title: Prediction of perceptible artifacts in JPEG 2000-compressed chest CT images using mathematical and perceptual quality metrics.
abstract: OBJECTIVE
  The objective of our study was to determine whether peak signal-to-noise ratio (PSNR) and a perceptual quality metric (High-Dynamic Range Visual Difference Predictor [HDR-VDP]) can predict the presence of perceptible artifacts in Joint Photographic Experts Group (JPEG) 2000-compressed chest CT images.
  
  
  METHODS
  One hundred chest CT images were compressed to 5:1, 8:1, 10:1, and 15:1. Five radiologists determined if the original and compressed images were identical (negative response) or different (positive response). The correlation between the results for each metric and the number of readers with positive responses was evaluated using Spearman's rank correlation test. Using the pooled readers' responses as the reference standard, we performed receiver operating characteristic (ROC) analysis to determine the cutoff values balancing sensitivity and specificity and yielding 100% sensitivity in each metric. These cutoff values were then used to estimate the visually lossless thresholds for the compressions for the 100 original images, and the accuracy of the estimates of two metrics was compared (McNemar test).
  
  
  RESULTS
  The correlation coefficients were -0.918 and 0.925 for PSNR and the HDR-VDP, respectively. The areas under the ROC curves for the two metrics were 0.983 and 0.984, respectively (p = 0.11). The PSNR and HDR-VDP accurately predicted the visually lossless threshold for 69% and 72% of the 100 images (p = 0.68), respectively, at the cutoff values balancing sensitivity and specificity and for 43% and 47% (p = 0.22), respectively, at the cutoff values reaching 100% sensitivity.
  
  
  CONCLUSIONS
  Both metrics are promising in predicting the perceptible compression artifacts and therefore can potentially be used to estimate the visually lossless threshold.
authors.affiliation: Department of Radiology, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do, Seoul 463-707, Korea.
authors.forename: Bohyoung
authors.initials: B
authors.lastname: Kim
authors.forename: Kyoung Ho
authors.initials: KH
authors.lastname: Lee
authors.forename: Kil Joong
authors.initials: KJ
authors.lastname: Kim
authors.forename: Rafal
authors.initials: R
authors.lastname: Mantiuk
authors.forename: Seokyung
authors.initials: S
authors.lastname: Hahn
authors.forename: Tae Jung
authors.initials: TJ
authors.lastname: Kim
authors.forename: Young Hoon
authors.initials: YH
authors.lastname: Kim
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016477:Artifacts; D044962:Data Compression / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011785:Quality Assurance, Health Care / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods* / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data