Reduced-reference methods for measuring quality attributes of natural images in imaging systems

Mikko Nuutinen

Reduced-reference methods for measuring quality attributes of natural images in imaging systems

Julkaisun otsikon käännös: Vähennetyn referenssin menetelmiä kuvasysteemien luonnollisen kuvan laatuattribuuttien mittaamiseen

Mikko Nuutinen

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Kuvanlaatututkimuksen päätavoite on aikaansaada laskennallinen malli, joka ennustaa luonnollisen kuvan tai videon visuaalista laatua. Laskennallinen malli korvaisi vaikeasti järjestettävät, aikaa vievät ja kalliit subjektiiviset kokeet. Tämä väitöstutkimus sijoittuu kuvanlaadun tutkimusalueelle ja keskittyy erityisesti kuvannussysteemien, kuten kameroiden ja tulostimien kuvanlaatumittauksen sovelluksiin. Kuvannussysteemien laatua on perinteisesti määritetty testikenttämittauksilla. Testikenttämittaus karakterisoi kuinka kuvannussysteemi toistaa tai vääristää yksinkertaisia testisignaaleja. Testikenttämittaukset eivät kerro miten kuvannussysteemillä kuvattu, esitetty tai tulostettu luonnollinen kuva havaitaan visuaalisen laadun kriteereillä arvioituna. Väitöstutkimuksessa kehitettiin algoritmeja ja menetelmiä sekä kameroille että tulostetulla kuvalle, jotka mittaa kuvanlaatuattribuutteja nimenomaan suoraan luonnollisista kuvista ja joilla voidaan korvata testikenttämittaukset. Kehitetyt menetelmät perustuu vähennetyn referenssin periaatteeseen. Tulostetun kuvan sovelluksissa tulostettu luonnollinen kuva digitoitiin käyttämällä korkealaatuista kameraa sekä useaa valotusta. Digitoinnin jälkeen alkuperäisen kuvan informaation avulla mitattiin tulostetuille luonnollisille kuville laatuattribuutteja. Kameran kuvan sovellukset käytti referenssikameraa, joka kaappasi näkymään liittyvää informaatiota. Näkymäinformaation avulla eri kameroilla kuvatuista luonnollisista kuvista mitattiin laatuattribuutteja. Kehitettyjen menetelmien suorituskyky validioitiin subjektiivisella aineistolla. Verrattuna testikenttämittauksiin, kehitettyjen menetelmien suorituskyky oli korkea ennustettaessa kameralla kuvattujen tai tulostimella tulostettujen luonnollisten kuvien laatua. Tulosten perusteella ehdotetut menetelmät voivat korvata testikenttämenetelmien lisäksi myös pienen skaalan subjektiiviset testiasetelmat. Väitöstutkimus rajattiin käsittelemään ainostaan matalan tason laatuattribuutteja, kuten terävyys, kohina ja värintoisto.

Julkaisun otsikon käännös	Vähennetyn referenssin menetelmiä kuvasysteemien luonnollisen kuvan laatuattribuuttien mittaamiseen
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Oittinen, Pirkko, Vastuuprofessori
Kustantaja	Aalto University
Painoksen ISBN	978-952-60-4880-2
Sähköinen ISBN	978-952-60-4881-9
Tila	Julkaistu - 2012
OKM-julkaisutyyppi	G5 Tohtorinväitöskirja (artikkeli)

Tutkimusalat

kuvanlaatu
luonnollinen kuva
testikenttä
kamerat
tulostimet
terävyys
kohina
rakeisuus
väri
vähennetyn referenssin menetelmä

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http://urn.fi/URN:ISBN:978-952-60-4881-9

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@phdthesis{f67854025d3a40daa11d224c637829b8,

title = "Reduced-reference methods for measuring quality attributes of natural images in imaging systems",

abstract = "The main goal of the research field of image quality is to create a computational model capable of predicting the subjective visual quality of natural images and video. The model can be an alternative for expensive quality evaluations by human assessors. This dissertation aligns with this research field in the case of imaging systems, such as cameras, displays and printers. The traditional approach to measuring the quality of imaging systems is based on test targets. These targets primarily facilitate description of the performance of a system in terms of how it reproduces and distorts simple test signals rather than measure the visual quality of natural images captured or shown by an imaging system. This dissertation primarily contributes novel methods and algorithms for measuring the image quality attributes of natural images captured by cameras or printed by a printer. Both methods utilize reference image data in the reduced-reference mode. The method and algorithms developed for printers transform the printed natural test images into the form of the reference image by using a high-quality reference camera and multiple exposures. The methods and algorithms developed for camera images use a reference camera to capture scene information. The scene information is used to help measure the attributes of natural images. The main problem which needed to be solved concerns localization of areas in images from which different attributes can be measured. The challenge arises from a multidimensional distortion space in capture and display. The solution relies on low-level computational understanding of images. The methods were evaluated with subjective data. Compared with the state-of-the-art computational or test target metrics, these methods were highly effective at predicting the quality attributes of natural images captured by different cameras or printed on different papers. According to the results, the proposed methods can replace test target methods and even small-scale subjective tests in some situations.",

keywords = "image quality, natural image, test-target, cameras, printers, sharpness, noise, graininess, color, reduced-reference, kuvanlaatu, luonnollinen kuva, testikentt{\"a}, kamerat, tulostimet, ter{\"a}vyys, kohina, rakeisuus, v{\"a}ri, v{\"a}hennetyn referenssin menetelm{\"a}, image quality, natural image, test-target, cameras, printers, sharpness, noise, graininess, color, reduced-reference",

author = "Mikko Nuutinen",

year = "2012",

language = "English",

isbn = "978-952-60-4880-2",

series = "Aalto University publication series DOCTORAL DISSERTATIONS",

publisher = "Aalto University",

number = "157",

school = "Aalto University",

}

TY - THES

T1 - Reduced-reference methods for measuring quality attributes of natural images in imaging systems

AU - Nuutinen, Mikko

PY - 2012

Y1 - 2012

N2 - The main goal of the research field of image quality is to create a computational model capable of predicting the subjective visual quality of natural images and video. The model can be an alternative for expensive quality evaluations by human assessors. This dissertation aligns with this research field in the case of imaging systems, such as cameras, displays and printers. The traditional approach to measuring the quality of imaging systems is based on test targets. These targets primarily facilitate description of the performance of a system in terms of how it reproduces and distorts simple test signals rather than measure the visual quality of natural images captured or shown by an imaging system. This dissertation primarily contributes novel methods and algorithms for measuring the image quality attributes of natural images captured by cameras or printed by a printer. Both methods utilize reference image data in the reduced-reference mode. The method and algorithms developed for printers transform the printed natural test images into the form of the reference image by using a high-quality reference camera and multiple exposures. The methods and algorithms developed for camera images use a reference camera to capture scene information. The scene information is used to help measure the attributes of natural images. The main problem which needed to be solved concerns localization of areas in images from which different attributes can be measured. The challenge arises from a multidimensional distortion space in capture and display. The solution relies on low-level computational understanding of images. The methods were evaluated with subjective data. Compared with the state-of-the-art computational or test target metrics, these methods were highly effective at predicting the quality attributes of natural images captured by different cameras or printed on different papers. According to the results, the proposed methods can replace test target methods and even small-scale subjective tests in some situations.

AB - The main goal of the research field of image quality is to create a computational model capable of predicting the subjective visual quality of natural images and video. The model can be an alternative for expensive quality evaluations by human assessors. This dissertation aligns with this research field in the case of imaging systems, such as cameras, displays and printers. The traditional approach to measuring the quality of imaging systems is based on test targets. These targets primarily facilitate description of the performance of a system in terms of how it reproduces and distorts simple test signals rather than measure the visual quality of natural images captured or shown by an imaging system. This dissertation primarily contributes novel methods and algorithms for measuring the image quality attributes of natural images captured by cameras or printed by a printer. Both methods utilize reference image data in the reduced-reference mode. The method and algorithms developed for printers transform the printed natural test images into the form of the reference image by using a high-quality reference camera and multiple exposures. The methods and algorithms developed for camera images use a reference camera to capture scene information. The scene information is used to help measure the attributes of natural images. The main problem which needed to be solved concerns localization of areas in images from which different attributes can be measured. The challenge arises from a multidimensional distortion space in capture and display. The solution relies on low-level computational understanding of images. The methods were evaluated with subjective data. Compared with the state-of-the-art computational or test target metrics, these methods were highly effective at predicting the quality attributes of natural images captured by different cameras or printed on different papers. According to the results, the proposed methods can replace test target methods and even small-scale subjective tests in some situations.

KW - image quality

KW - natural image

KW - test-target

KW - cameras

KW - printers

KW - sharpness

KW - noise

KW - graininess

KW - color

KW - reduced-reference

KW - kuvanlaatu

KW - luonnollinen kuva

KW - testikenttä

KW - kamerat

KW - tulostimet

KW - terävyys

KW - kohina

KW - rakeisuus

KW - väri

KW - vähennetyn referenssin menetelmä

KW - image quality

KW - natural image

KW - test-target

KW - cameras

KW - printers

KW - sharpness

KW - noise

KW - graininess

KW - color

KW - reduced-reference

M3 - Doctoral Thesis

SN - 978-952-60-4880-2

T3 - Aalto University publication series DOCTORAL DISSERTATIONS

PB - Aalto University

ER -

Reduced-reference methods for measuring quality attributes of natural images in imaging systems

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