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Сжатие видео - Metrics

Английские материалы
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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
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

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