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An adaptive image interpolation algorithm for image/video processing Author : Cheng-Soon Chuah, Jin-Jang Leou Source : Pattern Recognition 34 (2001) 2383-2393.

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Presentation on theme: "An adaptive image interpolation algorithm for image/video processing Author : Cheng-Soon Chuah, Jin-Jang Leou Source : Pattern Recognition 34 (2001) 2383-2393."— Presentation transcript:

1 An adaptive image interpolation algorithm for image/video processing Author : Cheng-Soon Chuah, Jin-Jang Leou Source : Pattern Recognition 34 (2001) 2383-2393 Speaker : Yi - Ping Lu Adviser : Ku-Yaw Chang

2 2007/12/17Digital Image Processing2 Outline ► ► Introduction ► ► Proposed adaptive image interpolation algorithm ► ► Simulation results ► ► Concluding remarks

3 2007/12/17Digital Image Processing3 Introduction ► ► Image interpolation is one of the key technologies in image/video processing ► ► A new adaptive image interpolation algorithm is proposed

4 2007/12/17Digital Image Processing4 Introduction ► ► The main purposes of image interpolation include:   Image expansion or zooming   Achieving higher compression ratio for image sequence compression

5 2007/12/17Digital Image Processing5 Introduction ► ► Based on the experimental results   PSNR (peak signal-to-noise ratio) in dB   Subjective measure of the quality of the interpolated images

6 2007/12/17Digital Image Processing6 Outline ► ► Introduction ► ► Proposed adaptive image interpolation algorithm ► ► Simulation results ► ► Concluding remarks

7 2007/12/17Digital Image Processing7 Adaptive image interpolation algorithm

8 2007/12/17Digital Image Processing8 Adaptive image interpolation algorithm ► ► A cubic B-spline function   The low-resolution image frame is first interpolated into a coarsely up-sampled image frame ► ► Pixel classification   Edge and non-edge pixels by analyzing the local image characteristics

9 2007/12/17Digital Image Processing9 Adaptive image interpolation algorithm ► ► 1-difference filter and 2-D edge sensitive filter   Iteratively improve the coarsely (cubic B-spline) interpolated image frame until the termination criterion is satisfied ► ► 1-difference filter   Recover high-frequency components lost within the decimation procedure ► ► 2-D edge sensitive filter   Reconstruct sharp edges and image details ► ► Blocking artifacts   Reduced by a smoothing operator

10 2007/12/17Digital Image Processing10 Adaptive image interpolation algorithm

11 2007/12/17Digital Image Processing11 Adaptive image interpolation algorithm ► ► 2.1. Proposed 1-difference filter   Increases or decreases the pixel value of a pixel to be interpolated   Define dif(r) as the absolute difference between L k and the mean of pixel values of all pixels in expansion block B k within the rth iteration

12 2007/12/17Digital Image Processing12 Adaptive image interpolation algorithm ► ► new pixel value   Each in expansion block B k within the (r+1)th iteration 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 B k are changed into their corresponding new pixel values   only if <

13 2007/12/17Digital Image Processing13 Adaptive image interpolation algorithm ► ► 2.2. Proposed 2-D edge-sensitive filter   Human eyes are more sensitive to high-contrast edges 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 compensate the over-smoothing effect

14 2007/12/17Digital Image Processing14 Adaptive image interpolation algorithm ► ► 2.2. Proposed 2-D edge-sensitive filter   Based on the direction and the relative position of a pixel within a 5*5 window   The Prewitt operators ► ► Determine the existences and orientations of edge pixels

15 2007/12/17Digital Image Processing15 Adaptive image interpolation algorithm

16 2007/12/17Digital Image Processing16 Adaptive image interpolation algorithm

17 2007/12/17Digital Image Processing17 Adaptive image interpolation algorithm

18 2007/12/17Digital Image Processing18 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

19 2007/12/17Digital Image Processing19 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

20 2007/12/17Digital Image Processing20 Outline ► ► Introduction ► ► Proposed adaptive image interpolation algorithm ► ► Simulation results ► ► Concluding remarks

21 2007/12/17Digital Image Processing21 Simulation results ► ► The proposed adaptive image interpolation 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

22 2007/12/17Digital Image Processing22 Simulation results ► ► Three existing approaches for comparison   Zero-order (Zero-O)   Bilinear interpolation (Bilinear)   Cubic B-spline interpolation (Cubic)

23 2007/12/17Digital Image Processing23 Simulation results

24 2007/12/17Digital Image Processing24 Simulation results

25 2007/12/17Digital Image Processing25

26 2007/12/17Digital Image Processing26

27 2007/12/17Digital Image Processing27

28 2007/12/17Digital Image Processing28

29 2007/12/17Digital Image Processing29

30 2007/12/17Digital Image Processing30

31 2007/12/17Digital Image Processing31

32 2007/12/17Digital Image Processing32 Outline ► ► Introduction ► ► Proposed adaptive image interpolation algorithm ► ► Simulation results ► ► Concluding remarks

33 2007/12/17Digital Image Processing33 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

34 2007/12/17Digital Image Processing34 Concluding remarks ► ► Based on the experimental results   PSNR in dB   Subjective measure of the quality of the interpolated images   The proposed approach are better than that by the three existing interpolation approaches ► ► This shows the feasibility of the proposed approach

35 The End

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