Motion-compensated highly scalable video compression using an adaptive 3D wavelet transform based on lifting

Secker, Andrew; Taubman, David

doi:10.1109/icip.2001.958672

Cited by 165 publications

(114 citation statements)

References 7 publications

(9 reference statements)

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“…It should be emphasized that in principle the MC in the prediction and the IMC in the update step could be independent. This still would guarantee perfect reconstruction by the inherent properties of the lifting structure, as was shown in [36]). Nevertheless, the match between MC and IMC is important to guarantee undistorted L frames and retain high compression performance.…”

Section: Article In Pressmentioning

confidence: 99%

“…This means that it will now be possible to release the restriction of full-pixel shifts and gain perfect reconstruction for arbitrary motion vector fields. The interpretation by lifting filters was first made in [24,34,36]. A special case had previously been developed in [32], where it was shown that the polyphase kernels of 1D or 2D biorthogonal filter pairs can be used as perfect-reconstructing interpolation filters in the case of a half-pixel accurate motion compensation with temporal-axis Haar filters; the gain achievable by this method in an operational MCTF coding system was first reported in [8].…”

Section: Article In Pressmentioning

confidence: 99%

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Interframe wavelet coding—motion picture representation for universal scalability

Ohm

Schaar

Woods

2004

Signal Processing: Image Communication

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Scalability at the bitstream level is an important feature for encoded video that is to be transmitted and stored with a variety of target rates or to be replayed on devices with different capabilities and resolutions. This is attractive for digital cinema applications, where the same encoded source representation could seamlessly be used for purposes of archival and various distribution channels. Conventional high-performance video compression schemes are based on the method of motion-compensated prediction, using a recursive loop in the prediction process. Due to this recursion and the inherent drift in cases of deviation between encoder and decoder states, scalability is difficult to realize and typically effects a penalty in compression performance for prediction-based coders. The method of interframe wavelet coding overcomes this limitation by replacing the prediction along the time axis by a wavelet filter, which can nevertheless be operated in combination with motion compensation. Recent advances in motion-compensated temporal filtering (MCTF) have proven that combination with arbitrary motion compensation methods is possible. Compression performance is achieved that is comparable with state of the art single-layer coders targeting only for one rate. The paper provides an explanation of MCTF methods and the resulting 3D wavelet representation, and shows results obtained in the context of encoding digital cinema (DC) materials. r

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Section: Article In Pressmentioning

confidence: 99%

Section: Article In Pressmentioning

confidence: 99%

Interframe wavelet coding—motion picture representation for universal scalability

Ohm

Schaar

Woods

2004

Signal Processing: Image Communication

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“…A summary of wavelet video compression techniques is provided by Woods et al in [29]. Advances in the area include the implementations of MCTF using lifting, proposed recently in [14] and [19].…”

Section: Article In Pressmentioning

confidence: 99%

“…Lifting implementations of MCTF [14] and [19] increase its flexibility significantly and as a result, improved motion-compensation features like bidirectional filtering, multiple reference frames etc. can all be introduced into MCTF.…”

Section: Lifting Implementations Of Mctfmentioning

confidence: 99%

Unconstrained motion compensated temporal filtering (UMCTF) for efficient and flexible interframe wavelet video coding

Turaga¹,

Schaar²,

Andreopoulos³

et al. 2005

Signal Processing: Image Communication

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We introduce an efficient and flexible framework for temporal filtering in wavelet-based scalable video codecs called unconstrained motion compensated temporal filtering (UMCTF). UMCTF allows for the use of different filters and temporal decomposition structures through a set of controlling parameters that may be easily modified during the coding process, at different granularities and levels. The proposed framework enables the adaptation of the coding process to the video content, network and end-device characteristics, allows for enhanced scalability, content-adaptivity and reduced delay, while improving the coding efficiency as compared to state-of-the-art motion-compensated wavelet video coders. Additionally, a mechanism for the control of the distortion variation in video coding based on UMCTF employing only the predict step is proposed. The control mechanism is formulated by expressing the distortion in an arbitrary decoded frame, at any temporal level in the pyramid, as a function of the distortions in the reference frames at the same temporal level. All the different scenarios proposed in the paper are experimentally validated through a coding scheme that incorporates advanced features (such as rate-distortion optimized variable block-size multihypothesis prediction and overlapped block motion compensation). Experiments are carried out to determine the relative efficiency of different UMCTF instantiations, as well as to compare against the current state-of-the-art in video coding. r

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“…In the static CS MRI, shearlet and curvelet [146,147] sparsity transforms can preserve the edges and features in image reconstruction because of the excellent directional selectivity and localization property. In addition to these 2D sparsity transforms, a series of high-order sparsity transforms like 3D wavelet transform [148], 3D…”

Section: Improvement To Current Solutionsmentioning

confidence: 99%

Improving the image quality in compressed sensing MRI by the exploitation of data properties

Yang¹

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Minimizing scan time without compromising image quality has been the main thrust of magnetic resonance imaging (MRI) since the establishment of the field. Tremendous effort has been put into MRI systems in pursuit of faster MR imaging techniques, which can substantially facilitate clinical diagnoses and improve the patient experience. Traditional MRI accelerated approaches mainly focused on hardware improvements to improve the speed of scanning. These methods are still governed by Nyquist-Shannon sampling rate. Hence, when the acceleration rate is pushed beyond the theoretical limit, aliasing artefacts are introduced which degrade the reconstructed image quality and are difficult to eliminate via current methods. Recently, it has been found that a newly developed signal processing theory, compressed sensing (CS), can be used to create high-resolution signal data sets from sparse samples, despite violating the classical Nyquist-Shannon sampling criteria. The application of CS to MRI opens up an appealing avenue to offer potentially significant scan time reductions. CS reconstructs MR images from incomplete k-space measurements. Taking advantage of the fact that MR images have sparse representations under certain mathematical transformations, CS overcomes the distortions resulting from the under-sampled k-space data. Significant progress has been made in the development of CS MRI. However, there are two major remaining challenges to its full adoption in clinical applications: (1) the small amount of under-sampled data (for example, less than 1/8 of the whole k-space data) will inherently introduce artefacts in the reconstructed image and compromise diagnosis accuracy for the reconstructed images; (2) owing to hardware limits, it is difficult to realise two-dimension random under-sampling, which is favoured by CS operation.Practically, the random phase-encode under-sampling pattern has been widely implemented.However, it introduces coherent aliasing artefacts that are hard to eliminate using the CS theory.In this thesis, several techniques are proposed that can improve the quality of images reconstructed by CS MRI. These techniques have the potential to facilitate faithful reconstruction of CS MRI in clinical applications. A novel two-stage reconstruction scheme is introduced in Chapter 3. In this method, the under-sampled k-space data is segmented into low-frequency and high-frequency parts.Then, in stage one, using dense measurements, the low-frequency region of k-space data is faithfully reconstructed. The fully reconstituted low-frequency k-space data from the first stage is then combined with the under-sampled high-frequency k-space data to complete the second stage reconstruction of the whole image. With this two-stage approach, each reconstruction inherently incorporates a lower data under-sampling rate than conventional approaches. Because the restricted isometric property is easier to satisfy than conventional CS method, the reconstruction consequently ii produces lower residual errors in each step...

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Motion-compensated highly scalable video compression using an adaptive 3D wavelet transform based on lifting

Cited by 165 publications

References 7 publications

Interframe wavelet coding—motion picture representation for universal scalability

Interframe wavelet coding—motion picture representation for universal scalability

Unconstrained motion compensated temporal filtering (UMCTF) for efficient and flexible interframe wavelet video coding

Improving the image quality in compressed sensing MRI by the exploitation of data properties

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