrical Engineering, The University of New South Wales, Sydney 2052, Australia.
Taubman
David
D