1 / 21

Ariawan Suwendi Prof. Jan P. Allebach Purdue University - West Lafayette, IN

Nearest-neighbor and Bilinear Resampling Factor Estimation to Detect Blockiness or Blurriness of an Image*. Ariawan Suwendi Prof. Jan P. Allebach Purdue University - West Lafayette, IN. *Research supported by the Hewlett-Packard Company. Outline. Introduction

amal-herring
Télécharger la présentation

Ariawan Suwendi Prof. Jan P. Allebach Purdue University - West Lafayette, IN

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Nearest-neighbor and Bilinear Resampling Factor Estimation to Detect Blockiness or Blurriness of an Image* Ariawan Suwendi Prof. Jan P. Allebach Purdue University - West Lafayette, IN *Research supported by the Hewlett-Packard Company

  2. Outline • Introduction • 1-D Nearest-neighbor and bilinear interpolation • The basis for interpolation detection (RF>1) • Step-by-step illustration of the resampling factor estimation algorithm • Robustness evaluation • Conclusions

  3. Original Low-Res Image NN interpolation Bilinear interpolation Introduction • Nearest-neighbor and bilinear interpolation are widely used • Popescu and Farid (IEEE T-SP, 2005): Detect resampled images by analyzing statistical correlations • Not able to detect the resampling amount • Ineffective to some common post-processings

  4. Introduction (cont.) • How to detect and estimate resampling factor (RF) for nearest-neighbor and bilinear interpolation • Since both interpolations are separable, most of the things will be explained in 1-D space

  5. 1-D Nearest-neighbor and bilinear interpolation • Rational resampling factor ( )

  6. peak interval Basis for nearest-neighbor interpolation detection (RF=5) Nearest-neighbor interpolated image • Periodic peaks in first-order difference image • Peak intervals contain information about the RF applied Periodic peaks in |First-order difference|

  7. Basis for bilinear interpolation detection (RF=5) Bilinear interpolated image Periodic peaks in |Second-order difference| peak interval First-order difference

  8. Basis for interpolation detection • In nearest-neighbor interpolated images, the first-order difference image should contain peaks with peak intervals equal floor(RF) or ceil(RF) • In bilinear interpolated images, the second-order difference image should contain peaks with peak intervals equal floor(RF) or ceil(RF) • Resampling factor RF can be estimated as the average of the detected peak intervals • Smooth regions in the difference image do not provide a reliable reading of peak intervals and, hence, should be ignored

  9. Model for peak intervals in bilinear interpolation (RF=2.5) Uninterpolated pixel values: Interpolated pixel values: • Assume that the increment term (Δn) is uniformly distributed in [-255,255] • Periodic second-order difference coefficient sequence: 0,1,1,0,2,0,1,1,0,2,0,1,1,0,2,… one period

  10. Peak detection (RF=2.5) • Assignment of peak location for 4 possible peaks: • Peak intervals for the second-order diff. coeff. sequence: • 0,1,1,0,2, 0,1,1,0,2, 0,1,1,0,2,0,… • RFest = Average of detected peak intervals = 2.5 Second-order difference Legend Peak A Peak B1 Peak B2 Peak B3 Peak location Interpolated pixel 3 2 3 2 3

  11. Step-by-step illustration of vertical RF estimation for bilinear interpolation (RF=4.5) ? Image Interpolate by RF=4.5 JPEG-compression 90% quality Bilinear RF Estimation algorithm RFest

  12. Step-by-step illustration (cont.) • Step 1: Compute luminance plane using YCbCr model • Step 2: Compute |second difference image| • Step 3: Scale the difference image to [0,255] • Step 4: Apply the horizontal Sobel edge detection filter

  13. Step-by-step illustration (cont.) • Step 5: Dilate the edge map to get a mask • Smooth regions do not provide a reliable reading of peak intervals

  14. RFest=4.46 Step-by-step illustration (cont.) • Step 6: Mask the difference image, project, and average to get a 1-D projection array • Step 7: Detect peaks and measure peak intervals • Step 8: Use histogram to extract resampling factor • Step 9: Detect possible false alarms Histogram of detected peak intervals

  15. Robustness evaluation(30 Images, 26 resampling factors)

  16. Test results(NN) • Tolerance for estimation accuracy: 15% • Reliable estimation for RF>1.5

  17. Test results(NN with post-processing) • Reliable estimation for RF>2

  18. Test results(BI) • Reliable estimation for RF>2

  19. Test results(BI with post-processing) • For (BI, JPEG): Reliable estimation for RF>2

  20. Conclusions • The NN resampling factor estimation algorithm works well for RF>2 • It can withstand significant post-processing • The bilinear resampling factor estimation algorithm works well for RF>2 except in sharpening and watermarking tests • It can only withstand mild post-processing • One weakness is that bilinear interpolation with 1<RF<2 tends to be overestimated with 2<RFest≤3

  21. Thank you for listening

More Related