Visual quality of printed surfaces: Study of homogeneity

International audienceThis paper introduces a homogeneity assessment method for the printed versions of uniform color images. This parameter has been specifically selected as one of the relevant attributes of printing quality. The method relies on image processing algorithms from a scanned image of the printed surface, especially the computation of gray level cooccurrence matrices and of objective homogeneity attribute inspired of Haralick's parameters. The viewing distance is also taken into account when computing the homogeneity index. Resizing and filtering of the scanned image are performed in order to keep the level of details visible by a standard human observer at short and long distances. The combination of the obtained homogeneity scores on both high and low resolution images provides a homogeneity index, which can be computed for any printed version of a uniform digital image. We tested the method on several hardcopies of a same image, and compared the scores to the empirical evaluations carried out by non-expert observers who were asked to sort the samples and to place them on a metric scale. Our experiments show a good matching between the sorting by the observers and the score computed by our algorith

DR2S : Deep Regression with Region Selection for Camera Quality Evaluation

In this work, we tackle the problem of estimating a camera capability to preserve fine texture details at a given lighting condition. Importantly, our texture preservation measurement should coincide with human perception. Consequently, we formulate our problem as a regression one and we introduce a deep convolutional network to estimate texture quality score. At training time, we use ground-truth quality scores provided by expert human annotators in order to obtain a subjective quality measure. In addition, we propose a region selection method to identify the image regions that are better suited at measuring perceptual quality. Finally, our experimental evaluation shows that our learning-based approach outperforms existing methods and that our region selection algorithm consistently improves the quality estimation

Objective and subjective assessment of perceptual factors in HDR content processing

The development of the display and camera technology makes high dynamic range (HDR) image become more and more popular. High dynamic range image give us pleasant image which has more details that makes high dynamic range image has good quality. This paper shows us the some important techniques in HDR images. And it also presents the work the author did. The paper is formed of three parts. The first part is an introduction of HDR image. From this part we can know why HDR image has good quality

WAVELET AND SINE BASED ANALYSIS OF PRINT QUALITY EVALUATIONS

Recent advances in imaging technology have resulted in a proliferation of images across different media. Before it reaches the end user, these signals undergo several transformations, which may introduce defects/artifacts that affect the perceived image quality. In order to design and evaluate these imaging systems, perceived image quality must be measured. This work focuses on analysis of print image defects and characterization of printer artifacts such as banding and graininess by using a human visual system (HVS) based framework. Specifically the work addresses the prediction of visibility of print defects (banding and graininess) by representing the print defects in terms of the orthogonal wavelet and sinusoidal basis functions and combining the detection probabilities of each basis functions to predict the response of the human visual system (HVS). The detection probabilities for basis function components and the simulated print defects are obtained from separate subjective tests. The prediction performance from both the wavelet based and sine based approaches is compared with the subjective testing results .The wavelet based prediction performs better than the sinusoidal based approach and can be a useful technique in developing measures and methods for print quality evaluations based on HVS

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

Tent-pole spatial defect pooling for prediction of subjective quality assessment of streaks and bands in color printing

Abstract. An algorithm is described for measuring the subjective, visual impact of 1-D defects (streaks and bands

Understanding perceived quality through visual representations

The formatting of images can be considered as an optimization problem, whose cost function is a quality assessment algorithm. There is a trade-off between bit budget per pixel and quality. To maximize the quality and minimize the bit budget, we need to measure the perceived quality. In this thesis, we focus on understanding perceived quality through visual representations that are based on visual system characteristics and color perception mechanisms. Specifically, we use the contrast sensitivity mechanisms in retinal ganglion cells and the suppression mechanisms in cortical neurons. We utilize color difference equations and color name distances to mimic pixel-wise color perception and a bio-inspired model to formulate center surround effects. Based on these formulations, we introduce two novel image quality estimators PerSIM and CSV, and a new image quality-assistance method BLeSS. We combine our findings from visual system and color perception with data-driven methods to generate visual representations and measure their quality. The majority of existing data-driven methods require subjective scores or degraded images. In contrast, we follow an unsupervised approach that only utilizes generic images. We introduce a novel unsupervised image quality estimator UNIQUE, and extend it with multiple models and layers to obtain MS-UNIQUE and DMS-UNIQUE. In addition to introducing quality estimators, we analyze the role of spatial pooling and boosting in image quality assessment.Ph.D

Visual Quality Assessment and Blur Detection Based on the Transform of Gradient Magnitudes

abstract: Digital imaging and image processing technologies have revolutionized the way in which we capture, store, receive, view, utilize, and share images. In image-based applications, through different processing stages (e.g., acquisition, compression, and transmission), images are subjected to different types of distortions which degrade their visual quality. Image Quality Assessment (IQA) attempts to use computational models to automatically evaluate and estimate the image quality in accordance with subjective evaluations. Moreover, with the fast development of computer vision techniques, it is important in practice to extract and understand the information contained in blurred images or regions. The work in this dissertation focuses on reduced-reference visual quality assessment of images and textures, as well as perceptual-based spatially-varying blur detection. A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The proposed method requires a very small number of reduced-reference (RR) features. Extensive experiments performed on different benchmark databases demonstrate that the proposed RRIQA method, delivers highly competitive performance as compared with the state-of-the-art RRIQA models for both natural and texture images. In the context of texture, the effect of texture granularity on the quality of synthesized textures is studied. Moreover, two RR objective visual quality assessment methods that quantify the perceived quality of synthesized textures are proposed. Performance evaluations on two synthesized texture databases demonstrate that the proposed RR metrics outperforms full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures. Last but not least, an effective approach to address the spatially-varying blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings is proposed. The evaluations of the proposed approach on a diverse sets of blurry images with different blur types, levels, and content demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201