2,738 research outputs found
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
State of the art in 2D content representation and compression
Livrable D1.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D3.1 du projet
A family of stereoscopic image compression algorithms using wavelet transforms
With the standardization of JPEG-2000, wavelet-based image and video
compression technologies are gradually replacing the popular DCT-based methods. In
parallel to this, recent developments in autostereoscopic display technology is now
threatening to revolutionize the way in which consumers are used to enjoying the
traditional 2D display based electronic media such as television, computer and
movies. However, due to the two-fold bandwidth/storage space requirement of
stereoscopic imaging, an essential requirement of a stereo imaging system is efficient
data compression.
In this thesis, seven wavelet-based stereo image compression algorithms are
proposed, to take advantage of the higher data compaction capability and better
flexibility of wavelets. In the proposed CODEC I, block-based disparity
estimation/compensation (DE/DC) is performed in pixel domain. However, this
results in an inefficiency when DWT is applied on the whole predictive error image
that results from the DE process. This is because of the existence of artificial block
boundaries between error blocks in the predictive error image. To overcome this
problem, in the remaining proposed CODECs, DE/DC is performed in the wavelet
domain. Due to the multiresolution nature of the wavelet domain, two methods of
disparity estimation and compensation have been proposed. The first method is
performing DEJDC in each subband of the lowest/coarsest resolution level and then
propagating the disparity vectors obtained to the corresponding subbands of
higher/finer resolution. Note that DE is not performed in every subband due to the
high overhead bits that could be required for the coding of disparity vectors of all
subbands. This method is being used in CODEC II. In the second method, DEJDC is
performed m the wavelet-block domain. This enables disparity estimation to be
performed m all subbands simultaneously without increasing the overhead bits
required for the coding disparity vectors. This method is used by CODEC III.
However, performing disparity estimation/compensation in all subbands would result
in a significant improvement of CODEC III. To further improve the performance of
CODEC ill, pioneering wavelet-block search technique is implemented in CODEC
IV. The pioneering wavelet-block search technique enables the right/predicted image
to be reconstructed at the decoder end without the need of transmitting the disparity
vectors. In proposed CODEC V, pioneering block search is performed in all subbands
of DWT decomposition which results in an improvement of its performance. Further,
the CODEC IV and V are able to perform at very low bit rates(< 0.15 bpp). In
CODEC VI and CODEC VII, Overlapped Block Disparity Compensation (OBDC) is
used with & without the need of coding disparity vector. Our experiment results
showed that no significant coding gains could be obtained for these CODECs over
CODEC IV & V.
All proposed CODECs m this thesis are wavelet-based stereo image coding
algorithms that maximise the flexibility and benefits offered by wavelet transform
technology when applied to stereo imaging. In addition the use of a baseline-JPEG
coding architecture would enable the easy adaptation of the proposed algorithms
within systems originally built for DCT-based coding. This is an important feature
that would be useful during an era where DCT-based technology is only slowly being
phased out to give way for DWT based compression technology.
In addition, this thesis proposed a stereo image coding algorithm that uses JPEG-2000
technology as the basic compression engine. The proposed CODEC, named RASTER
is a rate scalable stereo image CODEC that has a unique ability to preserve the image
quality at binocular depth boundaries, which is an important requirement in the design
of stereo image CODEC. The experimental results have shown that the proposed
CODEC is able to achieve PSNR gains of up to 3.7 dB as compared to directly
transmitting the right frame using JPEG-2000
A family of stereoscopic image compression algorithms using wavelet transforms
With the standardization of JPEG-2000, wavelet-based image and video
compression technologies are gradually replacing the popular DCT-based methods. In
parallel to this, recent developments in autostereoscopic display technology is now
threatening to revolutionize the way in which consumers are used to enjoying the
traditional 2-D display based electronic media such as television, computer and
movies. However, due to the two-fold bandwidth/storage space requirement of
stereoscopic imaging, an essential requirement of a stereo imaging system is efficient
data compression.
In this thesis, seven wavelet-based stereo image compression algorithms are
proposed, to take advantage of the higher data compaction capability and better
flexibility of wavelets. [Continues.
A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
The richness of natural images makes the quest for optimal representations in
image processing and computer vision challenging. The latter observation has
not prevented the design of image representations, which trade off between
efficiency and complexity, while achieving accurate rendering of smooth regions
as well as reproducing faithful contours and textures. The most recent ones,
proposed in the past decade, share an hybrid heritage highlighting the
multiscale and oriented nature of edges and patterns in images. This paper
presents a panorama of the aforementioned literature on decompositions in
multiscale, multi-orientation bases or dictionaries. They typically exhibit
redundancy to improve sparsity in the transformed domain and sometimes its
invariance with respect to simple geometric deformations (translation,
rotation). Oriented multiscale dictionaries extend traditional wavelet
processing and may offer rotation invariance. Highly redundant dictionaries
require specific algorithms to simplify the search for an efficient (sparse)
representation. We also discuss the extension of multiscale geometric
decompositions to non-Euclidean domains such as the sphere or arbitrary meshed
surfaces. The etymology of panorama suggests an overview, based on a choice of
partially overlapping "pictures". We hope that this paper will contribute to
the appreciation and apprehension of a stream of current research directions in
image understanding.Comment: 65 pages, 33 figures, 303 reference
Directional edge and texture representations for image processing
An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations
Efficient compression of motion compensated residuals
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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