We propose a novel, content adaptive method for motion-compensated three-dimensional wavelet transformation (MC 3-D DWT) of video. The proposed method overcomes problems of ghosting and nonaligned aliasing artifacts which can arise in regions of motion model failure, when the video is reconstructed at reduced temporal or spatial resolutions. Previous MC 3-D DWT structures either take the form of MC temporal DWT followed by a spatial transform ("t+2D"), or perform the spatial transform first ("2D + t"), limiting the spatial frequencies which can be jointly compensated in the temporal transform, and hence limiting the compression efficiency. When the motion model fails, the "t + 2D" structure causes nonaligned aliasing artifacts in reduced spatial resolution sequences. Essentially, the proposed transform continuously adapts itself between the "t + 2D" and "2D + t" structures, based on information available within the compressed bit stream. Ghosting artifacts may also appear in reduced frame-rate sequences due to temporal low-pass filtering along invalid motion trajectories. To avoid the ghosting artifacts, we continuously select between different low-pass temporal filters, based on the estimated accuracy of the motion model. Experimental results indicate that the proposed adaptive transform preserves high compression efficiency while substantially improving the quality of reduced spatial and temporal resolution sequences.

We investigate the implications of the conventional "t+2-D" motion-compensated (MC) three-dimensional (3-D) discrete wavelet/subband transform structure for spatial scalability and propose a novel flexible structure for fully scalable video compression. In this structure, any number of levels of "pretemporal" spatial wavelet decomposition are performed on the original full resolution frames, followed by MC temporal decomposition of the subbands within each spatial resolution level. Further levels of "posttemporal" spatial decomposition may be performed on the spatiotemporal subbands to provide additional levels of spatial scalability and energy compaction. This structure allows us to trade energy compaction against the potential for artifacts at reduced spatial resolutions. More importantly, the structure permits extensive study of the interaction between spatial aliasing, scalability and energy compaction. We show that where the motion model fails, the "t+2-D" structure inevitably produces misaligned spatial aliasing artifacts in reduced resolution sequences. These artifacts can be removed by using pretemporal spatial decomposition. On the other hand, we also show that the "t+2-D" structure necessarily maximizes compression efficiency. We propose different schemes to minimize the loss of compression efficiency associated with pretemporal spatial decomposition.

Motion-compensated temporal wavelet decomposition is a useful framework for fully scalable video compression schemes. In this paper we propose a new approach to reduce the ghosting artifacts in low-pass temporal suhhands; we adaptively weight the update steps according to the energy in the high-pass temporal suhhands 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 hit-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 enor.

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In this paper, we propose an orientation adaptive discrete wavelet transform (DWT) with perfect reconstruction. The proposed transform utilizes the lifting structure to effectively orient the 2D-DWT bases in the direction of local image features. A shifting operator is employed within each lifting step to align spatial geometric features along the vertical or horizontal directions. The proposed oriented transform generates a scalable representation for the image and the orientation information. To approximate the asymptotically optimal rate-distortion performance of a piecewise regular function more closely, we adopt a packet wavelet decomposition. The experimental results obtained by implementing the proposed transform in a JPEG2000 codec illustrate superior compression performance for the oriented transform with more than 2.5 dB improvement for highly oriented natural images. More importantly, even at the same PSNR, the proposed scheme reduces the visual appearance of the Gibbs-like artifacts significantly, considerably improving the visual quality of the reconstructed image.