title: Prediction of perceptible artifacts in JPEG2000 compressed abdomen CT images using a perceptual image quality metric.
abstract: OBJECTIVE
  To test a perceptual quality metric (high-dynamic range visual difference predictor, HDR-VDP) in predicting perceptible artifacts in Joint Photographic Experts Group 2000 compressed thin- and thick-section abdomen computed tomography images.
  
  
  METHODS
  A total of 120 thin (0.67 mm) and corresponding thick (5 mm) sections were compressed to ratios from 4:1 to 15:1. Peak signal-to-noise ratio (PSNR), HDR-VDP results (paired t-tests), and five radiologists' pooled responses for the presence of artifacts (exact tests for paired proportions) were compared between the thin and thick sections. For three subsets of 120 thin- (subset A), 120 thick- (subset B), and 60 thin- and 60 thick-section compressed images (subset C), receiver operating curve analysis was performed to compare PSNR and HDR-VDP in predicting the radiologists' responses. Using the cutoff values where the sum of sensitivity and specificity was the maximum in subset C, visually lossless thresholds (VLTs) were estimated for the 240 original images and the estimation accuracy was compared (McNemar test).
  
  
  RESULTS
  Thin sections showed more artifacts in terms of PSNR, HDR-VDP, and radiologists' responses (p < .0001). HDR-VDP outperformed PSNR for subset C (area under the curve: 0.97 versus 0.93, p = 0.03), whereas they did not differ significantly for subset A or B. Using the cutoff values, PSNR and HDR-VDP predicted the VLT accurately for 124 (51.7%) and 183 (76.3%) images, respectively (p < .0001).
  
  
  CONCLUSIONS
  HDR-VDP can predict the perceptible compression artifacts, and therefore can be potentially used to estimate the VLT for such compressions.
authors.affiliation: Department of Radiology, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do, 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: Vasundhara
authors.initials: V
authors.lastname: Bajpai
authors.forename: Tae Jung
authors.initials: TJ
authors.lastname: Kim
authors.forename: Young Hoon
authors.initials: YH
authors.lastname: Kim
authors.forename: Chang Jin
authors.initials: CJ
authors.lastname: Yoon
authors.forename: Seokyung
authors.initials: S
authors.lastname: Hahn
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D016477:Artifacts; D044962:Data Compression / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*