25 research outputs found
Quality Analysis of a Printed Natural Reference Image
Tämän diplomityön tarkoituksena oli tutkia paperin vaikutusta koettuun kuvanlaatuun. Päätavoitteeksi asetettiin automaattisen, objektiivisen ohjelmistojärjestelmän kehittäminen ennustamaan ihmisen arviota paperin kuvanlaatuominaisuuksista. Tutkimusprojekti koostui neljästä vaiheesta: testikuvan suunnittelusta kuvanlaadun tutkimukseen, subjektiivisen kokonaislaadun ja laatuattribuuttien arvioinnista testikuvasta, ohjelmiston kehittämisestä ennustamaan laatuattribuutteja sekä visuaalisen laatumallin muodostamisesta ilmaisemaan kokonaislaatua yhdellä laatuarvosanalla. Tutkimuksessa käsiteltiin neljää laatuattribuuttia: värikkyyttä, kontrastia, terävyyttä ja kohinaa. Painatusmenetelmänä käytettiin mustesuihkutulostusta.
Ensimmäisessä vaiheessa luotiin luonnollinen referenssikuva kuvanlaadun subjektiivista ja objektiivista arviointia varten. Suunnittelussa painotettiin laatuominaisuuksien lisäksi korkean tason ominaisuuksia, kuten luonnollisuutta, tasapainoa, ja esteettistä vaikutelmaa. Erityispiirteenä kuvaan lisättiin seitsemän GretagMacbeth testiväriä, jotka sisällytettiin kuvassa sijaitseviin luonnollisiin esineisiin. Seuraavassa vaiheessa suoritettiin subjektiivinen testaus ihmisen visuaalisen laatuarvion mittaamiseksi, josta saatuja laatuattribuuttien referenssiarvoja käytettiin objektiivisten laatumittojen suunnittelussa Matlab-ohjelmistolle. Lopuksi kehitetyt laatumitat yhdistettiin tilastollisen regressioanalyysin avulla yhdeksi arvosanaksi paperin kokonaislaadusta, ns. visuaaliseksi laatumalliksi. Myös laatuattribuuteille muodostettiin regressiomallit.
Tutkimuksen tuloksena luotiin toimivat ja tilastollisesti tarkat objektiiviset mitat kolmelle laatuattribuutille: värikkyydelle, kontrastille ja kohinalle. Lisäksi kehitettiin mitta värivirheen laskentaan. Myös visuaalisen laatumallin toteutuksessa onnistuttiin hyvin, ja kaikkien regressiomallien selitysasteet olivat tilastollisesti korkeita. Subjektiivisten arvosanojen samankaltaisuus laadun ja laatuattribuuttien välillä johti kuitenkin ongelmiin regressiomallien yleistämisessä, mistä johtuen mallien käyttöä ei voitu suositella reaalimaailman sovelluksissa. Erityistä paneutumista vaativat myös testikuvan suuri värikkyys sekä ohjelmallisten laatumittojen optimointi paperi- ja painatusympäristöön.This thesis was contributed to study the image quality properties of printing papers. The main goal was to produce an automatic, objective software system for predicting human opinion on the print quality of papers. To reach this goal, the project was divided into four phases: the development of a reference image for image quality evaluation, the assessment of subjective print quality from the reference image, the programming of quality analysis software for quality attributes, and the construction of a single grade for print quality, visual quality index (VQI). Four low-level quality attributes were studied: colorfulness, contrast, sharpness, and noise. Only inkjet printing technology was covered.
In the first phase, a natural reference image was developed for subjective and objective image quality testing. Focus was placed not only on quality aspects, but also on the high-level properties of the image, i.e. naturalness, balance, and aesthetical expression. Furthermore, presenting a unique feature for a reference image of this kind, seven GretagMacbeth test colors were implemented into natural objects in the image. During later phases, subjective tests were arranged to gather the subjective reference data of print quality for software development with Matlab. Finally, the computed quality attribute scores were combined with statistical regression analysis into a single grade for the print quality of papers, VQI, accompanied with individual regression models for the quality attributes.
The outcome of the software development was three functional and statistically accurate Matlab implementations, i.e. for colorfulness, contrast, and noise, complemented with a color difference method. The implementation of the VQI was successful as well, showing remarkably strong goodness measures. However, the generalization of the regression models was compromised by the strong cross-attribute similarity of the subjective reference data, eventually preventing the feasibility of the models in real world applications. Other issues requiring attention included handling the high colorfulness of the reference image and optimizing the software to the print context
Human Jury Assessment of Image Quality as a Measurement: Modeling with Bayes Network
Image quality assessment has been done previously manually by human jury assessment as reference. Due to lack of rationality in human jury voting and its high costs it is desirable to replace it with instrumental measurements that can predict jury assessment reliably. But high uncertainty in jury assessments and sensitivity of image context make it cumbersome for the instrumental measurements. Previous research has shown that modeling with a Bayesian network can resolve some of the problems.
A Bayesian network is a belief network of causal model representation of multivariate probabilistic distributions that describes the relationships between the interacting nodes in the form of conditional independency. By conditioning and marginalization operations we can estimate the conditional probabilities of unmeasured elements and their uncertainty in Bayes network. In this thesis we have considered a four-layer pre-existing Bayes network consisting of both qualitative and quantitative component and we have tried to assess probabilities of quality elements assessed by jurors based on instrumental measurement values. To analyze and to quantify the relationship between perceptual quality elements and instrumental measurements, we have calculated mutual information from our provided data set. Based on mutual information calculation and Kullback-Leibler distance measure we have investigated the sensitivity of the network, and we have tried to validate a feasible network model where network parameters have been selected such a way that it minimizes the uncertainties of our chosen Bayes network
Modeling Perceptual Trade-offs for Designing HDR Displays
Display technology has evolved in pursuit of perceptual pleasure by providing realism and visual impact. The endeavor of the evolution has brought HDR displays to the market. HDR displays, which have become the mainstream display technology recently, are considered not only the present but also the future of displays because of their daunting technical goals: A peak luminance of 10,000 cd/m^2 and near-monochromatic primaries. However, both positive and negative prospects in terms of perceptual aspects for future HDR displays coexist. On the positive side, it is expected that HDR displays will provide better image quality and more vivid color. On the negative side, apart from technical barriers such as production cost and power consumption, HDR displays will induce side effects, for example, observer metamerism, which refers to the phenomenon that color matches for one observer result in color mismatches for other observers. This particular side effect could be a severe issue in HDR displays as their narrow-band primaries likely worsen the color mismatches. Hence, critical to the success of future HDR displays is dealing properly with the perceptual trade-offs. In other words, future HDR display designers need to select physical specifications that maximize perceptual benefits while minimizing adverse effects. This dissertation aims at exploring both potentially positive and negative aspects of future HDR displays, using various perceptual assessments. In particular, the dissertation focuses on two physical factors of a display device: peak luminance and chromaticity color gamut, and the effects of the two factors on related human perception: image quality, observer metamerism, and colorfulness. The ultimate goal of this dissertation is to address the related human perception aroused by the physical factors and propose models to help design future HDR displays. In order to achieve the goal, the dissertation first addresses the image quality trade-off relationship between peak luminance and chromaticity color gamut. A psychophysical experiment was used to develop models to predict equivalent image quality under the trade-off between peak luminance and chromaticity gamut as a function of the perceptual attributes lightness and chroma. Second, a novel approach based on a computational evaluation to investigate potential observer metamerism in HDR displays was explored. This research shows how observer metamerism in HDR displays varies with varying peak luminance and chromaticity color gamut. This research aims at developing a straightforward model to predict observer metamerism in HDR displays based on the computational evaluation. Third, a psychophysical experiment to derive a colorfulness scale for very saturated colors is carried out. This experiment focuses on understanding how the sensitivity of the human visual system responds to highly-saturated colors that extend beyond the stimuli studied in previous research. The colorfulness scale would help both advanced lighting system and display system designers. Fourth, the dissertation suggests an evaluation tool devised based on the observer metamerism and colorfulness scale works that can be utilized to determine the physical specification of HDR displays, maximizing perceptually positive effects while minimizing perceptually negative effects at the same time
Appearance-based image splitting for HDR display systems
High dynamic range displays that incorporate two optically-coupled image planes have recently been developed. This dual image plane design requires that a given HDR input image be split into two complementary standard dynamic range components that drive the coupled systems, therefore there existing image splitting issue. In this research, two types of HDR display systems (hardcopy and softcopy HDR display) are constructed to facilitate the study of HDR image splitting algorithm for building HDR displays. A new HDR image splitting algorithm which incorporates iCAM06 image appearance model is proposed, seeking to create displayed HDR images that can provide better image quality. The new algorithm has potential to improve image details perception, colorfulness and better gamut utilization. Finally, the performance of the new iCAM06-based HDR image splitting algorithm is evaluated and compared with widely spread luminance square root algorithm through psychophysical studies
The Influence of media displays and image quality attributes for HDR image reproductions
High Dynamic Range (HDR) photography has been in existence at least since the time of Ansel Adams, with his experiments using analog film and darkroom techniques for the production of black and white prints in the 1940\u27s (Ashbrook, 2010). This photographic method has the ability to provide a more accurate representation of a scene through a greater range of the light and dark areas captured in an image. In the mid-20th century HDR Photography it has continued to grow in popularity among those interested in photography wishing to optimize their resulting image beyond a more commonly used technique. Presently, the limitations of commonly available reproduction technologies can lead to unpredictable output results through media such as monitor displays and inkjet prints. The purpose of this research was to determine the influence of quality attributes and image content on the preference of display media for HDR image reproductions. To achieve this purpose, a psychophysical experiment was conducted of 38 observers with previous imaging related exposure. This part of the study consisted of HDR comparisons across both a monitor display device and inkjet prints. Through qualitative and quantitative methods, common trends were identified among observer responses. The results show that for inkjet prints are the most preferred for the output of HDR images, specifically when printed on a metallic substrate. Additionally, the content of displayed images can directly impact display preference depending on the viewer\u27s perception and relationship formed with the photographic image. When evaluating HDR images across two media platforms, quality attributes comprising of a strong influence towards preference are sharpness, naturalness, contrast and highlights while artifacts, physical qualities and shadows were found to have barely any influence. Within the attributes related to HDR, relationships between attributes are found to be significant regarding image evaluation, leading to areas of further research
Evaluation of the color image and video processing chain and visual quality management for consumer systems
With the advent of novel digital display technologies, color processing is increasingly becoming a key aspect in consumer video applications. Today’s state-of-the-art displays require sophisticated color and image reproduction techniques in order to achieve larger screen size, higher luminance and higher resolution than ever before. However, from color science perspective, there are clearly opportunities for improvement in the color reproduction capabilities of various emerging and conventional display technologies. This research seeks to identify potential areas for improvement in color processing in a video processing chain. As part of this research, various processes involved in a typical video processing chain in consumer video applications were reviewed. Several published color and contrast enhancement algorithms were evaluated, and a novel algorithm was developed to enhance color and contrast in images and videos in an effective and coordinated manner. Further, a psychophysical technique was developed and implemented for performing visual evaluation of color image and consumer video quality. Based on the performance analysis and visual experiments involving various algorithms, guidelines were proposed for the development of an effective color and contrast enhancement method for images and video applications. It is hoped that the knowledge gained from this research will help build a better understanding of color processing and color quality management methods in consumer video
Camera based Display Image Quality Assessment
This thesis presents the outcomes of research carried out by the PhD candidate Ping Zhao during 2012 to 2015 in Gjøvik University College. The underlying research was a part of the HyPerCept project, in the program of Strategic Projects for University Colleges, which was funded by The Research Council of Norway. The research was engaged under the supervision of Professor Jon Yngve Hardeberg and co-supervision of Associate Professor Marius Pedersen, from The Norwegian Colour and Visual Computing Laboratory, in the Faculty of Computer Science and Media Technology of Gjøvik University College; as well as the co-supervision of Associate Professor Jean-Baptiste Thomas, from The Laboratoire Electronique, Informatique et Image, in the Faculty of Computer Science of Universit´e de Bourgogne. The main goal of this research was to develop a fast and an inexpensive camera based display image quality assessment framework. Due to the limited time frame, we decided to focus only on projection displays with static images displayed on them. However, the proposed methods were not limited to projection displays, and they were expected to work with other types of displays, such as desktop monitors, laptop screens, smart phone screens, etc., with limited modifications. The primary contributions from this research can be summarized as follows:
1. We proposed a camera based display image quality assessment framework, which was originally designed for projection displays but it can be used for other types of displays with limited modifications.
2. We proposed a method to calibrate the camera in order to eliminate unwanted vignetting artifact, which is mainly introduced by the camera lens.
3. We proposed a method to optimize the camera’s exposure with respect to the measured luminance of incident light, so that after the calibration all camera sensors share a common linear response region.
4. We proposed a marker-less and view-independent method to register one captured image with its original at a sub-pixel level, so that we can incorporate existing full reference image quality metrics without modifying them.
5. We identified spatial uniformity, contrast and sharpness as the most important image quality attributes for projection displays, and we used the proposed framework to evaluate the prediction performance of the state-of-the-art image quality metrics regarding these attributes.
The proposed image quality assessment framework is the core contribution of this research. Comparing to conventional image quality assessment approaches, which were largely based on the measurements of colorimeter or spectroradiometer, using camera as the acquisition device has the advantages of quickly recording all displayed pixels in one shot, relatively inexpensive to purchase the instrument. Therefore, the consumption of time and resources for image quality assessment can be largely reduced. We proposed a method to calibrate the camera in order to eliminate unwanted vignetting artifact primarily introduced by the camera lens. We used a hazy sky as a closely uniform light source, and the vignetting mask was generated with respect to the median sensor responses over i only a few rotated shots of the same spot on the sky. We also proposed a method to quickly determine whether all camera sensors were sharing a common linear response region. In order to incorporate existing full reference image quality metrics without modifying them, an accurate registration of pairs of pixels between one captured image and its original is required. We proposed a marker-less and view-independent image registration method to solve this problem. The experimental results proved that the proposed method worked well in the viewing conditions with a low ambient light. We further identified spatial uniformity, contrast and sharpness as the most important image quality attributes for projection displays. Subsequently, we used the developed framework to objectively evaluate the prediction performance of the state-of-art image quality metrics regarding these attributes in a robust manner. In this process, the metrics were benchmarked with respect to the correlations between the prediction results and the perceptual ratings collected from subjective experiments. The analysis of the experimental results indicated that our proposed methods were effective and efficient. Subjective experiment is an essential component for image quality assessment; however it can be time and resource consuming, especially in the cases that additional image distortion levels are required to extend the existing subjective experimental results. For this reason, we investigated the possibility of extending subjective experiments with baseline adjustment method, and we found that the method could work well if appropriate strategies were applied. The underlying strategies referred to the best distortion levels to be included in the baseline, as well as the number of them
Preferred color correction for mixed taking-illuminant placement and cropping
The growth of automatic layout capabilities for publications such as photo books and image sharing websites enables consumers to create personalized presentations without much experience or the use of professional page design software. Automated color correction of images has been well studied over the years, but the methodology for determining how to correct images has almost exclusively considered images as independent indivisible objects. In modern documents, such as photo books or web sharing sites, images are automatically placed on pages in juxtaposition to others and some images are automatically cropped. Understanding how color correction preferences are impacted by complex arrangements has become important. A small number of photographs taken under a variety illumination conditions were presented to observers both individually and in combinations. Cropped and uncropped versions of the shots were included. Users had opportunities to set preferred color balance and chroma for the images within the experiment. Analyses point toward trends indicating a preference for higher chroma for most cropped images in comparison to settings for the full spatial extent images. It is also shown that observers make different color balance choices when correcting an image in isolation versus when correcting the same image in the presence of a second shot taken under a different illuminant. Across 84 responses, approximately 60% showed the tendency to choose image white points that were further from the display white point when multiple images from different taking illuminants were simultaneously presented versus when the images were adjusted in isolation on the same display. Observers were also shown to preserve the relative white point bias of the original taking illuminants