123 research outputs found
AN INTEGER TONE MAPPING OPERATION FOR HDR IMAGES IN OPENEXR WITH DENORMALIZED NUMBERS
ABSTRACT We propose an integer tone mapping operator (TMO) for high dynamic range (HDR) images expressed in a floating-point data format. Two purposes are achieved by the proposed TMO. The first purpose is to implement a TMO with less memory space. The second purpose is to give an important step to realize a fixed-point TMO. The proposed TMO is available for HDR images in the OpenEXR format. The OpenEXR format has two numerical representations (the normalized number and the denormalized number) which are not in other HDR formats such as RGBE. These two numerical representations cause a problem in applying an integer TMO. The proposed method enables us to avoid the problem by using the intermediate format. Moreover, the exponent part and the mantissa part are processed separately as two integer numbers. As a result, an integer TMO with less numerical range is achieved by our method. The experimental results show that the proposed method can generate high-quality low dynamic range (LDR) images with less memory space. Index Terms-high dynamic range, tone mapping, OpenEXR, denormalized number, integer operatio
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
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
A simplified HDR image processing pipeline for digital photography
High Dynamic Range (HDR) imaging has revolutionized the digital imaging. It allows
capture, storage, manipulation, and display of full dynamic range of the captured scene.
As a result, it has spawned whole new possibilities for digital photography, from photorealistic
to hyper-real. With all these advantages, the technique is expected to replace
the conventional 8-bit Low Dynamic Range (LDR) imaging in the future. However,
HDR results in an even more complex imaging pipeline including new techniques for
capturing, encoding, and displaying images. The goal of this thesis is to bridge the
gap between conventional imaging pipeline to the HDR’s in as simple a way as possible.
We make three contributions. First we show that a simple extension of gamma
encoding suffices as a representation to store HDR images. Second, gamma as a control
for image contrast can be ‘optimally’ tuned on a per image basis. Lastly, we show
a general tone curve, with detail preservation, suffices to tone map an image (there is
only a limited need for the expensive spatially varying tone mappers). All three of our
contributions are evaluated psychophysically. Together they support our general thesis
that an HDR workflow, similar to that already used in photography, might be used. This
said, we believe the adoption of HDR into photography is, perhaps, less difficult than it
is sometimes posed to be
JPEG XT: A Compression Standard for HDR and WCG Images [Standards in a Nutshell]
High bit depth data acquisition and manipulation have been largely studied at the academic level over the last 15 years and are rapidly attracting interest at the industrial level. An example of the increasing interest for high-dynamic range (HDR) imaging is the use of 32-bit floating point data for video and image acquisition and manipulation that allows a variety of visual effects that closely mimic the real-world visual experience of the end user [1] (see Figure 1). At the industrial level, we are witnessing increasing traction toward supporting HDR and wide color gamut (WCG). WCG leverages HDR for each color channel to display a wider range of colors. Consumer cameras are currently available with a 14- or 16-bit analog-to-digital converter. Rendering devices are also appearing with the capability to display HDR images and video with a peak brightness of up to 4,000 nits and to support WCG (ITU-R Rec. BT.2020 [2]) rather than the historical ITU-R Rec. BT.709 [3]. This trend calls for a widely accepted standard for higher bit depth support that can be seamlessly integrated into existing products and applications. While standard formats such as the Joint Photographic Experts Group (JPEG) 2000 [5] and JPEG XR [6] offer support for high bit depth image representations, their adoption requires a nonnegligible investment that may not always be affordable in existing imaging ecosystems, and induces a difficult transition, as they are not backward-compatible with the popular JPEG image format
Compression, Modeling, and Real-Time Rendering of Realistic Materials and Objects
The realism of a scene basically depends on the quality of the geometry, the
illumination and the materials that are used. Whereas many sources for
the creation of three-dimensional geometry exist and numerous algorithms
for the approximation of global illumination were presented, the acquisition
and rendering of realistic materials remains a challenging problem.
Realistic materials are very important in computer graphics, because
they describe the reflectance properties of surfaces, which are based on the
interaction of light and matter. In the real world, an enormous diversity of
materials can be found, comprising very different properties. One important
objective in computer graphics is to understand these processes, to formalize
them and to finally simulate them.
For this purpose various analytical models do already exist, but their
parameterization remains difficult as the number of parameters is usually
very high. Also, they fail for very complex materials that occur in the real
world. Measured materials, on the other hand, are prone to long acquisition
time and to huge input data size. Although very efficient statistical
compression algorithms were presented, most of them do not allow for editability,
such as altering the diffuse color or mesostructure. In this thesis,
a material representation is introduced that makes it possible to edit these
features. This makes it possible to re-use the acquisition results in order to
easily and quickly create deviations of the original material. These deviations
may be subtle, but also substantial, allowing for a wide spectrum of
material appearances.
The approach presented in this thesis is not based on compression, but on
a decomposition of the surface into several materials with different reflection
properties. Based on a microfacette model, the light-matter interaction is
represented by a function that can be stored in an ordinary two-dimensional
texture. Additionally, depth information, local rotations, and the diffuse
color are stored in these textures. As a result of the decomposition, some
of the original information is inevitably lost, therefore an algorithm for the
efficient simulation of subsurface scattering is presented as well.
Another contribution of this work is a novel perception-based simplification
metric that includes the material of an object. This metric comprises
features of the human visual system, for example trichromatic color
perception or reduced resolution. The proposed metric allows for a more
aggressive simplification in regions where geometric metrics do not simplif
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
Propuesta de arquitectura y circuitos para la mejora del rango dinámico de sistemas de visión en un chip diseñados en tecnologías CMOS profundamente submicrométrica
El trabajo presentado en esta tesis trata de proponer nuevas técnicas para la expansión
del rango dinámico en sensores electrónicos de imagen. En este caso, hemos dirigido nuestros
estudios hacia la posibilidad de proveer dicha funcionalidad en un solo chip. Esto es, sin
necesitar ningún soporte externo de hardware o software, formando un tipo de sistema
denominado Sistema de Visión en un Chip (VSoC). El rango dinámico de los sensores
electrónicos de imagen se define como el cociente entre la máxima y la mínima iluminación
medible. Para mejorar este factor surgen dos opciones. La primera, reducir la mínima luz
medible mediante la disminución del ruido en el sensor de imagen. La segunda, incrementar la
máxima luz medible mediante la extensión del límite de saturación del sensor.
Cronológicamente, nuestra primera opción para mejorar el rango dinámico se basó en
reducir el ruido. Varias opciones se pueden tomar para mejorar la figura de mérito de ruido del
sistema: reducir el ruido usando una tecnología CIS o usar circuitos dedicados, tales como
calibración o auto cero. Sin embargo, el uso de técnicas de circuitos implica limitaciones, las
cuales sólo pueden ser resueltas mediante el uso de tecnologías no estándar que están
especialmente diseñadas para este propósito. La tecnología CIS utilizada está dirigida a la
mejora de la calidad y las posibilidades del proceso de fotosensado, tales como sensibilidad,
ruido, permitir imagen a color, etcétera. Para estudiar las características de la tecnología en más
detalle, se diseñó un chip de test, lo cual permite extraer las mejores opciones para futuros
píxeles. No obstante, a pesar de un satisfactorio comportamiento general, las medidas referentes
al rango dinámico indicaron que la mejora de este mediante sólo tecnología CIS es muy
limitada. Es decir, la mejora de la corriente oscura del sensor no es suficiente para nuestro
propósito. Para una mayor mejora del rango dinámico se deben incluir circuitos dentro del píxel.
No obstante, las tecnologías CIS usualmente no permiten nada más que transistores NMOS al
lado del fotosensor, lo cual implica una seria restricción en el circuito a usar. Como resultado, el
diseño de un sensor de imagen con mejora del rango dinámico en tecnologías CIS fue
desestimado en favor del uso de una tecnología estándar, la cual da más flexibilidad al diseño
del píxel.
En tecnologías estándar, es posible introducir una alta funcionalidad usando circuitos
dentro del píxel, lo cual permite técnicas avanzadas para extender el límite de saturación de los
sensores de imagen. Para este objetivo surgen dos opciones: adquisición lineal o compresiva. Si
se realiza una adquisición lineal, se generarán una gran cantidad de datos por cada píxel. Como
ejemplo, si el rango dinámico de la escena es de 120dB al menos se necesitarían 20-bits/píxel,
log2(10120/20)=19.93, para la representación binaria de este rango dinámico. Esto necesitaría de
amplios recursos para procesar esta gran cantidad de datos, y un gran ancho de banda para
moverlos al circuito de procesamiento. Para evitar estos problemas, los sensores de imagen de
alto rango dinámico usualmente optan por utilizar una adquisición compresiva de la luz. Por lo
tanto, esto implica dos tareas a realizar: la captura y la compresión de la imagen. La captura de
la imagen se realiza a nivel de píxel, en el dispositivo fotosensor, mientras que la compresión de
la imagen puede ser realizada a nivel de píxel, de sistema, o mediante postprocesado externo.
Usando el postprocesado, existe un campo de investigación que estudia la compresión de
escenas de alto rango dinámico mientras se mantienen los detalles, produciendo un resultado
apropiado para la percepción humana en monitores convencionales de bajo rango dinámico.
Esto se denomina Mapeo de Tonos (Tone Mapping) y usualmente emplea solo 8-bits/píxel para
las representaciones de imágenes, ya que éste es el estándar para las imágenes de bajo rango
dinámico.
Los píxeles de adquisición compresiva, por su parte, realizan una compresión que no es
dependiente de la escena de alto rango dinámico a capturar, lo cual implica una baja compresión
o pérdida de detalles y contraste. Para evitar estas desventajas, en este trabajo, se presenta un
píxel de adquisición compresiva que aplica una técnica de mapeo de tonos que permite la
captura de imágenes ya comprimidas de una forma optimizada para mantener los detalles y el
contraste, produciendo una cantidad muy reducida de datos. Las técnicas de mapeo de tonos
ejecutan normalmente postprocesamiento mediante software en un ordenador sobre imágenes
capturadas sin compresión, las cuales contienen una gran cantidad de datos. Estas técnicas han
pertenecido tradicionalmente al campo de los gráficos por ordenador debido a la gran cantidad
de esfuerzo computacional que requieren. Sin embargo, hemos desarrollado un nuevo algoritmo
de mapeo de tonos especialmente adaptado para aprovechar los circuitos dentro del píxel y que
requiere un reducido esfuerzo de computación fuera de la matriz de píxeles, lo cual permite el
desarrollo de un sistema de visión en un solo chip. El nuevo algoritmo de mapeo de tonos, el
cual es un concepto matemático que puede ser simulado mediante software, se ha implementado
también en un chip. Sin embargo, para esta implementación hardware en un chip son necesarias
algunas adaptaciones y técnicas avanzadas de diseño, que constituyen en sí mismas otra de las
contribuciones de este trabajo. Más aún, debido a la nueva funcionalidad, se han desarrollado
modificaciones de los típicos métodos a usar para la caracterización y captura de imágenes
Towards a High Quality Real-Time Graphics Pipeline
Modern graphics hardware pipelines create photorealistic images with high geometric complexity in real time. The quality is constantly improving and advanced techniques from feature film visual effects, such as high dynamic range images and support for higher-order surface primitives, have recently been adopted. Visual effect techniques have large computational costs and significant memory bandwidth usage. In this thesis, we identify three problem areas and propose new algorithms that increase the performance of a set of computer graphics techniques. Our main focus is on efficient algorithms for the real-time graphics pipeline, but parts of our research are equally applicable to offline rendering. Our first focus is texture compression, which is a technique to reduce the memory bandwidth usage. The core idea is to store images in small compressed blocks which are sent over the memory bus and are decompressed on-the-fly when accessed. We present compression algorithms for two types of texture formats. High dynamic range images capture environment lighting with luminance differences over a wide intensity range. Normal maps store perturbation vectors for local surface normals, and give the illusion of high geometric surface detail. Our compression formats are tailored to these texture types and have compression ratios of 6:1, high visual fidelity, and low-cost decompression logic. Our second focus is tessellation culling. Culling is a commonly used technique in computer graphics for removing work that does not contribute to the final image, such as completely hidden geometry. By discarding rendering primitives from further processing, substantial arithmetic computations and memory bandwidth can be saved. Modern graphics processing units include flexible tessellation stages, where rendering primitives are subdivided for increased geometric detail. Images with highly detailed models can be synthesized, but the incurred cost is significant. We have devised a simple remapping technique that allowsfor better tessellation distribution in screen space. Furthermore, we present programmable tessellation culling, where bounding volumes for displaced geometry are computed and used to conservatively test if a primitive can be discarded before tessellation. We introduce a general tessellation culling framework, and an optimized algorithm for rendering of displaced Bézier patches, which is expected to be a common use case for graphics hardware tessellation. Our third and final focus is forward-looking, and relates to efficient algorithms for stochastic rasterization, a rendering technique where camera effects such as depth of field and motion blur can be faithfully simulated. We extend a graphics pipeline with stochastic rasterization in spatio-temporal space and show that stochastic motion blur can be rendered with rather modest pipeline modifications. Furthermore, backface culling algorithms for motion blur and depth of field rendering are presented, which are directly applicable to stochastic rasterization. Hopefully, our work in this field brings us closer to high quality real-time stochastic rendering
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