Interpretation techniques development

Processing, interpretation, classification, recognition, and enhancement techniques for use with ERTS-1 multispectral-imagery - Conferenc

Evaluation of changes in image appearance with changes in displayed image size

This research focused on the quantification of changes in image appearance when images are displayed at different image sizes on LCD devices. The final results provided in calibrated Just Noticeable Differences (JNDs) on relevant perceptual scales, allowing the prediction of sharpness and contrast appearance with changes in the displayed image size. A series of psychophysical experiments were conducted to enable appearance predictions. Firstly, a rank order experiment was carried out to identify the image attributes that were most affected by changes in displayed image size. Two digital cameras, exhibiting very different reproduction qualities, were employed to capture the same scenes, for the investigation of the effect of the original image quality on image appearance changes. A wide range of scenes with different scene properties was used as a test-set for the investigation of image appearance changes with scene type. The outcomes indicated that sharpness and contrast were the most important attributes for the majority of scene types and original image qualities. Appearance matching experiments were further conducted to quantify changes in perceived sharpness and contrast with respect to changes in the displayed image size. For the creation of sharpness matching stimuli, a set of frequency domain filters were designed to provide equal intervals in image quality, by taking into account the system’s Spatial Frequency Response (SFR) and the observation distance. For the creation of contrast matching stimuli, a series of spatial domain S-shaped filters were designed to provide equal intervals in image contrast, by gamma adjustments. Five displayed image sizes were investigated. Observers were always asked to match the appearance of the smaller version of each stimulus to its larger reference. Lastly, rating experiments were conducted to validate the derived JNDs in perceptual quality for both sharpness and contrast stimuli. Data obtained by these experiments finally converted into JND scales for each individual image attribute. Linear functions were fitted to the final data, which allowed the prediction of image appearance of images viewed at larger sizes than these investigated in this research

Quality Assessment for CRT and LCD Color Reproduction Using a Blind Metric

This paper deals with image quality assessment that is capturing the focus of several research teams from academic and industrial parts. This field has an important role in various applications related to image from acquisition to projection. A large numbers of objective image quality metrics have been developed during the last decade. These metrics are more or less correlated to end-user feedback and can be separated in three categories: 1) Full Reference (FR) trying to evaluate the impairment in comparison to the reference image, 2) Reduced Reference (RR) using some features extracted from an image to represent it and compare it with the distorted one and 3) No Reference (NR) measures known as distortions such as blockiness, blurriness,. . .without the use of a reference. Unfortunately, the quality assessment community have not achieved a universal image quality model and only empiricalmodels established on psychophysical experimentation are generally used. In this paper, we focus only on the third category to evaluate the quality of CRT (Cathode Ray Tube) and LCD (Liquid Crystal Display) color reproduction where a blind metric is, based on modeling a part of the human visual system behavior. The objective results are validated by single-media and cross-media subjective tests. This allows to study the ability of simulating displays on a reference one

Underlying elements of image quality assessment: : Preference and terminology for communicating image quality characteristics

Image quality markedly affects the evaluation of images, and its control is crucial in studies using natural visual scenes as stimuli. Various image elements, such as sharpness or naturalness, can impact how observers view images and more directly how they evaluate their quality. To gain a better understanding of the types of interactions between these various elements, we conducted a study with a large set of images with multiple overlapping distortions, covering a wide range of quality variation. Observers assigned a quality rating on a 0-10 scale plus a verbal description of the images, explaining the elements on which their rating was based. Regression model predicting image quality ratings using 68 attributes uncovered the link between verbal descriptions and quality ratings and the importance of the image quality rating for each of the 68 image attributes. Brightness, naturalness, and good colors seem to be related to the highest image quality preference. However, the most important elements for predicting good image quality were related to image fidelity such as graininess and sharpness. This indicates that a certain level of image fidelity must be achieved before more subjective associations with, for instance, naturalness can emerge. Of the attributes, 72% had a negative impact on the preference judgment. This negative bias may be due to the fact that there are more ways that observers can perceive an image to fail than to excel when they are asked to evaluate image quality.Image quality markedly affects the evaluation of images, and its control is crucial in studies using natural visual scenes as stimuli. Various image elements, such as sharpness or naturalness, can impact how observers view images and, more directly, how they evaluate their quality. To gain a better understanding of the types of interactions between these various elements, we conducted a study with a large set of images with multiple overlapping distortions, covering a wide range of quality variation. Observers assigned a quality rating of the images on a 0–10 scale and gave a verbal description explaining the elements on which their rating was based. A regression model predicting image quality ratings using 68 attributes uncovered the link between verbal descriptions and quality ratings and the importance of the image quality rating for each of the 68 image attributes. Brightness, naturalness, and good colors seem to be related to the highest image quality preference. However, the most important elements for predicting good image quality were related to image fidelity such as graininess and sharpness. This indicates that a certain level of image fidelity must be achieved before more subjective associations with, for instance, naturalness can emerge. Of the attributes, 72% had a negative impact on the preference judgment. This negative bias may be due to the fact that there are more ways that observers can perceive an image to fail than to excel when they are asked to evaluate image quality.Peer reviewe

Kuvanlaatukokemuksen arvionnin instrumentit

This dissertation describes the instruments available for image quality evaluation, develops new methods for subjective image quality evaluation and provides image and video databases for the assessment and development of image quality assessment (IQA) algorithms. The contributions of the thesis are based on six original publications. The first publication introduced the VQone toolbox for subjective image quality evaluation. It created a platform for free-form experimentation with standardized image quality methods and was the foundation for later studies. The second publication focused on the dilemma of reference in subjective experiments by proposing a new method for image quality evaluation: the absolute category rating with dynamic reference (ACR-DR). The third publication presented a database (CID2013) in which 480 images were evaluated by 188 observers using the ACR-DR method proposed in the prior publication. Providing databases of image files along with their quality ratings is essential in the field of IQA algorithm development. The fourth publication introduced a video database (CVD2014) based on having 210 observers rate 234 video clips. The temporal aspect of the stimuli creates peculiar artifacts and degradations, as well as challenges to experimental design and video quality assessment (VQA) algorithms. When the CID2013 and CVD2014 databases were published, most state-of-the-art I/VQAs had been trained on and tested against databases created by degrading an original image or video with a single distortion at a time. The novel aspect of CID2013 and CVD2014 was that they consisted of multiple concurrent distortions. To facilitate communication and understanding among professionals in various fields of image quality as well as among non-professionals, an attribute lexicon of image quality, the image quality wheel, was presented in the fifth publication of this thesis. Reference wheels and terminology lexicons have a long tradition in sensory evaluation contexts, such as taste experience studies, where they are used to facilitate communication among interested stakeholders; however, such an approach has not been common in visual experience domains, especially in studies on image quality. The sixth publication examined how the free descriptions given by the observers influenced the ratings of the images. Understanding how various elements, such as perceived sharpness and naturalness, affect subjective image quality can help to understand the decision-making processes behind image quality evaluation. Knowing the impact of each preferential attribute can then be used for I/VQA algorithm development; certain I/VQA algorithms already incorporate low-level human visual system (HVS) models in their algorithms.Väitöskirja tarkastelee ja kehittää uusia kuvanlaadun arvioinnin menetelmiä, sekä tarjoaa kuva- ja videotietokantoja kuvanlaadun arviointialgoritmien (IQA) testaamiseen ja kehittämiseen. Se, mikä koetaan kauniina ja miellyttävänä, on psykologisesti kiinnostava kysymys. Työllä on myös merkitystä teollisuuteen kameroiden kuvanlaadun kehittämisessä. Väitöskirja sisältää kuusi julkaisua, joissa tarkastellaan aihetta eri näkökulmista. I. julkaisussa kehitettiin sovellus keräämään ihmisten antamia arvioita esitetyistä kuvista tutkijoiden vapaaseen käyttöön. Se antoi mahdollisuuden testata standardoituja kuvanlaadun arviointiin kehitettyjä menetelmiä ja kehittää niiden pohjalta myös uusia menetelmiä luoden perustan myöhemmille tutkimuksille. II. julkaisussa kehitettiin uusi kuvanlaadun arviointimenetelmä. Menetelmä hyödyntää sarjallista kuvien esitystapaa, jolla muodostettiin henkilöille mielikuva kuvien laatuvaihtelusta ennen varsinaista arviointia. Tämän todettiin vähentävän tulosten hajontaa ja erottelevan pienempiä kuvanlaatueroja. III. julkaisussa kuvaillaan tietokanta, jossa on 188 henkilön 480 kuvasta antamat laatuarviot ja niihin liittyvät kuvatiedostot. Tietokannat ovat arvokas työkalu pyrittäessä kehittämään algoritmeja kuvanlaadun automaattiseen arvosteluun. Niitä tarvitaan mm. opetusmateriaalina tekoälyyn pohjautuvien algoritmien kehityksessä sekä vertailtaessa eri algoritmien suorituskykyä toisiinsa. Mitä paremmin algoritmin tuottama ennuste korreloi ihmisten antamiin laatuarvioihin, sen parempi suorituskyky sillä voidaan sanoa olevan. IV. julkaisussa esitellään tietokanta, jossa on 210 henkilön 234 videoleikkeestä tekemät laatuarviot ja niihin liittyvät videotiedostot. Ajallisen ulottuvuuden vuoksi videoärsykkeiden virheet ovat erilaisia kuin kuvissa, mikä tuo omat haasteensa videoiden laatua arvioiville algoritmeille (VQA). Aikaisempien tietokantojen ärsykkeet on muodostettu esimerkiksi sumentamalla yksittäistä kuvaa asteittain, jolloin ne sisältävät vain yksiulotteisia vääristymiä. Nyt esitetyt tietokannat poikkeavat aikaisemmista ja sisältävät useita samanaikaisia vääristymistä, joiden interaktio kuvanlaadulle voi olla merkittävää. V. julkaisussa esitellään kuvanlaatuympyrä (image quality wheel). Se on kuvanlaadun käsitteiden sanasto, joka on kerätty analysoimalla 146 henkilön tuottamat 39 415 kuvanlaadun sanallista kuvausta. Sanastoilla on pitkät perinteet aistinvaraisen arvioinnin tutkimusperinteessä, mutta niitä ei ole aikaisemmin kehitetty kuvanlaadulle. VI. tutkimuksessa tutkittiin, kuinka arvioitsijoiden antamat käsitteet vaikuttavat kuvien laadun arviointiin. Esimerkiksi kuvien arvioitu terävyys tai luonnollisuus auttaa ymmärtämään laadunarvioinnin taustalla olevia päätöksentekoprosesseja. Tietoa voidaan käyttää esimerkiksi kuvan- ja videonlaadun arviointialgoritmien (I/VQA) kehitystyössä

ArborZ: Photometric Redshifts Using Boosted Decision Trees

Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area imaging surveys. In this paper we introduce a photometric redshift algorithm, ArborZ, based on the machine-learning technique of Boosted Decision Trees. We study the algorithm using galaxies from the Sloan Digital Sky Survey and from mock catalogs intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show that it improves upon the performance of existing algorithms. Moreover, the method naturally leads to the reconstruction of a full probability density function (PDF) for the photometric redshift of each galaxy, not merely a single "best estimate" and error, and also provides a photo-z quality figure-of-merit for each galaxy that can be used to reject outliers. We show that the stacked PDFs yield a more accurate reconstruction of the redshift distribution N(z). We discuss limitations of the current algorithm and ideas for future work.Comment: 10 pages, 13 figures, submitted to Ap

Subjective and objective quality evaluation of synthetic and high dynamic range images

Recent years have seen a huge growth in the acquisition, transmission, and storage of videos. The visual data consists of both natural scenes as well as synthetic scenes, such as animated movies, cartoons and video games. In all these cases, the ultimate goal is to provide the viewers with a satisfactory quality-of-experience. In addition to the traditional 8-bit images, high dynamic range imaging is also becoming popular because of its ability to represent the real world luminances more realistically. Coming up with objective image quality assessment algorithms for these applications is an interesting research problem. In this work, I have developed a synthetic image quality database by introducing varying degrees of different types of distortions and conducted a subjective experiment in order to obtain the ground-truth data. I evaluated the performance of state-of-the-art image quality assessment algorithms (typically meant for natural images) on this database, especially no-reference algorithms that have not been applied to the domain of computer graphics images before. I identified the top-performing algorithms along with analyzing the types of distortions on which the present algorithms show a less impressive performance. For high dynamic range(HDR) images, I have designed two new full-reference image quality assessment algorithms to judge the quality of tonemapped HDR images using statistical features extracted from them. I have also conducted a massive online crowd-sourced subjective test for HDR image artifacts arising from tonemapping, multiple-exposure fusion and post processing. To the best of our knowledge, presently this is the largest HDR image database in the world involving the largest number of source images and most number of human evaluations. Based on the subjective evaluations obtained, I have also proposed machine learning based no-reference image quality assessment algorithms to predict the perceptual quality of HDR images.Electrical and Computer Engineerin