In this paper, we propose two multiview image compression methods. The basic concept of both schemes is
the layer-based representation, in which the captured three-dimensional (3D) scene is partitioned into layers
each related to a constant depth in the scene. The first algorithm is a centralized scheme where each layer is
de-correlated using a separable multi-dimensional wavelet transform applied across the viewpoint and spatial
dimensions. The transform is modified to efficiently deal with occlusions and disparity variations for different
depths. Although the method achieves a high compression rate, the joint encoding approach requires the transmission
of all data to the users. By contrast, in an interactive setting, the users request only a subset of the
captured images, but in an unknown order a priori. We address this scenario in the second algorithm using
Distributed Source Coding (DSC) principles which reduces the inter-view redundancy and facilitates random
access at the image level. We demonstrate that the proposed centralized and interactive methods outperform
H.264/MVC and JPEG 2000, respectively.
The standard separable two-dimensional (2-D) wavelet transform (WT) has recently achieved a great success
in image processing because it provides a sparse representation of smooth images. However, it fails to capture
efficiently one-dimensional (1-D) discontinuities, like edges or contours. These features, being elongated and
characterized by geometrical regularity along different directions, intersect and generate many large magnitude
wavelet coefficients. Since contours are very important elements in visual perception of images, to provide a
good visual quality of compressed images, it is fundamental to preserve good reconstruction of these directional
features. We propose a construction of critically sampled perfect reconstruction transforms with directional
vanishing moments (DVMs) imposed in the corresponding basis functions along different directions, called directionlets.
We also demonstrate the outperforming non-linear approximation (NLA) results achieved by our transforms and we show how to design and implement a novel efficient space-frequency quantization (SFQ) compression algorithm using directionlets. Our new compression method beats the standard SFQ both in terms of mean-square-error (MSE) and visual quality, especially in the low-rate compression regime. We also show that our compression method, does not increase the order of computational complexity as compared to the standard SFQ algorithm.
The application of the wavelet transform in image processing is most frequently based on a separable construction. Lines and columns in an image are treated independently and the basis functions are simply products of the corresponding one dimensional functions. Such method keeps simplicity in design and computation, but is not capable of capturing properly all the properties of an image. In this paper, a new truly separable discrete multi-directional transform is proposed with a subsampling method based on lattice theory. Alternatively, the subsampling can be omitted and this leads to a multi-directional frame. This transform can be applied in many areas like denoising, non-linear approximation and compression. The results on non-linear approximation and denoising show interesting gains compared to the standard two-dimensional analysis.
The application of the wavelet transform in image processing
is most frequently based on a separable construction. Lines and columns in an image are treated independently and the basis functions are simply products of the corresponding one dimensional functions.
Such method keeps simplicity in design and computation, but is not capable of capturing properly all the properties of an image. In this paper, a new truly separable discrete multi-directional transform is proposed with a subsampling method based on lattice theory. Alternatively, the subsampling can be omitted and this leads to a multi-directional frame. This transform can be applied in many areas like denoising, non-linear approximation and compression. The results on non-linear approximation and denoising show very interesting gains compared to the standard two-dimensional analysis.
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