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Image deblocking using local segmentation

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An image which is divided into 8x8 blocks has 64 possible sub-band images ... Combine multiple slightly different images to form one higher quality image ... – PowerPoint PPT presentation

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Title: Image deblocking using local segmentation


1
Image deblocking using local segmentation
  • By Mirsad Makalic
  • Supervisor Dr. Peter Tischer

2
Presentation Outline
  • An introduction to JPEG
  • Lossy image compression
  • Discrete Cosine Transform (DCT) and quantization
  • Local segmentation and prior research
  • Measuring image quality
  • Deblocking Filter
  • Super-Resolution Filter
  • Conclusion

3
Lossy image compression
  • JPEG is the most common lossy image compression
    format
  • Easy to implement
  • Good quality with high compression ratios
  • Block based transform approach
  • Divide image into 8x8 blocks
  • Perform discrete cosine transform (DCT) on each
    8x8 block
  • Quantize the DCT coefficients

4
Discrete Cosine Transform
DC coefficient
  • Transform 8x8 block of pixels into a set of
    weighted basis functions (DCT coefficients)

AC coefficients
5
DCT - Subbands
  • An image consisting of just one coefficient from
    each 8x8 block is called the sub-band image
  • An image which is divided into 8x8 blocks has 64
    possible sub-band images

An 8x8 image split into 2x2 blocks has 4 subbands
6
Quantization
  • Exploit visual redundancy
  • Quantization is a many-to-one mapping
  • Divide each DCT coefficient by a value and round
    to the nearest integer
  • The decoder makes a guess from a range of values
    (pick midpoint by default)

5 6 7 8 9 10 11 12 13 14
Quantizer 10
1
7
Quantization
  • Coarse quantization introduces artifacts into
    reconstructed image
  • DCT coefficients are reconstructed inaccurately
  • Most visually distracting artifact is blockiness

JPEG compressed image at quality 10 PSNR 30.41 dB
8
Deblocking Techniques
  • Three approaches in the literature
  • Filter the reconstructed pixel values
  • Attempt to reconstruct DCT coefficients more
    accurately
  • A hybrid approach

9
Local Segmentation
  • Most deblocking filters introduce excessive
    blurring
  • Destroys the structure of the image
  • Edges lose their sharpness
  • Local segmentation takes into account the
    structure of the image

10
Local Segmentation
  • Divide a mask of pixels into N segments and
    filter each segment independently

Average of whole mask 43.22, average of yellow
segment 20.8
  • Two questions
  • How many segments do we use?
  • How do we segment a mask of pixels?

11
Prior Research
  • Lukasz Kizewski, BSE (hons) 2004
  • DC subband approach
  • Filter using a mask of DC subbands
  • How do we segment a mask of pixels?
  • Segment the pixel mask using thresholding
  • How many segments do we use?
  • Do-No-Harm heuristic

12
Prior Research
  • Do-No-Harm heuristic
  • Try a 1-segment model (average of the whole mask)
  • If filtered value is implausible reject and try a
    2-segment model
  • A plausible value is one which falls inside the
    quantization range midpoint /- ½ Quantum
  • Try a 2-segment model
  • If still implausible then dont filter

13
Prior Research
  • Results

(a) Unfiltered image
(a) Filtered image
14
Room for improvement
  • No objective measure used to test the
    effectiveness of the filter
  • Difficult to make comparisons
  • Difficult to rate changes in filter
  • Works only on DC subband
  • AC subbands contain edge and texture information
  • A very simple local segmentation method

15
Measuring image quality
  • Peak-signal-to-noise-ratio
  • Most commonly used metric
  • Does not necessarily reflect the subjective
    visual quality
  • Generalized Block-Edge Impairment Metric (GBIM)
    H.R. Wu, M. Yuen

16
Measuring image quality - GBIM
  • Measures the quality of DCT encoded images
  • Assume that what happens inside a block is the
    same as what happens across blocks
  • Take absolute mean difference of pixels inside a
    block (vertical/horizontal)
  • Take absolute mean difference of pixels across
    blocks (vertical/horizontal)
  • Compare them, if the two differ greatly than it
    is a sign of blockiness

2x2 block example with vertical blockiness
17
A new deblocking filter
  • Filter all coefficients
  • Treat each 8x8 block as a 64 element vector where
    each value in the vector is one of the DCT
    coefficients
  • Local segmentation no longer as simple (need to
    segment masks of vectors)
  • K-Means or K-Nearest Neighbours
  • DNH needs to work on vectors

18
A new deblocking filter
  • Basic structure of filter is same
  • Create an NxN mask where each item is a 64
    element DCT coefficient vector
  • Segment mask using K-Means or K-Nearest
    Neighbours segmentation
  • Check if segmentation produces valid result using
    DNH, if not, try different segment
  • If no segmentation produces valid result, leave
    alone

19
K-Means
  • Start with one segment (average of mask)
  • If segmentation is invalid, increase number of
    segments by one until a maximum number of
    segments is reached
  • Try largest change first

20
K-Nearest Neighbours
  • Set the number of nearest neighbours to find as
    the number of items in the entire mask
  • Keep decreasing by segment size by one until a
    valid segment is found

21
Vector DNH
  • Center DNH
  • Compare the filtered vector against only the
    center vector in the mask
  • Strict Segment DNH
  • Compare the filtered vector against all the
    vectors in the segment the center vector is in
  • Lesser Strict Segment DNH
  • Same as strict segment DNH with some error
    tolerance

22
Results
  • Best found parameters for the filter
  • 3x3 vector mask (covers 24x24 pixels)
  • K-Nearest Neighbour segmentation
  • Lesser strict segment DNH with 1 error tolerance
  • Tested
  • 5x5 mask, K-Means, Center DNH, Strict DNH etc.
  • Many variations of the filter parameters

23
Results
(a) JPEG compressed image
(b) Filtered image
  • An improvement of 0.07 dB in PSNR and a reduction
    of 0.62 in GBIM

24
Results
(a) JPEG compressed image
(b) Filtered image
  • An improvement of 0.07 dB in PSNR and a reduction
    of 0.55 in GBIM

25
Other uses for local segmentation
  • Super-resolution
  • Combine multiple slightly different images to
    form one higher quality image
  • Can we extract more information out of a single
    image?
  • Neighbouring pixels are similar and share
    information
  • Use local segmentation

26
Super-resolution filter
  • Very similar to the deblocking filter
  • Instead of using a 64 element vector for each DCT
    coefficient, use one element vector containing
    each pixel value in the mask
  • How to test if it works?
  • Convert 8 bit image to 4 bits and attempt to
    reconstruct back an 8 bit image

27
Results
  • Best found parameters for filter
  • 3x3 mask
  • K-Means segmentation with up to three segments
  • Center DNH
  • More than one iteration of the filter can further
    improve PSNR

28
Results
(a) An image rounded to 4 bits per pixel
(a) Filtered image (8 bits per pixel)
  • An improvement of 1.38 dB in PSNR

29
Results
(a) An image rounded to 4 bits per pixel
(a) Filtered image (8 bits per pixel)
  • An improvement of 1.32 dB in PSNR

30
Conclusion
  • Filtering AC subbands is difficult because most
    have been quantized to zero or have very large
    quantization ranges
  • Most improvements in image quality are from the
    stricter DNH and better local segmentation
    techniques
  • The increase in computational complexity may not
    be worth the increase in image quality

31
Conclusion
  • Super-resolution filter shows a lot of promise
  • Large increase in PSNR and image quality
  • A different DNH heuristic may work better with
    the super-resolution filter

32
Future Research
  • Allow more variation in pixels for the strict DNH
  • Assume local mask is linear and not constant
  • Try different segmentation techniques
  • Region growing
  • Further investigate iterative filtering

33
The End
  • Questions?
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