35,361 research outputs found
Exact Histogram Specification Optimized for Structural Similarity
An exact histogram specification (EHS) method modifies its input image to
have a specified histogram. Applications of EHS include image (contrast)
enhancement (e.g., by histogram equalization) and histogram watermarking.
Performing EHS on an image, however, reduces its visual quality. Starting from
the output of a generic EHS method, we maximize the structural similarity index
(SSIM) between the original image (before EHS) and the result of EHS
iteratively. Essential in this process is the computationally simple and
accurate formula we derive for SSIM gradient. As it is based on gradient
ascent, the proposed EHS always converges. Experimental results confirm that
while obtaining the histogram exactly as specified, the proposed method
invariably outperforms the existing methods in terms of visual quality of the
result. The computational complexity of the proposed method is shown to be of
the same order as that of the existing methods.
Index terms: histogram modification, histogram equalization, optimization for
perceptual visual quality, structural similarity gradient ascent, histogram
watermarking, contrast enhancement
A Convex Model for Edge-Histogram Specification with Applications to Edge-preserving Smoothing
The goal of edge-histogram specification is to find an image whose edge image
has a histogram that matches a given edge-histogram as much as possible.
Mignotte has proposed a non-convex model for the problem [M. Mignotte. An
energy-based model for the image edge-histogram specification problem. IEEE
Transactions on Image Processing, 21(1):379--386, 2012]. In his work, edge
magnitudes of an input image are first modified by histogram specification to
match the given edge-histogram. Then, a non-convex model is minimized to find
an output image whose edge-histogram matches the modified edge-histogram. The
non-convexity of the model hinders the computations and the inclusion of useful
constraints such as the dynamic range constraint. In this paper, instead of
considering edge magnitudes, we directly consider the image gradients and
propose a convex model based on them. Furthermore, we include additional
constraints in our model based on different applications. The convexity of our
model allows us to compute the output image efficiently using either
Alternating Direction Method of Multipliers or Fast Iterative
Shrinkage-Thresholding Algorithm. We consider several applications in
edge-preserving smoothing including image abstraction, edge extraction, details
exaggeration, and documents scan-through removal. Numerical results are given
to illustrate that our method successfully produces decent results efficiently
Self-consistent method for density estimation
The estimation of a density profile from experimental data points is a
challenging problem, usually tackled by plotting a histogram. Prior assumptions
on the nature of the density, from its smoothness to the specification of its
form, allow the design of more accurate estimation procedures, such as Maximum
Likelihood. Our aim is to construct a procedure that makes no explicit
assumptions, but still providing an accurate estimate of the density. We
introduce the self-consistent estimate: the power spectrum of a candidate
density is given, and an estimation procedure is constructed on the assumption,
to be released \emph{a posteriori}, that the candidate is correct. The
self-consistent estimate is defined as a prior candidate density that precisely
reproduces itself. Our main result is to derive the exact expression of the
self-consistent estimate for any given dataset, and to study its properties.
Applications of the method require neither priors on the form of the density
nor the subjective choice of parameters. A cutoff frequency, akin to a bin size
or a kernel bandwidth, emerges naturally from the derivation. We apply the
self-consistent estimate to artificial data generated from various
distributions and show that it reaches the theoretical limit for the scaling of
the square error with the dataset size.Comment: 21 pages, 5 figure
Downward Nominal Wage Rigidity in Europe: An Analysis of European Micro Data from the ECHP 1994-2001
This paper substantially extends the limited available evidence on existence and extent of downward nominal wage rigidity in the European Union and the Euro Area. For this purpose we develop an econometric multi-country model based on Kahn�s (1997) histogram-location approach and apply it to employee micro data from the European Community Household Panel (ECHP) for twelve of the EU�s current member states. Our estimates for the degree of downward nominal wage rigidity on the national as well as the EU-wide level point to marked downward nominal wage rigidity within the European Union. Dieser Beitrag liefert eine erhebliche Erweiterung der bislang nur spärlich vorhandenen Evidenz zum Ausmaß der Abwärtsnominallohnstarrheit in der Europäischen Union und im Eurogebiet. Zu diesem Zweck wird auf Basis des Histogram-Location-Approach von Kahn (1997) ein ökonometrisches Mehrländermodell entwickelt und auf die Daten des Europäischen Haushaltspanels für zwölf EU-Mitgliedsländer angewandt. Die Schätzergebnisse deuten sowohl auf nationaler als auch auf europäischer Ebene auf ausgeprägte Abwärtsnominallohnstarrheit hin.Lohnstarrheit ; Europäische Union ; Europäische Union / Währungsunion / Mitgliedsstaaten ; Lohnbildung ; Nominallohn ; Rigidität; Abwärtsnominallohnstarrheit ; Europäisches Haushaltspanel ; ECHP ; Lohnrigidität ; Histogram-Location-Ansatz; Downward Nominal Wage Rigidity ; Wage Stickiness ; European Comunity Household Panel ; European Union ; Euro Area
Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies
Privacy definitions provide ways for trading-off the privacy of individuals
in a statistical database for the utility of downstream analysis of the data.
In this paper, we present Blowfish, a class of privacy definitions inspired by
the Pufferfish framework, that provides a rich interface for this trade-off. In
particular, we allow data publishers to extend differential privacy using a
policy, which specifies (a) secrets, or information that must be kept secret,
and (b) constraints that may be known about the data. While the secret
specification allows increased utility by lessening protection for certain
individual properties, the constraint specification provides added protection
against an adversary who knows correlations in the data (arising from
constraints). We formalize policies and present novel algorithms that can
handle general specifications of sensitive information and certain count
constraints. We show that there are reasonable policies under which our privacy
mechanisms for k-means clustering, histograms and range queries introduce
significantly lesser noise than their differentially private counterparts. We
quantify the privacy-utility trade-offs for various policies analytically and
empirically on real datasets.Comment: Full version of the paper at SIGMOD'14 Snowbird, Utah US
Energy-Aware Cloud Management through Progressive SLA Specification
Novel energy-aware cloud management methods dynamically reallocate
computation across geographically distributed data centers to leverage regional
electricity price and temperature differences. As a result, a managed VM may
suffer occasional downtimes. Current cloud providers only offer high
availability VMs, without enough flexibility to apply such energy-aware
management. In this paper we show how to analyse past traces of dynamic cloud
management actions based on electricity prices and temperatures to estimate VM
availability and price values. We propose a novel SLA specification approach
for offering VMs with different availability and price values guaranteed over
multiple SLAs to enable flexible energy-aware cloud management. We determine
the optimal number of such SLAs as well as their availability and price
guaranteed values. We evaluate our approach in a user SLA selection simulation
using Wikipedia and Grid'5000 workloads. The results show higher customer
conversion and 39% average energy savings per VM.Comment: 14 pages, conferenc
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