"... In PAGE 4: ... 8 Table 3: A sample striped application trace. 10 Table4 : Quality measures. 10 Table 5: Correlation measures.... In PAGE 11: ... We repeatedly select an address bit with the highest corresponding quality measure and then update the quality measures using the correlations. As an example, for the trace given in Table 3 and quality/correlation measures computed in Table4 and Table 5, the algorithm first select A0 as the best index ... ..."

"... In PAGE 9: ...2. Data mining results and analysis The one-out-of n (1 out of 114) and k (=10) fold (10 times 10% out of 114) cross-validation results for as-is data set with 114 patient data set are shown in Table4 . The one-out-of n cross-validation produced higher classiFFcation accuracy with 28 misclassiFFcations (5 for above- median and 23 for below-median).... In PAGE 10: ...64 2.22 d Ven S The results of the one-out-of n (=114) and k (=10) fold cross-validation for the 114 patient data set with transformed parameters are shown in Table4 . The transformation of parameters resulted in increase in the accuracy to 84.... ..."

"... In PAGE 2: ...mproves perforance [10]. We use the opinions of human observers to evaluate our easure and others. In the last thre decades, a great deal of efort has gone into the development of image quality measures [7]. Se Table1 for examples of existing similarity measures. Table 1.... ..."

"... In PAGE 5: ...5.3 Global quality measure The calculation of the average maximum distance and the average distance difference shows the expected results (see Table1 ). The class OK contains all objects where the human operator comes to the result that there is definitely no change.... ..."

"... In PAGE 1: ... In this section we analyze correlation of objective measures with subjective grades and we propose objective measures, which should be used in relation to each image compression system, and objective measures, which are suitable for the comparison of picture quality between di erent compression systems. 2 PICTURE QUALITY MEASURES Among many objective numerical measures of picture quality, that are based on computable distortion mea- sures, we have chosen those listed in Table1 . All measures are discrete and they provide some degree of closeness be- tween two digital images by exploiting the di erences in the statistical distributions of pixel values.... In PAGE 2: ... Mean squared error (MSE ) and Peak Signal to Noise Ratio (PSNR) are the most common measures of pic- ture quality in image compression systems, despite the fact that they are not adequate as perceptually meaning- ful measures [5]. In addition to objective measures listed in Table1 , we chose to use perception based objective evaluation, quanti ed by Picture Quality Scale (PQS ) [6] and a perception based subjective evaluation, quanti- ed by Mean Opinion Score (MOS ) [7]. For the set of distorted images, the MOS values were obtained from an experiment involving 20 non-expert viewers.... In PAGE 7: ... The type of degradation can not be evaluated by objective picture quality measures and sub- jective assessments are needed to estimate degradation annoyance for human visual system. Table 3 shows the correlation between the numeri- cal objective quality measures introduced in Table1 and MOS . As a measure of the extent of the linear relation- ship, the Pearson product-moment (r) was used [20].... In PAGE 7: ...nd 0.9 for JPEG). So, we propose use of MD for com- parison of picture quality in di erent compression systems because of its good correlation with MOS and computing simplicity. LMSE has also good correlation with MOS for all tested compression techniques but this measure is not so simple as MD and has higher computational com- plexity than MD (see equations in Table1 for MD and LMSE ). 6 CONCLUSION The results of an evaluation concerning the usefulness of a number of objective quality measures in image com- pression systems have been presented.... ..."

"... In PAGE 22: ...rom Table 5, the average qualities vary from a low of 2.57 to a high of 5.23 units across products and markets. To assist in the interpretation of these mag- nitudes, Table6 presents a simple regression of implied qualities on the char- acteristics of the cable services whose quality they measure. Since cable services are bundles of programming networks, this amounts to the following regression: prime q* p b X H11001 H9255 , Gi, c,(15) ic ic ic where are the indicators of the programming offered on service i in market Xic c and measures market-product deviations from the expected quality given H9255ic .... In PAGE 23: ...52 3 estimates can be interpreted as the causal effects of inclusion of each network on overall service quality.35 The first column of Table6 presents the results of this regression.36 The interpretation of the coefficient on ESPN, for example, is that adding ESPN to a cable service is estimated to increase the quality of that service by .... In PAGE 23: ...94 utils. The second column of Table6 transforms this effect into the WTP for the average consumer just willing to purchase the high-quality good, that is, the average across markets. This equals 4.... ..."

"... In PAGE 9: ... The hot side to cell side differences are shown in Figure 14, and these have obviously improved greatly in the correlation. Overall quality of the model was evaluated in several ways, which are listed in Table2 . The primary measure of correlation was the change in temperature due to aerobraking.... ..."