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8/7/2019 Stereo Image Compression
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STEREO IMAGECOMPRESSION
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Why Do We Need Compression?
Requirements may outstrip the anticipated increase of storage space and bandwidth
For data storage and data transmission
� DVD� Video conference
� Printer
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What are stereo images
3D stereo images are acquired by simulatinghuman·s eyesight effect upon observing objects through two horizontally separated perspectives.
Correspondingly, two frames are resulted for one 3-D image, labelled as left frame and right frame. If these two frames are to be transmitted with the idea of reconstructing the 3D image at the receiver
end, we would need double the bandwidth required for monocular image transmission. Therefore, datacompression is necessary.
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APPLICATIONS
Satellite imaging systems
virtual reality, robot navigation and many other
aspects of production, security, defense, explorationand entertainment
Medical scanning purposes
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A number of approaches to the compression of
stereo pairs of images were proposed and
evaluated under various criteria. The normal
approach was to compress one image independently, then to compress the second image
using the knowledge about the first image
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A two-pronged approach was taken:
� Compressing the left image. The first step was tocompress a reference image as well as possible, given
no other information. This is essentially a standard image compression problem. The left image was chosenas the reference image in this project.
� Compressing the right image.Once a reference image has been optimally compressed, the right image can be
compressed given full knowledge of the left image. Again, different requirements yield differentapproaches to this problem.
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Left Image Compression
JPEG Compression :
1. (Optionally) If the color is represented in RGB mode, translate it to YCbCr.
2. Divide the file into 8 X 8 blocks. 3. Transform the pixel information from the spatial
domain to the frequency domain with the Discrete Cosine Transform.
4. Quantize the resulting values by dividing each coefficient by an integer value and rounding off to the
nearest integer. 5. Look at the resulting coefficients in a zigzag order.
Do a run-length encoding of the coefficients ordered inthis manner. Follow by Huffman coding.
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JPEG
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Obstacles during our implementation
Problem with end of bit in AC coefficients
Which is analysed and a unique end bit was used.
Getting the logic for standard Huffman Table used in standard Jpeg for DC and AC coefficients.
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Compression of the right image
Find the disparity vector of right image and left
image.
Use Huffman codes to send the Disparity vector.
Left image is compressed and transmitted and all
the information contained there in may be exploited
to minimise the rate of the right image.
3-D information can be computed by binoculardisparity and triangulation.
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Stereo Disparity
The distance between the two corresponding points
when the two images are aligned one on top of the
other is called the disparity.
Disparity estimation is used to exploit the similarity
between two images in order to reduce the bit rate.
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Disparity
Once we find the disparity
Disparity will be transmitted .
Disparity can be can be found out by differentways
In our case we used sum of squares(SSD) approach.
Other approaches include Sum of Absolute
Differences (SAD), Zero-mean Sum of Absolute Differences (ZSAD).
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SSD=
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Left Image Uncompressed
Format: png
RESULTS
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Left image at Receiver
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Compression ratio obtained
Size of left image=324kB
Size of compressed left image= 22.6021kB
C R = 324/22.6021=14.3350
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Original Right Image
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Recovered Right Image
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Final Compression Ratio
Size of the left Image=324kB
Size of the right Image=324kB
Size
of the
compr
essed le
ft Image=22
.6021kB
Size of the compressed disparity map=14.5886kB
Compression ratio =
(324+324)/(22.6021+14.5886)
=17.423710
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Obtained results
We got good recovery of image by sum of
squares(ssd) approach.
There are slight distortions in the images shown
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Receiver
At the receiver the reconstruction of right image will
be done by adding disparity to the left image .
Once we get the right image , 3-D reconstruction
will be done by these two images based on certain
techniques
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Conclusion
The stereo correspondence problem remains an
active area for research.
More and more modern applications demand not
only accuracy but real-time operation as well.
Simpler local algorithms that outperform any other
algorithms are required for the hardware
implementation
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Future work
3-D reconstruction Techniques
Single View Metrology
Mul
tiw
ay
Graph
Cut
Slanted Surfaces
We need to study about those techniques in detail
and will be using the most suitable of those methods
to reconstruct the 3d image .