Motion-Compensated Temporal Filtering for Critically-Sampled Wavelet-Encoded Images
This is an incremental improvement for video coding applications, addressing a specific bottleneck in wavelet-based compression.
The paper tackles the problem of shift-variance in critically sampled wavelet transforms for low-bitrate video coding by proposing a motion estimation/compensation method that operates directly on wavelet coefficients, eliminating redundancy and interpolation steps. Experimental results show it effectively improves video coding quality at very low bitrates.
We propose a novel motion estimation/compensation (ME/MC) method for wavelet-based (in-band) motion compensated temporal filtering (MCTF), with application to low-bitrate video coding. Unlike the conventional in-band MCTF algorithms, which require redundancy to overcome the shift-variance problem of critically sampled (complete) discrete wavelet transforms (DWT), we perform ME/MC steps directly on DWT coefficients by avoiding the need of shift-invariance. We omit upsampling, the inverse-DWT (IDWT), and the calculation of redundant DWT coefficients, while achieving arbitrary subpixel accuracy without interpolation, and high video quality even at very low-bitrates, by deriving the exact relationships between DWT subbands of input image sequences. Experimental results demonstrate the accuracy of the proposed method, confirming that our model for ME/MC effectively improves video coding quality.