Lifting Transforms on Graphs for Video Coding

Abstract

We present a new graph-based transform for video signals using wavelet lifting. Graphs are created to capture spatial and temporal correlations in video sequences. Our new transforms allow spatial and temporal correlation to be jointly exploited, in contrast to existing techniques, such as motion compensated temporal filtering, which can be seen as "separable" transforms, since spatial and temporal filtering are performed separately. We design efficient ways to form the graphs and to design the prediction and update filters for different levels of the lifting transform as a function of expected degree of correlation between pixels. Our initial results are promising, with improvements in performance as compared to existing methods in terms of PSNR as a function of the percentage of retained coefficients of the transform.