317 research outputs found
A study on user preference of high dynamic range over low dynamic range video
The increased interest in High Dynamic Range (HDR) video over existing Low Dynamic Range (LDR) video during the last decade or so was primarily due to its inherent capability to capture, store and display the full range of real-world lighting visible to the human eye with increased precision. This has led to an inherent assumption that HDR video would be preferable by the end-user over LDR video due to the more immersive and realistic visual experience provided by HDR. This assumption has led to a considerable body of research into efficient capture, processing, storage and display of HDR video. Although, this is beneficial for scientific research and industrial purposes, very little research has been conducted in order to test the veracity of this assumption. In this paper, we conduct two subjective studies by means of a ranking and a rating based experiment where 60 participants in total, 30 in each experiment, were tasked to rank and rate several reference HDR video scenes along with three mapped LDR versions of each scene on an HDR display, in order of their viewing preference. Results suggest that given the option, end-users prefer the HDR representation of the scene over its LDR counterpart
Hydra: An Accelerator for Real-Time Edge-Aware Permeability Filtering in 65nm CMOS
Many modern video processing pipelines rely on edge-aware (EA) filtering
methods. However, recent high-quality methods are challenging to run in
real-time on embedded hardware due to their computational load. To this end, we
propose an area-efficient and real-time capable hardware implementation of a
high quality EA method. In particular, we focus on the recently proposed
permeability filter (PF) that delivers promising quality and performance in the
domains of HDR tone mapping, disparity and optical flow estimation. We present
an efficient hardware accelerator that implements a tiled variant of the PF
with low on-chip memory requirements and a significantly reduced external
memory bandwidth (6.4x w.r.t. the non-tiled PF). The design has been taped out
in 65 nm CMOS technology, is able to filter 720p grayscale video at 24.8 Hz and
achieves a high compute density of 6.7 GFLOPS/mm2 (12x higher than embedded
GPUs when scaled to the same technology node). The low area and bandwidth
requirements make the accelerator highly suitable for integration into SoCs
where silicon area budget is constrained and external memory is typically a
heavily contended resource
Mixing tone mapping operators on the GPU by differential zone mapping based on psychophysical experiments
© 2016 In this paper, we present a new technique for displaying High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays in an efficient way on the GPU. The described process has three stages. First, the input image is segmented into luminance zones. Second, the tone mapping operator (TMO) that performs better in each zone is automatically selected. Finally, the resulting tone mapping (TM) outputs for each zone are merged, generating the final LDR output image. To establish the TMO that performs better in each luminance zone we conducted a preliminary psychophysical experiment using a set of HDR images and six different TMOs. We validated our composite technique on several (new) HDR images and conducted a further psychophysical experiment, using an HDR display as the reference that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO. Finally, we present a GPU version, which is perceptually equal to the standard version but with much improved computational performance
Cuboid-maps for indoor illumination modeling and augmented reality rendering
This thesis proposes a novel approach for indoor scene illumination modeling and augmented reality rendering. Our key observation is that an indoor scene is well represented by a set of rectangular spaces, where important illuminants reside on their boundary faces, such as a window on a wall or a ceiling light. Given a perspective image or a panorama and detected rectangular spaces as inputs, we estimate their cuboid shapes, and infer illumination components for each face of the cuboids by a simple convolutional neural architecture. The process turns an image into a set of cuboid environment maps, each of which is a simple extension of a traditional cube-map. For augmented reality rendering, we simply take a linear combination of inferred environment maps and an input image, producing surprisingly realistic illumination effects. This approach is simple and efficient, avoids flickering, and achieves quantitatively more accurate and qualitatively more realistic effects than competing substantially more complicated systems
HDRFusion:HDR SLAM using a low-cost auto-exposure RGB-D sensor
We describe a new method for comparing frame appearance in a frame-to-model
3-D mapping and tracking system using an low dynamic range (LDR) RGB-D camera
which is robust to brightness changes caused by auto exposure. It is based on a
normalised radiance measure which is invariant to exposure changes and not only
robustifies the tracking under changing lighting conditions, but also enables
the following exposure compensation perform accurately to allow online building
of high dynamic range (HDR) maps. The latter facilitates the frame-to-model
tracking to minimise drift as well as better capturing light variation within
the scene. Results from experiments with synthetic and real data demonstrate
that the method provides both improved tracking and maps with far greater
dynamic range of luminosity.Comment: 14 page
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
LAN-HDR: Luminance-based Alignment Network for High Dynamic Range Video Reconstruction
As demands for high-quality videos continue to rise, high-resolution and
high-dynamic range (HDR) imaging techniques are drawing attention. To generate
an HDR video from low dynamic range (LDR) images, one of the critical steps is
the motion compensation between LDR frames, for which most existing works
employed the optical flow algorithm. However, these methods suffer from flow
estimation errors when saturation or complicated motions exist. In this paper,
we propose an end-to-end HDR video composition framework, which aligns LDR
frames in the feature space and then merges aligned features into an HDR frame,
without relying on pixel-domain optical flow. Specifically, we propose a
luminance-based alignment network for HDR (LAN-HDR) consisting of an alignment
module and a hallucination module. The alignment module aligns a frame to the
adjacent reference by evaluating luminance-based attention, excluding color
information. The hallucination module generates sharp details, especially for
washed-out areas due to saturation. The aligned and hallucinated features are
then blended adaptively to complement each other. Finally, we merge the
features to generate a final HDR frame. In training, we adopt a temporal loss,
in addition to frame reconstruction losses, to enhance temporal consistency and
thus reduce flickering. Extensive experiments demonstrate that our method
performs better or comparable to state-of-the-art methods on several
benchmarks.Comment: ICCV 202
DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction
Due to hardware constraints, standard off-the-shelf digital cameras suffers
from low dynamic range (LDR) and low frame per second (FPS) outputs. Previous
works in high dynamic range (HDR) video reconstruction uses sequence of
alternating exposure LDR frames as input, and align the neighbouring frames
using optical flow based networks. However, these methods often result in
motion artifacts in challenging situations. This is because, the alternate
exposure frames have to be exposure matched in order to apply alignment using
optical flow. Hence, over-saturation and noise in the LDR frames results in
inaccurate alignment. To this end, we propose to align the input LDR frames
using a pre-trained video frame interpolation network. This results in better
alignment of LDR frames, since we circumvent the error-prone exposure matching
step, and directly generate intermediate missing frames from the same exposure
inputs. Furthermore, it allows us to generate high FPS HDR videos by
recursively interpolating the intermediate frames. Through this work, we
propose to use video frame interpolation for HDR video reconstruction, and
present the first method to generate high FPS HDR videos. Experimental results
demonstrate the efficacy of the proposed framework against optical flow based
alignment methods, with an absolute improvement of 2.4 PSNR value on standard
HDR video datasets [1], [2] and further benchmark our method for high FPS HDR
video generation.Comment: ICPR 202
A comparative review of tone-mapping algorithms for high dynamic range video
Tone-mapping constitutes a key component within the field of high dynamic range (HDR) imaging. Its importance is manifested in the vast amount of tone-mapping methods that can be found in the literature, which are the result of an active development in the area for more than two decades. Although these can accommodate most requirements for display of HDR images, new challenges arose with the advent of HDR video, calling for additional considerations in the design of tone-mapping operators (TMOs). Today, a range of TMOs exist that do support video material. We are now reaching a point where most camera captured HDR videos can be prepared in high quality without visible artifacts, for the constraints of a standard display device. In this report, we set out to summarize and categorize the research in tone-mapping as of today, distilling the most important trends and characteristics of the tone reproduction pipeline. While this gives a wide overview over the area, we then specifically focus on tone-mapping of HDR video and the problems this medium entails. First, we formulate the major challenges a video TMO needs to address. Then, we provide a description and categorization of each of the existing video TMOs. Finally, by constructing a set of quantitative measures, we evaluate the performance of a number of the operators, in order to give a hint on which can be expected to render the least amount of artifacts. This serves as a comprehensive reference, categorization and comparative assessment of the state-of-the-art in tone-mapping for HDR video.This project was funded by the Swedish Foundation for Strategic Research (SSF) through grant IIS11-0081, Linköping University Center for Industrial Information Technology (CENIIT), the Swedish Research Council through the Linnaeus Environment CADICS
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