sharpness estimation for document and scene imagesjayant/icpr_sharpness.pdf · narvekar and l....
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Sharpness Estimation for Document and Scene Images
Jayant Kumar, PhD Student, UMD
Francine Chen, FXPAL
David Doermann, UMD
Sharpness Estimation
• Measure of best in-focus image
• Measure of how sharp details (like edge) are preserved
> >
Automatic creation of photo-books
Select the least blurry of several similar images
Images can be scaled so that blurrier images are smaller (hence look nicer!)
Limitations of Previous Work
• Assumption on good Edge-detection
• Offset in Gradient Computation [Batten 2000]
• Poor time-performance (DCT Coefficients in blocks [Brandao et
al. 2008, JNB, CPBD]
• Blur-width quantized as number of pixels – Just-Noticeable-Blur (JNB) [Ferzli et al. 2009], Cumulative probability of blur detection (CPBD)
[Narvekar and Karam 2011]
• Dependence on image content – Qmetric [Zhu et al. 2010]
Sharpness as Edge-width?
Many previous approaches rely on edge-width for sharpness estimation1,2
1. R. Ferzli and L. Karam. A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB). IEEE Transactions on Image Processing, 2009.
2. N. Narvekar and L. Karam. A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Transactions on Image Processing, 2011.
These measures are somewhat coarse for document images composed of sharp edges with high contrast and frequent transition between back-ground/foreground
Ideal Sharpness Measure
• Correlates well with human perceived quality of sharpness
• Works for images from different domains: scenes, objects, and documents
• Images of same scene/document as well as across images of different scenes/documents
• Indicative of OCR accuracy for document images
• Fast enough to compute as a pre-processing step in many applications
Proposed Sharpness Measure
∑|∆DOM|
∑|∆I| > T sharp
blur ∑|∆DOM|
∑|∆I| < T
Rx = NsharpX
NedgeX
Ry =
NsharpY
NedgeY
Rx + Ry 2 2 √ ( ) SI
=
Median-filtered image
∆DOM model
∆DOMx(i, j) Im(i+2, j) Im(i,j) [ ] - Im(i, j) Im(i, j-2) [ ] - - =
Im = Median filtered image Median filtering smooth variations due to noise while preserving edges
Underlying edge is non-linear and can be modeled by a non-parametric model, such as the difference of differences that can model changes in the direction of a line.
From this model we derive a measure that captures whether the slope changes quickly, a characteristic of sharp edges.
edge-width
Datasets
• Document Image Sharpness Dataset (DocSharp)
– Pages taken from magazines, technical articles etc.
– 27 sets of 5 images each (135 images)
– Mechanical Turk for pair-wise comparisons
– 20-25 judgments for each pair
• Scene Images
– LIVE dataset1: Gaussian-blurred subset of 174 images
– CSIQ dataset2: Gaussian-blurred subset of 150 images
1: H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik. Live image quality assessment database release 2. In http://live.ece.utexas.edu/research/quality, 2006.
2: E. C. Larson and D. M. Chandler. Most apparent distortion: full reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19(1), 2010.
Evaluation
• Spearman Rank Correlation between ∆DOM sharpness measure and perceived sharpness (MOS/DMOS* scores and ranked pair-wise judgments)
• Accuracy on pair-wise judgments
• Time performance
• Comparisons with JNB, CPBD and Q
*MOS: Mean opinion score
*DMOS: Difference in mean opinion score
Spearman Correlation
Paired t-test for DocSharp:
∆DOM is the only measure that performs well on both document and scene images
Computation Time
JNB CPBD Q ∆DOM
DocSharp 33.63 55.46 12.36 3.91
LIVE 2.25 1.05 0.88 0.27
CSIQ 1.71 0.68 0.66 0.26
*Tested on Android for real-time sharpness on preview frames
Comparison with CPBD and JNB
JNB CPBD ΔDOM
(a) 0.84 0.71 0.78
(b) 1.03 0.59 0.174
(c) 1.35 0.76 0.17
(a)
(b)
(c)
Comparison with other methods
JNB CPBD Q Proposed
Human Perception of Scene Images
Human Perception of Document Images
Images of same object/document
Images of different objects/documents
Computation time