Traditional
image coding technology mainly uses the statistical redundancy between pixels
to reach the goal of compressing. The research on wavelet transform image
coding technology has made a rapid progress. Because of its high speed, low
memory requirements and complete reversibility, digital wavelet transform (IWT)
has been adopted by new image coding standard, JPEG 2000. The embedded zero
tree wavelet (EZW) algorithms have obtained not bad effect in low bit-rate
image compression. Set Partitioning in Hierarchical Trees (SPIHT) is an
improved version of EZW and has become the general standard of EZW.

**Transmitter:**

**Input Image:**

The input image is a
grayscale image. File type of that image is .bmp (Bitmap file format) or RAW
(Raw image format).These formats are uncompressed hence they are large.

**Discrete wavelet transforms:**

The
purpose served by the Wavelet Transform is that it produces a large number of
values having zero, or near zero, magnitudes.

One of the most efficient
algorithms in the area of image compression is the Set Partitioning in
Hierarchical Trees (SPIHT). In essence it uses a sub-band coder, to produce a
pyramid structure where an image is decomposed sequentially by applying power
complementary low pass and high pass filters and then decimating the resulting
images. These are one-dimensional filters that are applied in cascade (row then
column) to an image whereby creating four-way decomposition: LL (low-pass then
another low pass), LH (low pass then high pass), HL (high and low pass) and
finally HH (high pass then another high pass). The resulting LL version is
again four-way decomposed, as shown in Figure 1. This process is repeated until
the top of the pyramid is reached. The SPIHT algorithm sends
the top coefficients in the pyramid structure using a progressive transmission
scheme. This scheme is a method that allows obtaining a high quality version of
the original image from the minimal amount of transmitted data. Upper leftmost square
represents the smooth information (lowest frequency) i.e. blurred version of image.
Other squares represent detail information (edges) in different directions
(horizontal, diagonal &vertical) and at different scale.

**Quantization**

After
the wavelet transform, the coefficients are scalar-quantized to reduce the
number of bits to represent them, at the expense of quality. The output is a
set of integer numbers which have to be encoded bit-by-bit. The parameter that
can be changed to set the final quality is the quantization step: the greater
the step, the greater is the compression and the loss of quality. With a
quantization step that equals 1, no quantization is performed (it is used in
lossless compression).

**Set Partitioning in Hierarchical Trees (SPIHT)**

**Rules for Set Partitioning**

The set partitioning rule is designed to work in the subband
hierarchy. The objective of the set partitioning algorithm should be such that
the subsets expected to be insignificant contain larger number of elements and
the subsets expected to be significant should contain only one element.

We first define the following sets before presenting the set
partition rules:

**O (n1, n2):**The offspring set. It contains the coordinates of the pixels, which are offspring of the node (n1, n2)

**D (n1, n2):**The descendants’ set. It contains all the coordinates of the pixels which are descendants of the node (n1, n2)

**L (n1, n2):**It is the difference set of D (n1, n2) & O (n1, n2).It therefore contains the descendants of the node (n1, n2) other than the offspring.

**H:**This set includes the co-ordinates of all spatial orientation tree roots, which belong to the highest level of pyramid, that is, the LL subband.

Based
on the above set definitions, the set partitioning rules are presented below:

**Rule-1**

The
initial partition contains D (n1, n2) for each (n1, n2)
H.

**Rule-2**

If
D (n1, n2) is found significant, partition it into L (n1, n2) plus four single
sets with (i, j)
O (n1, n2).

**Rule-3**

If
L (n1, n2) is found significant, partition it into four sets of D (i,j),

Where
(i, j)
O (n1, n2)

**SPIHT ENCODING:**

The SPIHT algorithm applies
the set partitioning rules, as defined above on the subband coefficients. The
algorithm is identical for both encoder and decoder and no explicit
transmission of ordering information, as needed in other progressive
transmission algorithms for embedded coding, are necessary. This makes the
algorithm more coding efficient as compared to its predecessors. Both the
encoder and decoder maintain and continuously update the following three lists,
viz.

**List of Insignificant Pixels (LIP)**

The list of insignificant
pixels (LIP) contains individual coefficients that have magnitudes smaller than
the threshold.

**List of Significant Pixels (LSP)**

The list of significant
pixels (LSP) is a list of pixels found to have magnitudes larger than the
threshold (significant).

**List of Insignificant Sets (LIS)**

The list of insignificant
sets (LIS) contains sets of wavelet coefficients that are defined by tree
structures and are found to have magnitudes smaller than the threshold
(insignificant). The sets exclude the coefficients corresponding to the tree
and all sub tree roots and they have at least four elements.

**RECEIVER:**

**MATLAB Implementation of SPIHT:**
GUI window for compression using SPIHT

GUI window for Decompression using SPIHT

**if you want MATLAB code then mail us on.........****matlabprojects07@gmail.com**

can u give me amtlab code foe sphit and ezw techniques?

ReplyDeletecan u give me amatlab code for sphit and ezw techniques?

Deletecan you pls send me the code for "Image Compression using SPIHT"?

ReplyDeletecan u give me amatlab code for sphit and ezw techniques

ReplyDeleteI have applied wavelet transform on 'hyperspectral' images, i need to code that images using SPIHT ,can u send me the code for the same .

ReplyDelete