904 research outputs found
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
Vision-Aided Navigation for GPS-Denied Environments Using Landmark Feature Identification
In recent years, unmanned autonomous vehicles have been used in diverse applications because of their multifaceted capabilities. In most cases, the navigation systems for these vehicles are dependent on Global Positioning System (GPS) technology. Many applications of interest, however, entail operations in environments in which GPS is intermittent or completely denied. These applications include operations in complex urban or indoor environments as well as missions in adversarial environments where GPS might be denied using jamming technology.
This thesis investigate the development of vision-aided navigation algorithms that utilize processed images from a monocular camera as an alternative to GPS. The vision-aided navigation approach explored in this thesis entails defining a set of inertial landmarks, the locations of which are known within the environment, and employing image processing algorithms to detect these landmarks in image frames collected from an onboard monocular camera. These vision-based landmark measurements effectively serve as surrogate GPS measurements that can be incorporated into a navigation filter. Several image processing algorithms were considered for landmark detection and this thesis focuses in particular on two approaches: the continuous adaptive mean shift (CAMSHIFT) algorithm and the adaptable compressive (ADCOM) tracking algorithm. These algorithms are discussed in detail and applied for the detection and tracking of landmarks in monocular camera images. Navigation filters are then designed that employ sensor fusion of accelerometer and rate gyro data from an inertial measurement unit (IMU) with vision-based measurements of the centroids of one or more landmarks in the scene. These filters are tested in simulated navigation scenarios subject to varying levels of sensor and measurement noise and varying number of landmarks. Finally, conclusions and recommendations are provided regarding the implementation of this vision-aided navigation approach for autonomous vehicle navigation systems
Scalable coding of HDTV pictures using the MPEG coder
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 118-121).by Adnan Husain Lawai.M.S
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
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.
Recent Developments in Smart Healthcare
Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine
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
Efficient compression of motion compensated residuals
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
High performance shift invariant motion estimation and compensation in wavelet domain video compression
The contributions of this dissertation are in the development of two new interrelated approaches to video data compression:
1) A level-refined motion estimation and subband compensation method for the effective motion estimation and motion compensation.
2) A shift-invariant sub-decimation decomposition method in order to overcome the deficiency of the decimation process in estimating motion due to its shift-invariant property of wavelet transform.
The enormous data generated by digital videos call for an intense need of efficient video compression techniques to conserve storage space and minimize bandwidth utilization. The main idea of video compression is to reduce the interpixel redundancies inside and between the video frames by applying motion estimation and motion compensation (MEMC) in combination with spatial transform coding. To locate the global minimum of the matching criterion function reasonably, hierarchical motion estimation by coarse to fine resolution refinements using discrete wavelet transform is applied due to its intrinsic multiresolution and scalability natures
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