8 research outputs found
JP3D compression of solar data-cubes: photospheric imaging and spectropolarimetry
Hyperspectral imaging is an ubiquitous technique in solar physics
observations and the recent advances in solar instrumentation enabled us to
acquire and record data at an unprecedented rate. The huge amount of data which
will be archived in the upcoming solar observatories press us to compress the
data in order to reduce the storage space and transfer times. The correlation
present over all dimensions, spatial, temporal and spectral, of solar data-sets
suggests the use of a 3D base wavelet decomposition, to achieve higher
compression rates. In this work, we evaluate the performance of the recent
JPEG2000 Part 10 standard, known as JP3D, for the lossless compression of
several types of solar data-cubes. We explore the differences in: a) The
compressibility of broad-band or narrow-band time-sequence; I or V stokes
profiles in spectropolarimetric data-sets; b) Compressing data in
[x,y,] packages at different times or data in [x,y,t] packages of
different wavelength; c) Compressing a single large data-cube or several
smaller data-cubes; d) Compressing data which is under-sampled or super-sampled
with respect to the diffraction cut-off
3D Medical Image Lossless Compressor Using Deep Learning Approaches
The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in efficient handling. Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies effectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an effective compression method. Since medical information is critical and imposes an influential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable efforts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information efficiently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the differences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the effectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input configurations and sampling schemes for a many-to-one sequence prediction model, specifically for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by different hospitals, representing different body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are significantly more informative than others, specifically in medical domains where samples are available on a scale of billions. The effectiveness of models trained on the presented importance sampling scheme was evaluated compared to alternative strategies such as uniform, Gaussian, and sliced-based sampling
Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable
Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu
Scalable video compression with optimized visual performance and random accessibility
This thesis is concerned with maximizing the coding efficiency, random accessibility and visual performance of scalable compressed video. The unifying theme behind this work is the use of finely embedded localized coding structures, which govern the extent to which these goals may be jointly achieved.
The first part focuses on scalable volumetric image compression. We investigate 3D transform and coding techniques which exploit inter-slice statistical redundancies without compromising slice accessibility. Our study shows that the motion-compensated temporal discrete wavelet transform (MC-TDWT) practically achieves an upper bound to the compression efficiency of slice transforms. From a video coding perspective, we find that most of the coding gain is attributed to offsetting the learning penalty in adaptive arithmetic coding through 3D code-block extension, rather than inter-frame context modelling.
The second aspect of this thesis examines random accessibility. Accessibility refers to the ease with which a region of interest is accessed (subband samples needed for reconstruction are retrieved) from a compressed video bitstream, subject to spatiotemporal code-block constraints. We investigate the fundamental implications of motion compensation for random access efficiency and the compression performance of scalable interactive video. We demonstrate that inclusion of motion compensation operators within the lifting steps of a temporal subband transform incurs a random access penalty which depends on the characteristics of the motion field.
The final aspect of this thesis aims to minimize the perceptual impact of visible distortion in scalable reconstructed video. We present a visual optimization strategy based on distortion scaling which raises the distortion-length slope of perceptually significant samples. This alters the codestream embedding order during post-compression rate-distortion optimization, thus allowing visually sensitive sites to be encoded with higher fidelity at a given bit-rate.
For visual sensitivity analysis, we propose a contrast perception model that incorporates an adaptive masking slope. This versatile feature provides a context which models perceptual significance. It enables scene structures that otherwise suffer significant degradation to be preserved at lower bit-rates. The novelty in our approach derives from a set of "perceptual mappings" which account for quantization noise shaping effects induced by motion-compensated temporal synthesis. The proposed technique reduces wavelet compression artefacts and improves the perceptual quality of video
Distortion-constraint compression of three-dimensional CLSM images using image pyramid and vector quantization
The confocal microscopy imaging techniques, which allow optical sectioning, have
been successfully exploited in biomedical studies. Biomedical scientists can benefit
from more realistic visualization and much more accurate diagnosis by processing and
analysing on a three-dimensional image data. The lack of efficient image compression
standards makes such large volumetric image data slow to transfer over limited
bandwidth networks. It also imposes large storage space requirements and high cost in
archiving and maintenance.
Conventional two-dimensional image coders do not take into account inter-frame
correlations in three-dimensional image data. The standard multi-frame coders, like
video coders, although they have good performance in capturing motion information,
are not efficiently designed for coding multiple frames representing a stack of optical
planes of a real object. Therefore a real three-dimensional image compression
approach should be investigated.
Moreover the reconstructed image quality is a very important concern in compressing
medical images, because it could be directly related to the diagnosis accuracy. Most of
the state-of-the-arts methods are based on transform coding, for instance JPEG is based on discrete-cosine-transform CDCT) and JPEG2000 is based on discrete-
wavelet-transform (DWT). However in DCT and DWT methods, the control
of the reconstructed image quality is inconvenient, involving considerable costs in
computation, since they are fundamentally rate-parameterized methods rather than
distortion-parameterized methods. Therefore it is very desirable to develop a
transform-based distortion-parameterized compression method, which is expected to
have high coding performance and also able to conveniently and accurately control
the final distortion according to the user specified quality requirement.
This thesis describes our work in developing a distortion-constraint three-dimensional
image compression approach, using vector quantization techniques combined with
image pyramid structures. We are expecting our method to have:
1. High coding performance in compressing three-dimensional microscopic
image data, compared to the state-of-the-art three-dimensional image coders
and other standardized two-dimensional image coders and video coders.
2. Distortion-control capability, which is a very desirable feature in medical 2. Distortion-control capability, which is a very desirable feature in medical
image compression applications, is superior to the rate-parameterized methods
in achieving a user specified quality requirement.
The result is a three-dimensional image compression method, which has outstanding
compression performance, measured objectively, for volumetric microscopic images.
The distortion-constraint feature, by which users can expect to achieve a target image
quality rather than the compressed file size, offers more flexible control of the
reconstructed image quality than its rate-constraint counterparts in medical image
applications. Additionally, it effectively reduces the artifacts presented in other
approaches at low bit rates and also attenuates noise in the pre-compressed images.
Furthermore, its advantages in progressive transmission and fast decoding make it
suitable for bandwidth limited tele-communications and web-based image browsing
applications