2,070 research outputs found
An evaluation of power transfer functions for HDR video compression
High dynamic range (HDR) imaging enables the full range of light in a scene to be captured, transmitted and displayed. However, uncompressed 32-bit HDR is four times larger than traditional low dynamic range (LDR) imagery. If HDR is to fulfil its potential for use in live broadcasts and interactive remote gaming, fast, efficient compression is necessary for HDR video to be manageable on existing communications infrastructure. A number of methods have been put forward for HDR video compression. However, these can be relatively complex and frequently require the use of multiple video streams. In this paper, we propose the use of a straightforward Power Transfer Function (PTF) as a practical, computationally fast, HDR video compression solution. The use of PTF is presented and evaluated against four other HDR video compression methods. An objective evaluation shows that PTF exhibits improved quality at a range of bit-rates and, due to its straightforward nature, is highly suited for real-time HDR video applications
Trained Perceptual Transform for Quality Assessment of High Dynamic Range Images and Video
In this paper, we propose a trained perceptually transform for
quality assessment of high dynamic range (HDR) images and
video. The transform is used to convert absolute luminance
values found in HDR images into perceptually uniform units,
which can be used with any standard-dynamic-range metric.
The new transform is derived by fitting the parameters
of a previously proposed perceptual encoding function to 4
different HDR subjective quality assessment datasets using
Bayesian optimization. The new transform combined with a
simple peak signal-to-noise ratio measure achieves better prediction
performance in cross-dataset validation than existing
transforms. We provide Matlab code for our metri
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Active sampling, scaling and dataset merging for large-scale image quality assessment
The field of subjective assessment is concerned with eliciting human judgements about a set of stimuli. Collecting such data is costly and time-consuming, especially when the subjective study is to be conducted in a controlled environment and using a specialized equipment. Thus, data from these studies are usually scarce. One of the areas, for which obtaining subjective measurements is difficult is image quality assessment. The results from these studies are used to develop and train automated or objective image quality metrics, which, with the advent of deep learning, require large amounts of versatile and heterogeneous data.
I present three main contributions in this dissertation. First, I propose a new active sampling method for efficient collection of pairwise comparisons in subjective assessment experiments. In these experiments observers are asked to express a preference between two conditions. However, many pairwise comparison protocols require a large number of comparisons to infer accurate scores, which may be unfeasible when each comparison is time-consuming (e.g. videos) or expensive (e.g. medical imaging). This motivates the use of an active sampling algorithm that chooses only the most informative pairs for comparison. I demonstrate, with real and synthetic data, that my algorithm offers the highest accuracy of inferred scores given a fixed number of measurements compared to the existing methods. Second, I propose a probabilistic framework to fuse the outcomes of different psychophysical experimental protocols, namely rating and pairwise comparisons experiments. Such a method can be used for merging existing datasets of subjective nature and for experiments in which both measurements are collected. Third, with a new dataset merging technique and by collecting additional cross-dataset quality comparisons I create a Unified Photometric Image Quality (UPIQ) dataset with over 4,000 images by realigning and merging existing high-dynamic-range (HDR) and standard-dynamic-range (SDR) datasets. The realigned quality scores share the same unified quality scale across all datasets. I then use the new dataset to retrain existing HDR metrics and show that the dataset is sufficiently large for training deep architectures. I show the utility of the dataset and metrics in an application to image compression that accounts for viewing conditions, including screen brightness and the viewing distance
Towards a quality metric for dense light fields.
Light fields become a popular representation of three-dimensional scenes, and there is interest in their processing, resampling, and compression. As those operations often result in loss of quality, there is a need to quantify it. In this work, we collect a new dataset of dense reference and distorted light fields as well as the corresponding quality scores which are scaled in perceptual units. The scores were acquired in a subjective experiment using an interactive light-field viewing setup. The dataset contains typical artifacts that occur in light-field processing chain due to light-field reconstruction, multi-view compression, and limitations of automultiscopic displays. We test a number of existing objective quality metrics to determine how well they can predict the quality of light fields. We find that the existing image quality metrics provide good measures of light-field quality, but require dense reference light-fields for optimal performance. For more complex tasks of comparing two distorted light fields, their performance drops significantly, which reveals the need for new, light-field-specific metrics
A Perceptually Optimized and Self-Calibrated Tone Mapping Operator
With the increasing popularity and accessibility of high dynamic range (HDR)
photography, tone mapping operators (TMOs) for dynamic range compression are
practically demanding. In this paper, we develop a two-stage neural
network-based TMO that is self-calibrated and perceptually optimized. In Stage
one, motivated by the physiology of the early stages of the human visual
system, we first decompose an HDR image into a normalized Laplacian pyramid. We
then use two lightweight deep neural networks (DNNs), taking the normalized
representation as input and estimating the Laplacian pyramid of the
corresponding LDR image. We optimize the tone mapping network by minimizing the
normalized Laplacian pyramid distance (NLPD), a perceptual metric aligning with
human judgments of tone-mapped image quality. In Stage two, the input HDR image
is self-calibrated to compute the final LDR image. We feed the same HDR image
but rescaled with different maximum luminances to the learned tone mapping
network, and generate a pseudo-multi-exposure image stack with different detail
visibility and color saturation. We then train another lightweight DNN to fuse
the LDR image stack into a desired LDR image by maximizing a variant of the
structural similarity index for multi-exposure image fusion (MEF-SSIM), which
has been proven perceptually relevant to fused image quality. The proposed
self-calibration mechanism through MEF enables our TMO to accept uncalibrated
HDR images, while being physiology-driven. Extensive experiments show that our
method produces images with consistently better visual quality. Additionally,
since our method builds upon three lightweight DNNs, it is among the fastest
local TMOs.Comment: 20 pages,18 figure
Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels
Forensic analysis of digital photo provenance relies on intrinsic traces left
in the photograph at the time of its acquisition. Such analysis becomes
unreliable after heavy post-processing, such as down-sampling and
re-compression applied upon distribution in the Web. This paper explores
end-to-end optimization of the entire image acquisition and distribution
workflow to facilitate reliable forensic analysis at the end of the
distribution channel. We demonstrate that neural imaging pipelines can be
trained to replace the internals of digital cameras, and jointly optimized for
high-fidelity photo development and reliable provenance analysis. In our
experiments, the proposed approach increased image manipulation detection
accuracy from 45% to over 90%. The findings encourage further research towards
building more reliable imaging pipelines with explicit provenance-guaranteeing
properties.Comment: Camera ready + supplement, CVPR'1
The competitiveness of nations and implications for human development
This is the post-print version of the final paper published in Socio-Economic Planning Sciences. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.Human development should be the ultimate objective of human activity, its aim being healthier, longer, and fuller lives. Thus, if the competitiveness of a nation is properly managed, enhanced human welfare should be the key expected consequence. The research described here explores the relationship between the competitiveness of a nation and its implications for human development. For this purpose, 45 countries were evaluated initially using data envelopment analysis. In this stage, global competitiveness indicators were taken as input variables with human development index indicators as output variables. Subsequently, an artificial neural network analysis was conducted to identify those factors having the greatest impact on efficiency scores
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