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Lossless Compression of VQ Index
with Search-Order Coding
授課老師：王立洋
老師
製作學生：M9535204
蔡鐘葳
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Outline
▓ Introduction
▓ Search-Order Coding Algorithm
▓ Encoding Algorithm
▓ Simulation and Results
▓ Reference
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Abstract
A new lossless algorithm that exploits the interblock
correlation in the index domain
Compare the current index with previous indices in a
predefined search path
Then send the corresponding search order to the
decoder
The new algorithm achieves significant reduction of
bit rates without introducing extra coding distortion
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Introduction (1/2)
Fig. 1 shows the index map of a 64 x 64 image
quantized by a codebook with 256 codevectors
Each point in the index map corresponds to a 4 x 4
block of pixels
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Introduction (2/2)
Since image blocks are highly correlated, many
blocks in an image correspond to the same index
Now the problem is how to find and encode these
points in an efficient way
The bit rate can be further reduced without extra loss
of information
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Search-Order Coding Algorithm (1/9)
An efficient algorithm called search-order coding is
presented to achieve this goal
The main idea is to replace the current index by the
previous indices that are efficiently represented by
search orders
Here, a regular search scheme is presented to find the
points having the same index value
Each point in the index map is employed as a search
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center for each search
For the example in Fig. 1
The point (x, y) = (1, 1) is selected as a search center
for the first search
Then the point (2, 1) for the second search, and so on
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Search-Order Coding Algorithm (2/9)
In general, all points in the index map except the
search center are candidate points for matching
However, this will result in a coding delay, since VQ
indices are obtained in a raster scan order
Therefore, we choose the semicausal neighbors of the
search center as search points (SP’s) as illustrated by
the solid-boundary blocks in Fig. 2
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Fig. 2
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Search-Order Coding Algorithm (3/9)
To begin a search, we should select a starting search
point (SSP) from the SP’s
The SSP is determined by the four directions defined
in Fig. 3
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Search-Order Coding Algorithm (4/9)
After a search center and an SSP have been
determined, we search the four SP’s of the first level
in a clockwise way
If no SP’s are matched with the search center
search the SP’s of the second level in a similar way, as
illustrated in Fig. 2
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Search-Order Coding Algorithm (5/9)
If a matched point is found
the search-order code of the matched point is sent to the
decoder
Otherwise, the original index of the search center is
transmitted
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Search-Order Coding Algorithm (6/9)
There may exist some repetition points in the
neighborhood of the search center
For the example of Fig. 4, the points (2, 2), (3, 2), and
(1, 3) are repetition points
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Fig. 4
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Search-Order Coding Algorithm (7/9)
Therefore, before matching, we should determine
whether an SP is a repetition point
If it is a repetition point
the matching operation for this point is neglected
The exclusion of repetition points will increase the
possibility for finding matched points
Because the number of bits, n, assigned to searchorder code is limited
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Search-Order Coding Algorithm (8/9)
For example, if a 2-b search-order code is used
we have only four SP’s to be compared without
excluding the repetition points
whereas, we have more than four SP’s when the
repetition points are excluded
Consequently, it will increase the coding efficiency
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Search-Order Coding Algorithm (9/9)
Fig. 4 illustrates the search-order coding that excludes
the repetition points
the code words are given only to the nonrepetition
SP’s
It is apparent that we cannot find the matched point
(1, 2) if the repetition points are not excluded
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Encoding Algorithm Step1
Determine the number of bits n for encoding the
search order
Select the starting search point (SSP) according to the
search directions defined in Figs. 2 and 3
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Encoding Algorithm Step2
Input an index and use it as a search center
The index can be generated from any memoryless VQ
encoder
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Encoding Algorithm Step3
Clear the n-b search-order counter
Then search clockwise the nonrepetition search points
(NRSP) associated with the search center, starting
from the SSP
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Encoding Algorithm Step4
Check whether the NRSP is matched with the search
center
If it is true, the search-order code (the value of the
search-order counter) of the NRSP is sent and the
search ends here
Go to Step 2 for the next index
Otherwise, go to Step 5
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Encoding Algorithm Step5
Increment the search-order counter
Then repeat Step 4 for the next NRSP until a matched
point is found or all NRSP’s are examined (the
search-order counter value is greater than 2n)
If any NRSP is mismatched with the search center,
send the index value of the search center and go to
Step 2 for next index
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Proposed VQ System
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Simulation and Results
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Reference
[1] Chaur-Heh Hsieh; Jyi-Chang Tsai, “Lossless
compression of VQ index with search-order coding ,”
IEEE Signal Processing Lett. Volume 5, Issue
11, Nov. 1996 Page(s):1579 - 1582.
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