1 preprocessing for jpeg compression elad davidson & lilach schwartz project supervisor: ari...
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Preprocessing for JPEG Preprocessing for JPEG CompressionCompression
Elad Davidson & Lilach Schwartz
Project Supervisor: Ari Shenhar
SPRING 2000
TECHNION - ISRAEL INSTITUTE of TECHNOLOGY
Department of Electrical Engineering
The Vision Research and Image Science Laboratory
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Presentation OverviewPresentation Overview
Project goalsTheoretical BackgroundPossible SolutionsThe algorithmResults & Conclusions
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Project GoalsProject Goals
Compression– JPEG, GIF– Quality vs. File’s size
Object Segmentation– Secondary goal: Separate the objects in the picture.
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Theoretical BackgroundTheoretical Background
JPEG CompressionGIF CompressionMathematical Morphology– Dilation, Erosion, Opening, Closing
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JPEGJPEG
Lossy Useful for nature pictures, photos, and smooth
pictures. Compression ratio:
1:5 for gray scale image,
1:10 – 1:20 for color image
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JPEGJPEG Cont.Cont. : :
Method: separate the picture to small blocks (8x8) DCT conversion LPF Uniform quantizer (Max – Loyd) Hophman coding (lossless) Creating an header with the information needed
for the image decompressing
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GIFGIF
Compression algorithm for colored or gray scale pictures
Lossless, compressing ratio – 1:4 to 1:10 Method: scanning the picture, searching for a
sequence of similar pixels, insert the sequence into a translate table (LUT) and use this table any time such a sequence is encountered
Useful for pictures with only a few gray levels or images with objects that have sharp edges.
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Mathematical MorphologyMathematical Morphology
Erosion– Purpose: remove pixel with weak link– Method: subtracting the structure element from each
pixel’s area and replacing the pixel’s value with the minimum value of this area.
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Mathematical MorphologyMathematical Morphology Cont.Cont.
Dilation– Purpose: extending an object– Method: like erosion but the structure is added and the
pixel is replaced with the maximum value in the area
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Mathematical MorphologyMathematical Morphology Cont.Cont.
Opening – Purpose: remove small objects, sharpen edges and eliminate
noise.
– Method: erosion and dilation.
* before vs. after
Closing– Purpose: combine objects that have been separated
– Method: dilation and erosion
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Possible SolutionsPossible Solutions
Problem: Object Identification- Edge detection - identify the edge and separate it’s inner
part.
1. Morphological operation -subtracting the picture after dilation from the original picture
2. CANNY Algorithm – an edge detection algorithm
** Problems – the direction of the edge and what is the inner part of the object are hard to define.
- Histogram - using local maxima and separating all it’s neighboring pixels.
** Problems - one maximum can be hidden in another maximum and the
object won’t be separated.
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Possible Solutions – Cont.Possible Solutions – Cont.
Problem: Representative gray-scale level– Max-Loyd quantizer – iterative algorithm that
calculates the center of mass.
** Problems – not efficient for one level quantizing because it can convergent to a local minima
– Scalar quantizer – cut LSBits from the pixels.
** Problems – no relation to the actual data of the picture.
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The AlgorithmThe Algorithm
Input – gray scale image
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The Algorithm Cont.The Algorithm Cont.
Objects’ segmentation– Opening - smooth
the edges and reduce noise.
– Raster scan - object segmentation. Each object gets its representative value.
– The result is the ‘cluster’ matrix
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The Algorithm Cont.The Algorithm Cont.
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The Algorithm Cont.The Algorithm Cont.
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The Algorithm Cont.The Algorithm Cont.
Find the representative gray scale level Objects’ uniting Image subtracting - the united picture is
subtracted from original image JPEG compression to the image after the
subtraction GIF compression to the ‘united’ picture
Files’ size calculations - GIF & JPEG
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The Algorithm Cont.The Algorithm Cont.
Decompressing Adding the JPEG
picture to the GIF picture
Visual comparison, MSE & PSNR calculations.
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Results & ConclusionsResults & Conclusions
Visual Comparison MSE – Mean Square Error PSNR – Peak Signal to Noise Ratio (dB) File’s Size
MSE =
PSNR = 20 * log10 (255 / sqrt(MSE))
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ResultsResults
Orig. JPEG
JPEG +GIF
GIF size
JPEG size
PSNR MSE name
4.89 1.88 1.25 0.63 33.48 29.13 Gray_pk
5.12 3.37 1.62 1.75 25.77 172.04 Eight
4.29 3.48 1.53 1.95 31.27 48.43 Coins
7.83 1.859 1.36 0.499 72.44 0.0037 Test
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ConclusionsConclusions
Visual Comparison –– Good results for pictures with a large
difference between the objects and the background and pictures with sharp edges
–MSE/PSNR: the values were in the higher quality part of the value range
– File size: definitely smaller, about 2/3 of the size of the original picture
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Coins ExampleCoins Example
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Example – Cont.Example – Cont.
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Example – Cont.Example – Cont.