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Reduced-Reference Methods for Measuring Quality ... - HTML

Reduced-Reference Methods for Measuring Quality ... - HTML

developped the mettric

developped the mettric [74] andd replaced the parame eters of the GGD modeel with 888 different ffeatures callculated froom the wave elet coefficiients. Figurre 10 showws an exammple where the two-phhase model is used to ccompute NRR quality. . The first pphase inputss features fii and calcula ates the proobabilities oof the diffferent learnned distortioons. The second phase e applies the distortionn specificc metrics for the test immage and ccalculates th he overall immage qualitty by usingg the probaabilities fromm the first pphase as wei ighting facttors. Figure 10. A two-pphase model estimates thee overall ima age quality: tthe first phasse calculatess the probabiilities of diffeerent distortioons, and the second phasee calculates thhe overall immage quality value by usiing the probaabilities as weighting w factoors. Fi denotees feature vaalues and pk ddenotes the proobabilities of tthe different distortions. d Shen et al. [103] proposed tthe HNR (hhybrid no-re eference) mmetric, whicch is basedd on the hyybrid of the curvelet, wwavelet and cosine trannsforms. Thhe metric calculates tthe locationns for the toop coordina ates of histoograms. Thhe metric assumes thhat the diffferent disttortions loc cate the cooefficients iin differennt clusters in the dommain of the top coor rdinates. Lii et al. [600] proposeed a metricc based on a neural nnetwork tha at inputs thhe followinng three feeatures: phase congruency, image entropy and a image gradient. YYe and Dooermann [1223] proposeed a metric based on lo ocal texturee analysis. IIt uses Gabor filterss to capturre statisticss of image patches aand a visuaal codeboook to link loocal statisticcal propertiies and visu ual quality. Although the abbovementiooned metriics have been b used to measurre differennt types of ddistortions ffrom imagees, they do not n solve thee problem oof a multiddimensionaal distortionn space. Thee metrics ca an be highlyy effective aat predictiing the effecct of a singlle distortionn, but the pe erformancee decreases if the imaage concurrently includdes more thhan one dist tortion. In aaddition, thhe metricss have been used for sppecific distoortion sets, such as thee distortionns that cann be found in the popuular LIVE image set. If I the parammeters of thhe model are learned, there shhould be kknowledge of applicaation-specifiic distortions. In adddition, thhere shouldd be know wledge of tthe featurees (metriccs) that characterize thhe distortions. An ima age capturedd or printeed by an immaging systtem has maany differennt types and d sources oof distortionns withoutt any robustt known feaatures for chharacterizin ng these disttortions. 24� � �

� � � Survey�of�Image�Quality�Measurements� 2.6.4 Color information Algorithmic metrics are usually applied only to the luminance or the intensity channel. This decision can be justified by the fact that the algorithmic metrics often determine the level of image deterioration in terms of the image structure. The HVS is more sensitive to changes in the luminance than to changes in the chrominance channels. However, some scholars have also suggested methods and metrics that utilize the components of the chromatic channels. A simple FR type metric is the color error, which is expressed as the Euclidean distance �E between the reference and test images in the CIELAB space. The performance has been increased by utilizing the properties of the HVS. For example, the S-CIELAB metric [128] accounts for the sensitivity of the HVS to spatial frequencies before the color error values are calculated. The Hong and Luo [37] metric assigns a higher weight to the dominant colors and to the color with a greater difference when calculating the color error values. If no reference image is available, the color metrics utilize the statistics of the images and different assumptions. For example, Yendrikhovskij [124] proposed a metric that computed the color naturalness of the image by using the mean and deviation values of the saturation component. Hasler and Süsstrunk [35] proposed a metric that computed the colorfulness of the image based on the mean and variation values of the chromatic components in the CIELAB space. The metric assumed that the perceived colorfulness of an image correlates with the mean and standard deviation in the chromatic plane. 2.7 Test and reference image digitization In essence, test target measurements compare measured test signals with known reference signals. The main problem with the objective measurements of imaging systems using natural images is that these measurements are missing a reference signal (camera applications) or have different reference (digital) and test signal (analog) forms (printer applications). Table 1 lists the requirements for FR, RR and NR metrics when applied to camera and printer measurements. NR metrics can be used directly in the camera applications because the output of a camera is a digital image. FR and RR metrics always require a reference image. For the camera applications, reference images are missing. For the printer applications, a reference image is available because the original digital images can function as reference signals. The problem is that the printed 25�

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