753 research outputs found
Appearance-based image splitting for HDR display systems
High dynamic range displays that incorporate two optically-coupled image planes have recently been developed. This dual image plane design requires that a given HDR input image be split into two complementary standard dynamic range components that drive the coupled systems, therefore there existing image splitting issue. In this research, two types of HDR display systems (hardcopy and softcopy HDR display) are constructed to facilitate the study of HDR image splitting algorithm for building HDR displays. A new HDR image splitting algorithm which incorporates iCAM06 image appearance model is proposed, seeking to create displayed HDR images that can provide better image quality. The new algorithm has potential to improve image details perception, colorfulness and better gamut utilization. Finally, the performance of the new iCAM06-based HDR image splitting algorithm is evaluated and compared with widely spread luminance square root algorithm through psychophysical studies
High dynamic range display systems
High contrast ratio (CR) enables a display system to faithfully reproduce the real objects. However, achieving high contrast, especially high ambient contrast (ACR), is a challenging task. In this dissertation, two display systems with high CR are discussed: high ACR augmented reality (AR) display and high dynamic range (HDR) display. For an AR display, we improved its ACR by incorporating a tunable transmittance liquid crystal (LC) film. The film has high tunable transmittance range, fast response time, and is fail-safe. To reduce the weight and size of a display system, we proposed a functional reflective polarizer, which can also help people with color vision deficiency. As for the HDR display, we improved all three aspects of the hardware requirements: contrast ratio, color gamut and bit-depth. By stacking two liquid crystal display (LCD) panels together, we have achieved CR over one million to one, 14-bit depth with 5V operation voltage, and pixel-by-pixel local dimming. To widen color gamut, both photoluminescent and electroluminescent quantum dots (QDs) have been investigated. Our analysis shows that with QD approach, it is possible to achieve over 90% of the Rec. 2020 color gamut for a HDR display. Another goal of an HDR display is to achieve the 12-bit perceptual quantizer (PQ) curve covering from 0 to 10,000 nits. Our experimental results indicate that this is difficult with a single LCD panel because of the sluggish response time. To overcome this challenge, we proposed a method to drive the light emitting diode (LED) backlight and the LCD panel simultaneously. Besides relatively fast response time, this approach can also mitigate the imaging noise. Finally yet importantly, we improved the display pipeline by using a HDR gamut mapping approach to display HDR contents adaptively based on display specifications. A psychophysical experiment was conducted to determine the display requirements
Event-Based Motion Segmentation by Motion Compensation
In contrast to traditional cameras, whose pixels have a common exposure time,
event-based cameras are novel bio-inspired sensors whose pixels work
independently and asynchronously output intensity changes (called "events"),
with microsecond resolution. Since events are caused by the apparent motion of
objects, event-based cameras sample visual information based on the scene
dynamics and are, therefore, a more natural fit than traditional cameras to
acquire motion, especially at high speeds, where traditional cameras suffer
from motion blur. However, distinguishing between events caused by different
moving objects and by the camera's ego-motion is a challenging task. We present
the first per-event segmentation method for splitting a scene into
independently moving objects. Our method jointly estimates the event-object
associations (i.e., segmentation) and the motion parameters of the objects (or
the background) by maximization of an objective function, which builds upon
recent results on event-based motion-compensation. We provide a thorough
evaluation of our method on a public dataset, outperforming the
state-of-the-art by as much as 10%. We also show the first quantitative
evaluation of a segmentation algorithm for event cameras, yielding around 90%
accuracy at 4 pixels relative displacement.Comment: When viewed in Acrobat Reader, several of the figures animate. Video:
https://youtu.be/0q6ap_OSBA
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Perceptual image quality assessment for various viewing conditions and display systems
From complete darkness to direct sunlight, real-world dis-
plays operate in various viewing conditions often resulting in a
non-optimal viewing experience. Most existing Image Quality
Assessment (IQA) methods, however, assume ideal environments
and displays, and thus cannot be used when viewing conditions
differ from the standard. In this paper, we investigate the influence
of ambient illumination level and display luminance on human
perception of image quality. We conduct a psychophysical study
to collect a novel dataset of over 10000 image quality preference
judgments performed in illumination conditions ranging from 0 lux
to 20000 lux. We also propose a perceptual IQA framework that
allows most existing image quality metrics (IQM) to accurately
predict image quality for a wide range of illumination conditions
and display parameters 1 . Our analysis demonstrates strong cor-
relation between human IQA and the predictions of our proposed
framework combined with multiple prominent IQMs and across a
wide range of luminance values
High dynamic range video merging, tone mapping, and real-time implementation
Although High Dynamic Range (High Dynamic Range (HDR)) imaging has been the subject of significant research over the past fifteen years, the goal of cinemaquality HDR video has not yet been achieved. This work references an optical method patented by Contrast Optical which is used to capture sequences of Low Dynamic Range (LDR) images that can be used to form HDR images as the basis for HDR video. Because of the large diverence in exposure spacing of the LDR images captured by this camera, present methods of merging LDR images are insufficient to produce cinema quality HDR images and video without significant visible artifacts. Thus the focus of the research presented is two fold. The first contribution is a new method of combining LDR images with exposure differences of greater than 3 stops into an HDR image. The second contribution is a method of tone mapping HDR video which solves potential problems of HDR video flicker and automated parameter control of the tone mapping operator. A prototype of this HDR video capture technique along with the combining and tone mapping algorithms have been implemented in a high-definition HDR-video system. Additionally, Field Programmable Gate Array (FPGA) hardware implementation details are given to support real time HDR video. Still frames from the acquired HDR video system which have been merged used the merging and tone mapping techniques will be presented
Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping
Many image enhancement or editing operations, such as forward and inverse
tone mapping or color grading, do not have a unique solution, but instead a
range of solutions, each representing a different style. Despite this, existing
learning-based methods attempt to learn a unique mapping, disregarding this
style. In this work, we show that information about the style can be distilled
from collections of image pairs and encoded into a 2- or 3-dimensional vector.
This gives us not only an efficient representation but also an interpretable
latent space for editing the image style. We represent the global color mapping
between a pair of images as a custom normalizing flow, conditioned on a
polynomial basis of the pixel color. We show that such a network is more
effective than PCA or VAE at encoding image style in low-dimensional space and
lets us obtain an accuracy close to 40 dB, which is about 7-10 dB improvement
over the state-of-the-art methods.Comment: Published in European Conference on Visual Media Production (CVMP
'22
07171 Abstracts Collection -- Visual Computing -- Convergence of Computer Graphics and Computer Vision
From 22.04. to 27.04.2007, the Dagstuhl Seminar 07171 ``Visual Computing - Convergence of Computer Graphics and Computer Vision\u27\u27 was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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