Английские материалы
|
| Авторы |
Название статьи |
Описание |
Рейтинг |
| Ismail Avc?bas1, Bulent Sankur2, Khalid Sayood3 |
Statistical Evaluation of Image Quality Measures |
In this work we categorize comprehensively image quality measures, extend
measures defined for gray scale images to their multispectral case, and propose
novel image quality measures. They are categorized into pixel difference-based,
correlation-based, edge-based, spectral-based, context based and HVS-based
(Human Visual System-based) measures. Furthermore we compare these
measures statistically for still image compression applications. The statistical
behavior of the measures and their sensitivity to coding artifacts are investigated
via Analysis of Variance techniques. Their similarities or differences have been
illustrated by plotting their Kohonen maps. Measures that give consistent scores
across an image class and that are sensitive to coding artifacts are pointed out. It
has been found that measures based on phase spectrum, on multiresolution
distance or HVS filtered mean square error are computationally simple and are
more responsive to coding artifacts.
RAR 300 кбайт
|
?
|
| Mark R. Bolin and Gary W. Meyer |
A Visual Di?erence Metric for Realistic Image Synthesis |
An accurate and e±cient model of human perception has been developed to control the placement of samples in a
realistic image synthesis algorithm. Previous sampling techniques have sought to spread the error equally across the
image plane. However, this approach neglects the fact that the renderings are intended to be displayed for a human
observer. The human visual system has a varying sensitivity to error that is based upon the viewing context. This
means that equivalent optical discrepancies can be very obvious in one situation and imperceptible in another. It is
ultimately the perceptibility of this error that governs image quality and should be used as the basis of a sampling
algorithm.
This paper focuses on a simpliOed version of the Lubin Visual Discrimination Metric (VDM) that was developed
for insertion into an image synthesis algorithm. The simpliOed VDM makes use of a Haar wavelet basis for the
cortical transform and a less severe spatial pooling operation. The model was extended for color including the
e?ects of chromatic aberration. Comparisons are made between the execution time and visual di?erence map for the
original Lubin and simpliOed visual di?erence metrics. Results from the realistic image synthesis algorithm are also
presented.
RAR 489 кбайт
|
?
|
| Michael Garland Paul S. Heckbert† |
Simplifying Surfaces with Color and Texture using Quadric Error Metrics |
There are a variety of application areas in which there is a need
for simplifying complex polygonal surface models. These models
often have material properties such as colors, textures, and surface
normals. Our surface simplification algorithm, based on iterative
edge contraction and quadric error metrics, can rapidly produce
high quality approximations of such models. We present a natural
extension of our original error metric that can account for a wide
RAR 1078 кбайт
|
?
|
| Aaron Hertzmann1,2 Charles E. Jacobs2 Nuria Oliver2 Brian Curless3 David H. Salesin2,3 |
Image Analogies |
This paper describes a new framework for processing images by
example, called “image analogies.” The framework involves two
stages: a design phase, in which a pair of images, with one image
purported to be a “filtered” version of the other, is presented
as “training data”; and an application phase, in which the learned
filter is applied to some new target image in order to create an “analogous”
filtered result. Image analogies are based on a simple multiscale
autoregression, inspired primarily by recent results in texture
synthesis. By choosing different types of source image pairs as input,
the framework supports a wide variety of “image filter” effects,
including traditional image filters, such as blurring or embossing;
improved texture synthesis, in which some textures are synthesized
with higher quality than by previous approaches; super-resolution,
in which a higher-resolution image is inferred from a low-resolution
source; texture transfer, in which images are “texturized” with some
arbitrary source texture; artistic filters, in which various drawing
and painting styles are synthesized based on scanned real-world
examples; and texture-by-numbers, in which realistic scenes, composed
of a variety of textures, are created using a simple painting
interface.
RAR 1253 кбайт
|
?
|
| Charles E. Jacobs Adam Finkelstein David H. Salesin |
Fast Multiresolution Image Querying |
We present a method for searching in an image database using a
query image that is similar to the intended target. The query image
may be a hand-drawn sketch or a (potentially low-quality) scan
of the image to be retrieved. Our searching algorithm makes use of
multiresolution wavelet decompositions of the query and database
images. The coefficients of these decompositions are distilled into
small “signatures” for each image. We introduce an “image querying
metric” that operates on these signatures. This metric essentially
compares how many significant wavelet coefficients the query has in
common with potential targets. The metric includes parameters that
can be tuned, using a statistical analysis, to accommodate the kinds
of image distortions found in different types of image queries. The
resulting algorithm is simple, requires very little storage overhead
for the database of signatures, and is fast enough to be performed on
a database of 20,000 images at interactive rates (on standard desktop
machines) as a query is sketched. Our experiments with hundreds
of queries in databases of 1000 and 20,000 images show dramatic
improvement, in both speed and success rate, over using a conventional
L1, L2, or color histogram norm.
RAR 455 кбайт
|
?
|
| Sofia Tsekeridou Constantine Kotropoulos Ioannis Pitas |
MORPHOLOGICAL SIGNAL ADAPTIVE MEDIAN FILTER FOR STILL IMAGE AND IMAGE SEQUENCE FILTERING |
?
RAR 325 кбайт
|
?
|
| Mahesh Ramasubramanian Sumanta N. Pattanaik Donald P. Greenberg |
A Perceptually Based Physical Error Metric for Realistic Image Synthesis |
We introduce a new concept for accelerating realistic image synthesis
algorithms. At the core of this procedure is a novel physical error
metric that correctly predicts the perceptual threshold for detecting
artifacts in scene features. Built into this metric is a computational
model of the human visual system’s loss of sensitivity at high background
illumination levels, high spatial frequencies, and high contrast
levels (visual masking). An important feature of our model is
that it handles the luminance-dependent processing and spatiallydependent
processing independently. This allows us to precompute
the expensive spatially-dependent component, making our model
extremely efficient.
We illustrate the utility of our procedure with global illumination
algorithms used for realistic image synthesis. The expense
of global illumination computations is many orders of magnitude
higher than the expense of direct illumination computations and
can greatly benefit by applying our perceptually based technique.
Results show our method preserves visual quality while achieving
significant computational gains in areas of images with high frequency
texture patterns, geometric details, and lighting variations.
RAR 2671 кбайт
|
?
|
| H.Rushmeie and G.Ward and C.Piatkor |
Comparing Real and Synthetic Images |
?
RAR 237 кбайт
|
?
|
| ? |
Aliasing |
To illustrate the effects of aliasing, we employ a supersampling scheme which gradually decreases the
high frequency content in an image. In this experiment we start with the 2562 MRI image and then interpolate it to a
size of 10242. Then, we subsample to a size of 1282 image. Aliasing is visible at this sampling rate. Supersampling
can be carried to a factor of 8. With a higher supersampling factor the size of the neighborhood increases and the
pixel values are averaged over this neighborhood. As is evident (Figure 4) the ill effects of supersampling are diminished,
especially for the for the spectra at levels, k=1,2. The lower spectra are not effected at all as should be the case.
RAR 73 кбайт
|
?
|
| Patric C.Teo and David J.Heeger |
Perceptual image distortion |
?
RAR 521 кбайт
|
?
|
| Sergey I. Titov |
Perceptually Based Image Comparison Method |
In this work a new perceptually based method of image
comparison is proposed. It is based on the colour comparison in a
perceptually uniform colour space CIE Luv, and usin g Contrast
Sensitivity Function to modify colour comparison thresholds,
provided by CIE Luv space.
This method can be used to measure image distortion in case of
lossy image compression, and steering image generation.
RAR 340 кбайт
|
?
|
