206 research outputs found
Algorithms for compression of high dynamic range images and video
The recent advances in sensor and display technologies have brought upon the High Dynamic Range (HDR) imaging capability. The modern multiple exposure HDR sensors can achieve the dynamic range of 100-120 dB and LED and OLED display devices have contrast ratios of 10^5:1 to 10^6:1.
Despite the above advances in technology the image/video compression algorithms and associated hardware are yet based on Standard Dynamic Range (SDR) technology, i.e. they operate within an effective dynamic range of up to 70 dB for 8 bit gamma corrected images. Further the existing infrastructure for content distribution is also designed for SDR, which creates interoperability problems with true HDR capture and display equipment.
The current solutions for the above problem include tone mapping the HDR content to fit SDR. However this approach leads to image quality associated problems, when strong dynamic range compression is applied. Even though some HDR-only solutions have been proposed in literature, they are not interoperable with current SDR infrastructure and are thus typically used in closed systems.
Given the above observations a research gap was identified in the need for efficient algorithms for the compression of still images and video, which are capable of storing full dynamic range and colour gamut of HDR images and at the same time backward compatible with existing SDR infrastructure. To improve the usability of SDR content it is vital that any such algorithms should accommodate different tone mapping operators, including those that are spatially non-uniform.
In the course of the research presented in this thesis a novel two layer CODEC architecture is introduced for both HDR image and video coding. Further a universal and computationally efficient approximation of the tone mapping operator is developed and presented. It is shown that the use of perceptually uniform colourspaces for internal representation of pixel data enables improved compression efficiency of the algorithms. Further proposed novel approaches to the compression of metadata for the tone mapping operator is shown to improve compression performance for low bitrate video content. Multiple compression algorithms are designed, implemented and compared and quality-complexity trade-offs are identified. Finally practical aspects of implementing the developed algorithms are explored by automating the design space exploration flow and integrating the high level systems design framework with domain specific tools for synthesis and simulation of multiprocessor systems. The directions for further work are also presented
Põhjalik uuring ülisuure dünaamilise ulatusega piltide toonivastendamisest koos subjektiivsete testidega
A high dynamic range (HDR) image has a very wide range of luminance levels that
traditional low dynamic range (LDR) displays cannot visualize. For this reason, HDR
images are usually transformed to 8-bit representations, so that the alpha channel for
each pixel is used as an exponent value, sometimes referred to as exponential notation
[43]. Tone mapping operators (TMOs) are used to transform high dynamic range to
low dynamic range domain by compressing pixels so that traditional LDR display can
visualize them. The purpose of this thesis is to identify and analyse differences and
similarities between the wide range of tone mapping operators that are available in the
literature. Each TMO has been analyzed using subjective studies considering different
conditions, which include environment, luminance, and colour. Also, several inverse
tone mapping operators, HDR mappings with exposure fusion, histogram adjustment,
and retinex have been analysed in this study. 19 different TMOs have been examined
using a variety of HDR images. Mean opinion score (MOS) is calculated on those selected
TMOs by asking the opinion of 25 independent people considering candidates’
age, vision, and colour blindness
Optimal exposure compression for high dynamic range content
High dynamic range (HDR) imaging has become one of the foremost imaging methods capable of capturing and displaying the full range of lighting perceived by the human visual system in the real world. A number of HDR compression methods for both images and video have been developed to handle HDR data, but none of them has yet been adopted as the method of choice. In particular, the backwards-compatible methods that always maintain a stream/image that allow part of the content to be viewed on conventional displays make use of tone mapping operators which were developed to view HDR images on traditional displays. There are a large number of tone mappers, none of which is considered the best as the images produced could be deemed subjective. This work presents an alternative to tone mapping-based HDR content compression by identifying a single exposure that can reproduce the most information from the original HDR image. This single exposure can be adapted to fit within the bit depth of any traditional encoder. Any additional information that may be lost is stored as a residual. Results demonstrate quality is maintained as well, and better, than other traditional methods. Furthermore, the presented method is backwards-compatible, straightforward to implement, fast and does not require choosing tone mappers or settings
Stereoscopic high dynamic range imaging
Two modern technologies show promise to dramatically increase immersion in
virtual environments. Stereoscopic imaging captures two images representing
the views of both eyes and allows for better depth perception. High dynamic
range (HDR) imaging accurately represents real world lighting as opposed to
traditional low dynamic range (LDR) imaging. HDR provides a better contrast
and more natural looking scenes. The combination of the two technologies in
order to gain advantages of both has been, until now, mostly unexplored due to
the current limitations in the imaging pipeline. This thesis reviews both fields,
proposes stereoscopic high dynamic range (SHDR) imaging pipeline outlining the
challenges that need to be resolved to enable SHDR and focuses on capture and
compression aspects of that pipeline.
The problems of capturing SHDR images that would potentially require two
HDR cameras and introduce ghosting, are mitigated by capturing an HDR and
LDR pair and using it to generate SHDR images. A detailed user study compared
four different methods of generating SHDR images. Results demonstrated that
one of the methods may produce images perceptually indistinguishable from the
ground truth.
Insights obtained while developing static image operators guided the design
of SHDR video techniques. Three methods for generating SHDR video from an
HDR-LDR video pair are proposed and compared to the ground truth SHDR
videos. Results showed little overall error and identified a method with the least
error.
Once captured, SHDR content needs to be efficiently compressed. Five SHDR
compression methods that are backward compatible are presented. The proposed
methods can encode SHDR content to little more than that of a traditional single
LDR image (18% larger for one method) and the backward compatibility property
encourages early adoption of the format.
The work presented in this thesis has introduced and advanced capture and
compression methods for the adoption of SHDR imaging. In general, this research
paves the way for a novel field of SHDR imaging which should lead to improved
and more realistic representation of captured scenes
An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras
Event cameras are ideally suited to capture High Dynamic Range (HDR) visual
information without blur but provide poor imaging capability for static or
slowly varying scenes. Conversely, conventional image sensors measure absolute
intensity of slowly changing scenes effectively but do poorly on HDR or quickly
changing scenes. In this paper, we present an asynchronous linear filter
architecture, fusing event and frame camera data, for HDR video reconstruction
and spatial convolution that exploits the advantages of both sensor modalities.
The key idea is the introduction of a state that directly encodes the
integrated or convolved image information and that is updated asynchronously as
each event or each frame arrives from the camera. The state can be read-off
as-often-as and whenever required to feed into subsequent vision modules for
real-time robotic systems. Our experimental results are evaluated on both
publicly available datasets with challenging lighting conditions and fast
motions, along with a new dataset with HDR reference that we provide. The
proposed AKF pipeline outperforms other state-of-the-art methods in both
absolute intensity error (69.4% reduction) and image similarity indexes
(average 35.5% improvement). We also demonstrate the integration of image
convolution with linear spatial kernels Gaussian, Sobel, and Laplacian as an
application of our architecture.Comment: 17 pages, 10 figures, Accepted by IEEE Transactions on Pattern
Analysis and Machine Intelligence (TPAMI) in August 202
Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction
Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF).
This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA.
The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be
optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed.
The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions
Non-parametric Methods for Automatic Exposure Control, Radiometric Calibration and Dynamic Range Compression
Imaging systems are essential to a wide range of modern day
applications. With the continuous advancement in imaging systems,
there is an on-going need to adapt and improve the imaging
pipeline running inside the imaging systems.
In this thesis, methods are presented to improve the imaging
pipeline of digital cameras. Here we present three methods to
improve important phases of the imaging process, which are (i)
``Automatic exposure adjustment'' (ii) ``Radiometric
calibration'' (iii) ''High dynamic range compression''. These
contributions touch the initial, intermediate and final stages of
imaging pipeline of digital cameras.
For exposure control, we propose two methods. The first makes use
of CCD-based equations to formulate the exposure control problem.
To estimate the exposure time, an initial image was acquired for
each wavelength channel to which contrast adjustment techniques
were applied. This helps to recover a reference cumulative
distribution function of image brightness at each channel. The
second method proposed for automatic exposure control is an
iterative method applicable for a broad range of imaging systems.
It uses spectral sensitivity functions such as the photopic
response functions for the generation of a spectral power image
of the captured scene. A target image is then generated using the
spectral power image by applying histogram equalization. The
exposure time is hence calculated iteratively by minimizing the
squared difference between target and the current spectral power
image. Here we further analyze the method by performing its
stability and controllability analysis using a state space
representation used in control theory. The applicability of the
proposed method for exposure time calculation was shown on real
world scenes using cameras with varying architectures.
Radiometric calibration is the estimate of the non-linear mapping
of the input radiance map to the output brightness values. The
radiometric mapping is represented by the camera response
function with which the radiance map of the scene is estimated.
Our radiometric calibration method employs an L1 cost function by
taking advantage of Weisfeld optimization scheme. The proposed
calibration works with multiple input images of the scene with
varying exposure. It can also perform calibration using a single
input with few constraints. The proposed method outperforms,
quantitatively and qualitatively, various alternative methods
found in the literature of radiometric calibration.
Finally, to realistically represent the estimated radiance maps
on low dynamic range display (LDR) devices, we propose a method
for dynamic range compression. Radiance maps generally have
higher dynamic range (HDR) as compared to the widely used display
devices. Thus, for display purposes, dynamic range compression is
required on HDR images. Our proposed method generates few LDR
images from the HDR radiance map by clipping its values at
different exposures. Using contrast information of each LDR
image generated, the method uses an energy minimization approach
to estimate the probability map of each LDR image. These
probability maps are then used as label set to form final
compressed dynamic range image for the display device. The
results of our method were compared qualitatively and
quantitatively with those produced by widely cited and
professionally used methods
Inverse tone mapping
The introduction of High Dynamic Range Imaging in computer graphics has produced a novelty
in Imaging that can be compared to the introduction of colour photography or even more.
Light can now be captured, stored, processed, and finally visualised without losing information.
Moreover, new applications that can exploit physical values of the light have been introduced
such as re-lighting of synthetic/real objects, or enhanced visualisation of scenes. However,
these new processing and visualisation techniques cannot be applied to movies and pictures
that have been produced by photography and cinematography in more than one hundred years.
This thesis introduces a general framework for expanding legacy content into High Dynamic
Range content. The expansion is achieved avoiding artefacts, producing images suitable for
visualisation and re-lighting of synthetic/real objects. Moreover, it is presented a methodology
based on psychophysical experiments and computational metrics to measure performances of
expansion algorithms. Finally, a compression scheme, inspired by the framework, for High
Dynamic Range Textures, is proposed and evaluated
Contrast measures for predicting perceptual effects of daylight in architectural renderings
Daylit architecture is perceived as a dynamic luminous composition, yet most existing performance metrics were designed to evaluate natural illumination for its ability to adequately illuminate a two-dimensional task surface and avoid glare-based discomfort. It may be argued that task-driven approaches based on surface illumination and glare ignore the likelihood that contrast can provide positive impacts on our visual perception of space. Advances in these metrics to accommodate climate-based sky conditions and occupant behavior have improved our ability to evaluate task illumination and glare, yet the same attention has not been paid to evaluating positive perceptual responses to daylight. Existing studies have attempted to link subjective ratings of composition to simple global contrast metrics without reaching consensus. More advanced metrics have been developed in computational graphics and vision fields, but have not been applied to studies in qualitative lighting research. This paper introduces the results from an online experiment where subject ratings of daylight composition are compared to quantitative contrast measures across a series of renderings. This paper will identify which measures correlate to subjects’ ratings of visual interest, and introduces a modified contrast algorithm, which can be used as a novel prediction model for visual interest in daylit renderings
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