QMET : A new quality assessment metric for no-reference video coding by using human eye traversal

The subjective quality assessment (SQA) is an ever demanding approach due to its in-depth interactivity to the human cognition. The addition of no-reference based scheme could equip the SQA techniques to tackle further challenges. Existing widely used objective metrics-peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) or the subjective estimator-mean opinion score (MOS) requires original image for quality evaluation that limits their uses for the situation having no-reference. In this work, we present a no-reference based SQA technique that could be an impressive substitute to the reference-based approaches for quality evaluation. The High Efficiency Video Coding (HEVC) reference test model (HM15.0) is first exploited to generate five different qualities of the HEVC recommended eight class sequences. To assess different aspects of coded video quality, a group of ten participants are employed and their eye-tracker (ET) recorded data demonstrate closer correlation among gaze plots for relatively better quality video contents. Therefore, we innovatively calculate the amount of approximation of smooth eye traversal (ASET) by using distance, angle, and pupil-size feature from recorded gaze trajectory data and develop a new-quality metric based on eye traversal (QMET). Experimental results show that the quality evaluation carried out by QMET is highly correlated to the HM recommended coding quality. The performance of the QMET is also compared with the PSNR and SSIM metrics to justify the effectiveness of each other.International Conference Image and Vision Computing New Zealan

A novel no-reference subjective quality metric for free viewpoint video using human eye movement

The free viewpoint video (FVV) allows users to interactively control the viewpoint and generate new views of a dynamic scene from any 3D position for better 3D visual experience with depth perception. Multiview video coding exploits both texture and depth video information from various angles to encode a number of views to facilitate FVV. The usual practice for the single view or multiview quality assessment is characterized by evolving the objective quality assessment metrics due to their simplicity and real time applications such as the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM). However, the PSNR or SSIM requires reference image for quality evaluation and could not be successfully employed in FVV as the new view in FVV does not have any reference view to compare with. Conversely, the widely used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain knowledge, and many other factors that may actively influence on actual assessment. To address this limitation, in this work, we devise a no-reference subjective quality assessment metric by simply exploiting the pattern of human eye browsing on FVV. Over different quality contents of FVV, the participants eye-tracker recorded spatio-temporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the proposed QMET performs better than the SSIM and MOS in terms of assessing different aspects of coded video quality for a wide range of FVV contents.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

A novel quality metric using spatiotemporal correlational data of human eye maneuver

The popularly used subjective estimator- mean opinion score (MOS) is often biased by the testing environment, viewers mode, domain expertise, and many other factors that may actively influence on actual assessment. We therefore, devise a no- reference subjective quality assessment metric by exploiting the nature of human eye browsing on videos. The participants' eye-tracker recorded gaze-data indicate more concentrated eye- traversing approach for relatively better quality. We calculate the Length, Angle, Pupil-size, and Gaze-duration features from the recorded gaze trajectory. The content and resolution invariant operation is carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric using eye traversal (QMET). Tested results reveal that the quality evaluation carried out by QMET demonstrates a strong correlation with the most widely used peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the MOS.DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Application

Better Images : Understanding and Measuring Subjective Image-Quality

The objective in this thesis was to examine the psychological process of image-quality estimation, specifically focusing on people who are naïve in this respect and on how they estimate high-quality images. Quality estimation in this context tends to be a preference task, and to be subjective. The aim in this thesis is to enhance understanding of viewing behaviour and estimation rules in the subjective assessment of image-quality. On a more general level, the intention is to shed light on estimation processes in preference tasks. An Interpretation-Based Quality (IBQ) method was therefore developed to investigate the rules used by naïve participants in their quality estimations. It combines qualitative and quantitative approaches, and complements standard methods of image-quality measurement. The findings indicate that the content of the image influences perceptions of its quality: it influences how the interaction between the content and the changing image features is interpreted (Study 1). The IBQ method was also used to create three subjective quality dimensions: naturalness of colour, darkness and sharpness (Study 2). These dimensions were used to describe the performance of camera components. The IBQ also revealed individual differences in estimation rules: the participants differed as to whether they included interpretation of the changes perceived in an image in their estimations or whether they just commented on them (Study 4). Viewing behaviour was measured to enable examination of the task properties as well as the individual differences. Viewing behaviour was compared in two tasks that are commonly used in studies on image-quality estimation: the estimation of difference and the estimation of difference in quality (Study 3). The results showed that viewing behaviour differed even in two magnitude-estimation tasks with identical material. When they were estimating quality the participants concentrated mainly on the semantically important areas of the image, whereas in the difference-estimation task they also examined wider areas. Further examination of quality-estimation task revealed individual differences in the viewing behaviour and in the importance these viewing behaviour groups attached to the interpretation of changes in their estimations (Study 4). It seems that people engaged in a subjective preference-estimation task use different estimation rules, which is also reflected in their viewing behaviour. The findings reported in this thesis indicate that: 1) people are able to describe the basis of their quality estimations even without training when they are allowed to use their own vocabulary; 2) the IBQ method has the potential to reveal the rules used in quality estimation; 3) changes in instructions influence the way people search for information from the images; and 4) there are individual differences in terms of rules and viewing behaviour in quality-estimation tasks.Tämä väitöskirja käsittelee subjektiivista kuvanlaadun arviointiprosessia, etenkin keskittyen kuvanlaadun arvioinnin suhteen kouluttamattomien ihmisten korkea laatuisten kuvien arviointiin. Kuvanlaadulla tarkoitetaan tässä kuvan prosessointiin liittyviä tekijöitä. Tavoitteena on lisätä ymmärrystä kuvanlaadun arviointiprosessista ja sen mittaamisesta. Kuvanlaadun arviointiprosessissa on yleisesti keskitytty saamaan yksi arvio laadusta tai yksi arvio jollain etukäteen määritellyllä skaalalla. Tällöin emme tiedä mihin kouluttamaton arvioitsija olisi kiinnittänyt huomionsa ja millä perusteilla hän olisi kuvaa arvioinut. Tätä selvittämään kehitimme menetelmän, jolla voimme tarkastella ihmisten arvioissaan käyttämiä perusteita. Ihmiset kuvailivat perusteita vapaasti ja kun he saivat käyttää omaa sanastoaan, he olivat myös johdonmukaisia arvioissaan. Tätä menetelmää käytettiin myös selvittämään subjektiivisia kuvanlaatu-ulottuvuuksia, joita olivat värien luonnollisuus, tummuus ja tarkkuus. Toinen osa väitöskirjaa käsittelee kuvanlaadun arviointitehtävää prosessina. Selvitimme miten pieni muutos koehenkilöille annetussa ohjeistuksessa muuttaa heidän tapaansa katsella kuvaa heidän tehdessä siihen liittyviä arvioita. Tehtävänä oli kahdessa kuvassa näkyvien erojen arviointi joko erojen suuruuden tai kuvanlaadun erojen mukaan. Kuvanlaatua arvioitaessa huomio kiinnittyi enemmän kohtiin, jotka olivat semanttisesti merkityksellisiä, kun eroja arvioitaessa laajempi alue otettiin huomioon. Tarkastelimme myös kuvanlaadunarviointeihin liittyviä yksilöiden välisiä eroja. Koehenkilöt pystyttiin jakamaan kolmeen ryhmään heidän katselutapojensa perusteella. Nämä katselutaparyhmät erosivat toisistaan myös siinä kuinka paljon he käyttivät arvioinneissaan perusteina vaikutelmia, jotka syntyivät kuvanlaadun muutosten pohjalta. Toiset keskittyivät arvioimaan kuvanlaatua siihen liittyvien attribuuttien mukaan, kun toiset käyttivät perusteina näiden attribuuttien kuvan viestiin synnyttämiä vaikutelmia. Korkean kuvanlaadun arvioinnissa on usein kyseessä mieltymyksiin perustuva laadun arviointi. Tällöin on tärkeää antaa ihmisten käyttää omia käsitteitään, sekä ottaa huomioon että pienimmätkin tekijät, kuten sanavalinnat kysymyksissä ja ihmisten väliset eroavuudet, vaikuttavat arviointeihin. Tämä väitöskirja antaa eväitä tarkastella arviointiprosessia