stereo image compression

8/7/2019 Stereo Image Compression

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STEREO IMAGECOMPRESSION



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



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.



APPLICATIONS

Satellite imaging systems

virtual reality, robot navigation and many other

aspects of production, security, defense, explorationand entertainment

Medical scanning purposes



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



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.



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.



JPEG



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.



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.



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.



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).



SSD=



Left Image Uncompressed

Format: png

RESULTS



Left image at Receiver



Compression ratio obtained

Size of left image=324kB

Size of compressed left image= 22.6021kB

C R = 324/22.6021=14.3350



Original Right Image



Recovered Right Image



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



Obtained results

We got good recovery of image by sum of

squares(ssd) approach.

There are slight distortions in the images shown



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



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



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 .

stereo image compression

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