648 research outputs found
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 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
3D Wavelet Transformation for Visual Data Coding With Spatio and Temporal Scalability as Quality Artifacts: Current State Of The Art
Several techniques based on the three–dimensional (3-D) discrete cosine transform (DCT) have been proposed for visual data coding. These techniques fail to provide coding coupled with quality and resolution scalability, which is a significant drawback for contextual domains, such decease diagnosis, satellite image analysis. This paper gives an overview of several state-of-the-art 3-D wavelet coders that do meet these requirements and mainly investigates various types of compression techniques those exists, and putting it all together for a conclusion on further research scope
Efficient compression of motion compensated residuals
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Centralized and distributed semi-parametric compression of piecewise smooth functions
This thesis introduces novel wavelet-based semi-parametric centralized and distributed
compression methods for a class of piecewise smooth functions. Our proposed compression schemes are based on a non-conventional transform coding structure with simple
independent encoders and a complex joint decoder.
Current centralized state-of-the-art compression schemes are based on the conventional structure where an encoder is relatively complex and nonlinear. In addition, the
setting usually allows the encoder to observe the entire source. Recently, there has been
an increasing need for compression schemes where the encoder is lower in complexity
and, instead, the decoder has to handle more computationally intensive tasks. Furthermore, the setup may involve multiple encoders, where each one can only partially
observe the source. Such scenario is often referred to as distributed source coding.
In the first part, we focus on the dual situation of the centralized compression where
the encoder is linear and the decoder is nonlinear. Our analysis is centered around a
class of 1-D piecewise smooth functions. We show that, by incorporating parametric
estimation into the decoding procedure, it is possible to achieve the same distortion-
rate performance as that of a conventional wavelet-based compression scheme. We also
present a new constructive approach to parametric estimation based on the sampling
results of signals with finite rate of innovation.
The second part of the thesis focuses on the distributed compression scenario, where
each independent encoder partially observes the 1-D piecewise smooth function. We
propose a new wavelet-based distributed compression scheme that uses parametric estimation to perform joint decoding. Our distortion-rate analysis shows that it is possible
for the proposed scheme to achieve that same compression performance as that of a
joint encoding scheme.
Lastly, we apply the proposed theoretical framework in the context of distributed
image and video compression. We start by considering a simplified model of the video
signal and show that we can achieve distortion-rate performance close to that of a joint
encoding scheme. We then present practical compression schemes for real world signals.
Our simulations confirm the improvement in performance over classical schemes, both
in terms of the PSNR and the visual quality
Cast shadow modelling and detection
Computer vision applications are often confronted by the need to differentiate between objects and their shadows. A number of shadow detection algorithms have been
proposed in literature, based on physical, geometrical, and other heuristic techniques.
While most of these existing approaches are dependent on the scene environments and
object types, the ones that are not, are classified as superior to others conceptually
and in terms of accuracy. Despite these efforts, the design of a generic, accurate,
simple, and efficient shadow detection algorithm still remains an open problem. In
this thesis, based on a physically-derived hypothesis for shadow identification, novel,
multi-domain shadow detection algorithms are proposed and tested in the spatial and
transform domains.
A novel "Affine Shadow Test Hypothesis" has been proposed, derived, and validated
across multiple environments. Based on that, several new shadow detection algorithms
have been proposed and modelled for short-duration video sequences, where
a background frame is available as a reliable reference, and for long duration video
sequences, where the use of a dedicated background frame is unreliable. Finally, additional
algorithms have been proposed to detect shadows in still images, where the
use of a separate background frame is not possible. In this approach, the author
shows that the proposed algorithms are capable of detecting cast, and self shadows
simultaneously.
All proposed algorithms have been modelled, and tested to detect shadows in the
spatial (pixel) and transform (frequency) domains and are compared against state-of-art approaches, using popular test and novel videos, covering a wide range of
test conditions. It is shown that the proposed algorithms outperform most existing
methods and effectively detect different types of shadows under various lighting and
environmental conditions
Prioritizing Content of Interest in Multimedia Data Compression
Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph
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