The success of any transform coding technique depends on how well the basis functions represent the signal features. The discrete wavelet transform (DWT) performs a multiresolution analysis of a signal; this enables an efficient representation of smooth and detailed signal regions. Furthermore, computationally efficient algorithms exist for computing the DWT. For these reasons, recent image compression standards such as JPEG2000 use the wavelet transform. It is well known that orthogonality and symmetry are desirable transform properties in image compression applications. It is also known that the scalar wavelet transform does not possess both properties simultaneously. Multiwavelets overcome this limitation; the multiwavelet transform allows orthogonality and symmetry to co-exist. However recently reported image compression results indicate that the scalar wavelets still outperform the multiwavelets in terms of peak signal-to-noise ratio (PSNR). In a multiwavelet transform, the balancing order of the multiwavelet is indicative of its energy compaction efficiency (usually a higher balancing order implies lower mean-squared-error, MSE, in the compressed image). But a high balancing order alone does not ensure good image compression performance. Filter bank characteristics such as shift-variance, magnitude response, symmetry and phase response are important factors that also influence the MSE and perceived image quality. This thesis analyzes the impact of these multiwavelet characteristics on image compression performance. Our analysis allows us to explain---for the first time---reasons for the small performance gap between the scalar wavelets and multiwavelets. We study the characteristics of five balanced multiwavelets (and 2 unbalanced multiwavelets) and compare their image compression performance for grayscale images with the popular (9,7)-tap and (22,14)-tap biorthogonal scalar wavelets. We use the well-known SPIHT quantizer in our compression scheme and utilize PSNR and subjective quality measures to assess performance. We also study the effect of incorporating a human visual system (HVS)-based transform model in our multiwavelet compression scheme. Our results indicate those multiwavelet properties that are most important to image compression. Moreover, the PSNR and subjective quality results depict similar performance for the best scalar wavelets and multiwavelets. Our analysis also shows that the HVS-based multiwavelet transform coder considerably improves perceived image quality at low bit rates. / Master of Science

Yes / Binocularity in the human visual system poses two interesting and extremely challenging questions. The first, and perhaps most obvious stems from the singularity of perception even though the neural images we see originate as two separate images in the right and left eyes. Mechanistically we can ask how and where do we convert two images into one? The second question is more of a “why” question. By converting lateral eyes with their inherent panoramic visual field into frontal eyes with overlapping binocular visual fields, primates have developed an extremely large blind region (the half of the world behind us). We generally accept that this sacrifice in visual field size was driven by the potential benefit of extracting information about the 3rd dimension from overlapping right and left eye visual fields. For some people, both of these core processes of binocularity fail: a single fused binocular image is not achieved (when diplopia or suppression is present), and the ability to accurately represent the 3rd dimension is lost (stereo-blindness). In addition to these failures in the core functions of the human binocular system, early imbalances in the quality of right and left eye neural images (e.g. due to anisometropia, monocular deprivation, and/or strabismus), can precipitate profound neurological changes at a cortical level which can lead to serious vision loss in one eye (amblyopia). Caring for patients with malfunctioning binocular visual systems is a core therapeutic responsibility of the eye care professions (optometry, ophthalmology and orthoptics) and significant advances in patient care and subsequent visual outcomes will be gained from a deeper understanding of how the human brain accomplishes full binocular integration. This feature issue on binocular vision brings together original articles and reviews from leading groups of neuroscientists, psychophysicists and clinical scientists from around the world who embrace the multidisciplinary nature of this topic. Our authors have taken on the big issues facing the research community tasked with understanding how binocular vision is meant to work, how it fails, and how to better treat those with compromised binocularity. These studies address deep issues about how the human brain functions and how it fails, as well as how it can be altered by therapy.

The aim of this study is to work on the visual quality metrics that are widely accepted in literature, to evaluate them on different distortion types and to give a comparison of overall performances in terms of prediction accuracy, monotonicity, consistency and complexity. The algorithms behind the quality metrics in literature and parameters used for quality metric performance evaluations are studied. This thesis also includes the explanation of Human Visual System, classification of visual quality metrics and subjective quality assessment methods. Experimental results that show the correlation between objective scores and human perception are taken to compare the eight widely accepted visual quality metrics.

Please read the abstract in the section 00front of this document Copyright 1990, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Barnard, G 1990, High quality coding and reconstruction for transmission of single video images, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-10312007-110001/ > / Dissertation (M Eng (Electronic Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / unrestricted

In the modern society, mobile is gradually going to become all about video streaming. The main reasons of video growth are mobile devices such as smartphones and tablets which enable people to have access to videos they would like to watch at anywhere and anytime. However, due to the large video data size and intensive computation, video processing leads to a huge power consumption. Mobile system designers typically focus on hardware-level power optimization techniques without considering how hardware performance interfaces with viewer experience. In my research, I investigated how viewing context factors affect mobile viewing experience. Furthermore, a viewer-aware intelligent mobile video system was designed to optimize power efficiency automatically in real-time according to the viewing context and maintain the same viewing experience. Our research opened a door for developments of future viewer-aware mobile system design, accelerating low-cost mobile devices with longer battery life.

Yes / Where do the bottlenecks for information and attention lie when our visual system processes incoming stimuli? The human visual system encodes the incoming stimulus and transfers its contents into three major memory systems with increasing time scales, viz., sensory (or iconic) memory, visual short-term memory (VSTM), and long-term memory (LTM). It is commonly believed that the major bottleneck of information processing resides in VSTM. In contrast to this view, we show major bottlenecks for motion processing prior to VSTM. In the first experiment, we examined bottlenecks at the stimulus encoding stage through a partial-report technique by delivering the cue immediately at the end of the stimulus presentation. In the second experiment, we varied the cue delay to investigate sensory memory and VSTM. Performance decayed exponentially as a function of cue delay and we used the time-constant of the exponential-decay to demarcate sensory memory from VSTM. We then decomposed performance in terms of quality and quantity measures to analyze bottlenecks along these dimensions. In terms of the quality of information, two thirds to three quarters of the motion-processing bottleneck occurs in stimulus encoding rather than memory stages. In terms of the quantity of information, the motion-processing bottleneck is distributed, with the stimulus-encoding stage accounting for one third of the bottleneck. The bottleneck for the stimulus-encoding stage is dominated by the selection compared to the filtering function of attention. We also found that the filtering function of attention is operating mainly at the sensory memory stage in a specific manner, i.e., influencing only quantity and sparing quality. These results provide a novel and more complete understanding of information processing and storage bottlenecks for motion processing. / Supported by R01 EY018165 and P30 EY007551 from the National Institutes of Health (NIH).

Image quality measures are used to optimize image processing algorithms and evaluate their performances. The only reliable way to assess image quality is subjective evaluation by human observers, where the mean value of their scores is used as the quality measure. This is known as mean opinion score (MOS). In addition to this measure there are various objective (quantitative) measures. Most widely used quantitative measures are: mean squared error (MSE), peak signal to noise ratio (PSNR) and signal to noise ratio (SNR). Since these simple measures do not always produce results that are in agreement with subjective evaluation, many other quality measures have been proposed. They are mostly various modifications of MSE, which try to take into account some properties of human visual system (HVS) such as nonlinear character of brightness perception, contrast sensitivity function (CSF) and texture masking. In these approaches quality measure is computed as MSE of input image intensities or frequency domain coefficients obtained after some transform (DFT, DCT etc.), weighted by some coefficients which account for the mentioned properties of HVS. These measures have some advantages over MSE, but their ability to predict image quality is still limited. A different approach is presented here. Quality measure proposed here uses simple model of HVS, which has one user-defined parameter, whose value depends on the reference image. This quality measure is based on the average value of locally computed correlation coefficients. This takes into account structural similarity between original and distorted images, which cannot be measured by MSE or any kind of weighted MSE. The proposed measure also differentiates between random and signal dependant distortion, because these two have different effect on human observer. This is achieved by computing the average correlation coefficient between reference image and error image. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation.

Title from PDF of title page. / Document formatted into pages; contains 82 pages. / Thesis (M.S.E.E.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Image quality measures are used to optimize image processing algorithms and evaluate their performances. The only reliable way to assess image quality is subjective evaluation by human observers, where the mean value of their scores is used as the quality measure. This is known as mean opinion score (MOS). In addition to this measure there are various objective (quantitative) measures. Most widely used quantitative measures are: mean squared error (MSE), peak signal to noise ratio (PSNR) and signal to noise ratio (SNR). Since these simple measures do not always produce results that are in agreement with subjective evaluation, many other quality measures have been proposed. They are mostly various modifications of MSE, which try to take into account some properties of human visual system (HVS) such as nonlinear character of brightness perception, contrast sensitivity function (CSF) and texture masking. / ABSTRACT: In these approaches quality measure is computed as MSE of input image intensities or frequency domain coefficients obtained after some transform (DFT, DCT etc.), weighted by some coefficients which account for the mentioned properties of HVS. These measures have some advantages over MSE, but their ability to predict image quality is still limited. A different approach is presented here. Quality measure proposed here uses simple model of HVS, which has one user-defined parameter, whose value depends on the reference image. This quality measure is based on the average value of locally computed correlation coefficients. This takes into account structural similarity between original and distorted images, which cannot be measured by MSE or any kind of weighted MSE. The proposed measure also differentiates between random and signal dependant distortion, because these two have different effect on human observer. / ABSTRACT: This is achieved by computing the average correlation coefficient between reference image and error image. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

Visual sensitivity to small color difference is an important factor for precision color matching. Small color differences can be measured by the line element theory in terms of color distances between a color point and neighborhoods of points in a color space. This theory gives a smooth positive definite symmetric metric tensor which describes threshold of color differences by ellipsoids in three dimensions and ellipses in two dimensions. The metric tensor is also known as the Riemannian metric tensor. In regard to the color differences, there are many color difference formulas and color spaces to predict visual difference between two colors but, it is still challenging due to the nonexistence of a perfect uniform color space. In such case, the Riemannian metric tensor can be used as a tool to study the performance of various color spaces and color difference metrics for measuring the perceptual color differences. It also computes the shortest length or the distance between any two points in a color space. The shortest length is called a geodesic. According to Schrödinger's hypothesis geodesics starting from the neutral point of a surface of constant brightness correspond to the curves of constant hue. The chroma contours are closed curves at constant intervals from the origin measured as the distance along the constant hue geodesics. This hypothesis can be utilized to test the performance of color difference formulas to predict perceptual attributes (hue and chroma) and distribution of color stimulus in any color space. In this research work, a method to formulate line element models of color difference formulas the ΔE*ab, the ΔE*uv, the OSA-UCS ΔEE and infinitesimal approximation of CIEDE2000 (ΔE00) is presented. The Jacobian method is employed to transfer their Riemannian metric tensors in other color spaces. The coefficients of such metric tensors are used to compute ellipses in two dimensions. The performance of these four color difference formulas is evaluated by comparing computed ellipses with experimentally observed ellipses in different chromaticity diagrams. A method is also developed for comparing the similarity between a pair of ellipses. The technique works by calculating the ratio of the area of intersection and the area of union of a pair of ellipses. Similarly, at a fixed value of lightness L*, hue geodesics originating from the achromatic point and their corresponding chroma contours of the above four formulas in the CIELAB color space are computed by solving the Euler-Lagrange equations in association with their Riemannian metrics. They are compared with with the Munsell chromas and hue circles at the Munsell values 3, 5 and 7. The result shows that neither formulas are fully perfect for matching visual color difference data sets. However, Riemannized ΔE00 and the ΔEE formulas measure the visual color differences better than the ΔE*ab and the ΔE*uv formulas at local level. It is interesting to note that the latest color difference formulas like the OSA-UCS ΔEE and the Riemannized ΔE00 do not show better performance to predict hue geodesics and chroma contours than the conventional CIELAB and CIELUV color difference formulas and none of these formulas fit the Munsell data accurately

Afin de pouvoir apparier de manière précise les couleurs il est essentiel de prendre en compte la sensibilité visuelle à percevoir de petites différences de couleur. Les petites différences de couleur peuvent être mesurées par des ellipses qui décrivent les différences justes observables (just noticeable difference - JND). Ces ellipses décrivent la faculté du Système Visuel Humain à discriminer des couleurs très peu différentes. D'un point de vue mathématique, ces ellipses peuvent être modélisées par une fonction différentielle positive de forme quadratique, caractéristique de ce que l'on appelle communément une métrique Riemannienne. La métrique Riemannienne peut être considérée comme un outil utile pour évaluer l'adéquation, la robustesse et la précision, d'un espace couleur ou d'une métrique couleur, à décrire, à mesurer, correctement les différences de couleur telles qu'elles sont perçues par le Système Visuel Humain. L'un des particularités de cette métrique est qu'elle modélise la plus petite distance qui sépare deux couleurs dans un espace couleur par une ligne géodésique. Selon l'hypothèse de Schrödinger les lignes géodésiques qui partent d'un point neutre d'une surface de luminosité constante décrivent des courbes de teinte constante. Les contours de chrominance (chroma) forment alors des courbes fermées à intervalles constants à partir de ce point neutre situées à une distance constante des lignes géodésiques associées à ces teintes constances. Cette hypothèse peut être utilisée pour tester la robustesse, la précision, des formules mathématiques utilisées pour mesurer des différences couleur (color difference formulas) et pour prédire quelle valeurs peuvent prendre tel ou tel attribut perceptuel, ex. la teinte et la saturation (hue and chroma), ou telle distribution de stimulus couleur, dans n'importe quel espace couleur. Dans cette thèse, nous présentons une méthode qui permet de modéliser les éléments de ligne (lignes géodésiques), correspondants aux formules mathématiques Delta E * ab, Delta E * uv, OSA-UCS Delta EE utilisées pour mesurer des différences couleur, ainsi que les éléments de ligne correspondants à l'approximation infinitésimales du CIEDE2000. La pertinence de ces quatre formules mathématiques a été évaluée par comparaison, dans différents plans de représentation chromatique, des ellipses prédites et des ellipses expérimentalement obtenues par observation visuelle. Pour chacune de ces formules mathématiques, nous avons également testé l'hypothèse de Schrödinger, en calculant à partir de la métrique Riemannienne, les lignes géodésiques de teinte et les contours de chroma associés, puis en comparant les courbes calculées dans l'espace couleur CIELAB avec celles obtenues dans le système Munsell. Les résultats que nous avons obtenus démontrent qu'aucune de ces formules mathématiques ne prédit précisément les différences de couleur telles qu'elles sont perçues par le Système Visuel Humain. Ils démontrent également que les deux dernières formules en date, OSA-UCS Delta EE et l'approximation infinitésimale du CIEDE2000, ne sont pas plus précises que les formules conventionnelles calculées à partir des espaces couleur CIELAB et CIELUV, quand on se réfère au système Munsell (Munsell color order system) / Visual sensitivity to small color difference is an important factor for precision color matching. Small color differences can be measured by the line element theory in terms of color distances between a color point and neighborhoods of points in a color space. This theory gives a smooth positive definite symmetric metric tensor which describes threshold of color differences by ellipsoids in three dimensions and ellipses in two dimensions. The metric tensor is also known as the Riemannian metric tensor. In regard to the color differences, there are many color difference formulas and color spaces to predict visual difference between two colors but, it is still challenging due to the nonexistence of a perfect uniform color space. In such case, the Riemannian metric tensor can be used as a tool to study the performance of various color spaces and color difference metrics for measuring the perceptual color differences. It also computes the shortest length or the distance between any two points in a color space. The shortest length is called a geodesic. According to Schrödinger's hypothesis geodesics starting from the neutral point of a surface of constant brightness correspond to the curves of constant hue. The chroma contours are closed curves at constant intervals from the origin measured as the distance along the constant hue geodesics. This hypothesis can be utilized to test the performance of color difference formulas to predict perceptual attributes (hue and chroma) and distribution of color stimulus in any color space. In this research work, a method to formulate line element models of color difference formulas the ΔE*ab, the ΔE*uv, the OSA-UCS ΔEE and infinitesimal approximation of CIEDE2000 (ΔE00) is presented. The Jacobian method is employed to transfer their Riemannian metric tensors in other color spaces. The coefficients of such metric tensors are used to compute ellipses in two dimensions. The performance of these four color difference formulas is evaluated by comparing computed ellipses with experimentally observed ellipses in different chromaticity diagrams. A method is also developed for comparing the similarity between a pair of ellipses. The technique works by calculating the ratio of the area of intersection and the area of union of a pair of ellipses. Similarly, at a fixed value of lightness L*, hue geodesics originating from the achromatic point and their corresponding chroma contours of the above four formulas in the CIELAB color space are computed by solving the Euler-Lagrange equations in association with their Riemannian metrics. They are compared with with the Munsell chromas and hue circles at the Munsell values 3, 5 and 7. The result shows that neither formulas are fully perfect for matching visual color difference data sets. However, Riemannized ΔE00 and the ΔEE formulas measure the visual color differences better than the ΔE*ab and the ΔE*uv formulas at local level. It is interesting to note that the latest color difference formulas like the OSA-UCS ΔEE and the Riemannized ΔE00 do not show better performance to predict hue geodesics and chroma contours than the conventional CIELAB and CIELUV color difference formulas and none of these formulas fit the Munsell data accurately