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An adaptive image interpolation algorithm
for image/video processing
Author : Cheng-Soon Chuah, Jin-Jang LeouSource : Pattern Recognition 34 (2001) 2383-2393Speaker : Yi - Ping Lu Adviser : Ku-Yaw Chang
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2007/12/17 Digital Image Processing 2
OutlineOutline►Introduction►Proposed adaptive image interpolation al
gorithm►Simulation results►Concluding remarks
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2007/12/17 Digital Image Processing 3
Introduction►Image interpolation is one of the key tech
nologies in image/video processing►A new adaptive image interpolation algori
thm is proposed
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2007/12/17 Digital Image Processing 4
Introduction►The main purposes of image interpolation
include: Image expansion or zooming Achieving higher compression ratio for image
sequence compression
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2007/12/17 Digital Image Processing 5
Introduction►Based on the experimental results
PSNR (peak signal-to-noise ratio) in dB Subjective measure of the quality of the inter
polated images
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2007/12/17 Digital Image Processing 6
OutlineOutline►Introduction►Proposed adaptive image interpolation al
gorithm►Simulation results►Concluding remarks
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2007/12/17 Digital Image Processing 7
Adaptive image interpolation algorithm
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2007/12/17 Digital Image Processing 8
Adaptive image interpolation algorithm
►A cubic B-spline function The low-resolution image frame is first interp
olated into a coarsely up-sampled image frame
►Pixel classification Edge and non-edge pixels by analyzing the loc
al image characteristics
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2007/12/17 Digital Image Processing 9
Adaptive image interpolation algorithm
► 1-difference filter and 2-D edge sensitive filter Iteratively improve the coarsely (cubic B-spline) inter
polated image frame until the termination criterion is satisfied
► 1-difference filter Recover high-frequency components lost within the d
ecimation procedure ► 2-D edge sensitive filter
Reconstruct sharp edges and image details►Blocking artifacts
Reduced by a smoothing operator
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2007/12/17 Digital Image Processing 10
Adaptive image interpolation algorithm
image resolution-low in the pixel a of valuePixel Lk:blockExpansion Bk:
z*z Size z:iterationrth r :
operation Rounding )( Round :‧kji, Bblock expantion whithin j)(i, valuepixel The f :
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2007/12/17 Digital Image Processing 11
Adaptive image interpolation algorithm
►2.1. Proposed 1-difference filter Increases or decreases the pixel value of a pix
el to be interpolated Define dif(r) as the absolute difference betwee
n Lk and the mean of pixel values of all pixels in expansion block Bk within the rth iteration
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2007/12/17 Digital Image Processing 12
Adaptive image interpolation algorithm
► new pixel value Each in expansion block Bk within the (r+1)th iterati
on is then pre-computed by (3) Mean
►Pixel values of all the 9 pixels in the 3*3 window centered at pixel (i, j)
►All the pixel values in expansion block Bk are changed into their corresponding new pixel values
only if <
1)(rji,f
(r)ji,f
1)(rji,f
1)(rdif (r)dif
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2007/12/17 Digital Image Processing 13
Adaptive image interpolation algorithm
► 2.2. Proposed 2-D edge-sensitive filter Human eyes are more sensitive to high-contrast edge
s than smooth areas Applying bilinear and cubic B-spline functions
►Smooths image data►Blurs the discontinuities
To cope with this problem►An edge-sensitive filter is employed in this study to compens
ate the over-smoothing effect
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2007/12/17 Digital Image Processing 14
Adaptive image interpolation algorithm
► 2.2. Proposed 2-D edge-sensitive filter Based on the direction and the relative position of a p
ixel within a 5*5 window The Prewitt operators
►Determine the existences and orientations of edge pixels
)(,rjif
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2007/12/17 Digital Image Processing 15
Adaptive image interpolation algorithm
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2007/12/17 Digital Image Processing 16
Adaptive image interpolation algorithm
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2007/12/17 Digital Image Processing 17
Adaptive image interpolation algorithm
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2007/12/17 Digital Image Processing 18
Adaptive image interpolation algorithm
►2.3. Postprocessing: Blocking Artifacts Reduction Because the proposed approach is performed
in a block-by-block fashion, blocking artifacts are inevitable
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2007/12/17 Digital Image Processing 19
Adaptive image interpolation algorithm
► To measure the seriousness of blocking artifacts An expansion block is defined as the mean of the absolute pixel
value difference between the expansion block and its four neighboring expansion blocks
y visibilitartifacts blocking The : V ji,
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2007/12/17 Digital Image Processing 20
OutlineOutline►Introduction►Proposed adaptive image interpolation al
gorithm►Simulation results►Concluding remarks
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2007/12/17 Digital Image Processing 21
Simulation results►The proposed adaptive image interpolatio
n algorithm has been implemented On a Pentium 133 PC C programming language Three test image
►“Salesman”, “Football”, and “Flower Garden”
►Three different decimation/expansion ratios are employed z = 2, 3, 4
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2007/12/17 Digital Image Processing 22
Simulation results►Three existing approaches for comparison
Zero-order (Zero-O) Bilinear interpolation (Bilinear) Cubic B-spline interpolation (Cubic)
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2007/12/17 Digital Image Processing 23
Simulation results
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2007/12/17 Digital Image Processing 24
Simulation results
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2007/12/17 Digital Image Processing 25
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2007/12/17 Digital Image Processing 26
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2007/12/17 Digital Image Processing 27
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2007/12/17 Digital Image Processing 28
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2007/12/17 Digital Image Processing 29
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2007/12/17 Digital Image Processing 30
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2007/12/17 Digital Image Processing 31
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2007/12/17 Digital Image Processing 32
OutlineOutline►Introduction►Proposed adaptive image interpolation al
gorithm►Simulation results►Concluding remarks
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2007/12/17 Digital Image Processing 33
Concluding remarks►A new adaptive image interpolation algorithm is
proposed. ► In the proposed approach
A low-resolution image frame is first interpolated into a coarsely interpolated image frame using the cubic B-spline function
All the pixels in each coarsely interpolated image frame are then classified into non-edge and edge pixels
1-difference filter 2-D edge-sensitive filter Blocking artifacts-reducing
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2007/12/17 Digital Image Processing 34
Concluding remarks►Based on the experimental results
PSNR in dB Subjective measure of the quality of the inter
polated images The proposed approach are better than that b
y the three existing interpolation approaches ►This shows the feasibility of the proposed
approach
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The End