Adaptively weighted update steps in motion compensated lifting based scalable video compression

Motion-compensated temporal wavelet decomposition is a use-framework for fully scalable video compression schemes. In paper we propose a new approach to reduce the ghosting artifacts in low-pass temporal subbands; we adaptively weight the update steps according to the energy in the high-pass temporal sub-bands at the corresponding location. Experimental results show that the proposed algorithm can substantially remove ghosting from low-pass temporal frames. Importantly, at full frame-rate, the proposed algorithm has similar performance to the original motion-compensated temporal decomposition, with superior performance where the motion model fails significantly. While entirely skipping the update steps accomplishes a similar objective, we show that the proposed method for adaptively weighting the update steps has better performance, especially in the presence of additive noise. Since the compressed bit-stream is scalable, the decoder does not generally have exactly the same information which the encoder used to determine weights for the update steps. Nevertheless, the proposed method exhibits good robustness to quantization error.