Effective compression using SPIHT with Entropy encoder

under the guidance of Batch members

Ms.K.Sucharitha K .Himaja (105C1A04A0) Asst.professor P.Saritha (105C1A04A5) K.Divya (105C1A04A7)

AIM OF THE PROJECT

To Compress image by using combination of entropy encoder and

embedded encoder.

Reduction of bandwidth required for transmission

Existing methods

Compression based Entropy Encoder

Compression based on SPIHT (Embedded Encoder)

Drawbacks

Total image is consider for while compression

This above problem will be overcome by Embedded encoder but the

number bit count will be high

PROPOSED METHOD

Decompose the image to different level

Apply the Embedded Encoder (SPIHT)

Apply Huffman Encoder to Embedded output bit streams

Same as like reverse process we have to apply

CONTENTS INTRODUCTION

BLOCK DIAGRAM

WORKING

ADVANTAGES

APPLICATIONS

CONCLUSIONS

FUTURE SCOPE

INTRODUCTION

Traditional image coding technology mainly uses the statistical redundancy

between pixels to reach the goal of compressing.

This wavelet transform has high speed, low memory requirements and

complete reversibility, digital wavelet transform (IWT)

Set Partitioning in Hierarchical Trees (SPIHT) is an improved version of

EZW.

FOUNDATION OF MATLAB

The name is derived from MATrix LABoratory

MATLAB is a case sensitive language

MATLAB works with matrices everything MATLAB understands is a

matrix

Various data types exist within MATLAB

ABOUT MATLAB • MATLAB is developed by The MathWorks, Inc.

• MATLAB is a high-level technical computing language and interactive

environment for algorithm development, data visualization, data analysis,

and numeric computation.

• MATLAB can be install on Unix, Windows

STRENGTHS

MATLAB is relatively easy to learn.

MATLAB code is optimized to be relatively quick when performing matrix

operations.

MATLAB may behave like a calculator or as a programming language.

MATLAB is interpreted, errors are easier to fix.

Block Diagram

Input Image Discrete Wavelet

Transform

Algorithm (SPIHT + Huffman Encoding)

Bit stream

DecodingInverse Discrete Wavelet

Transform

Output Image

MAJOR COMPONENTS

• Discrete wavelet transform

• Bit stream SPIHT and huffmann encoding

• Decoding

• Inverse discrete wavelet transform

Discrete Wavelet Transform

The Discrete Wavelet Transform (DWT) is used in a wide variety of signal

processing applications.

2-D discrete wavelet transform (DWT) decomposes an image into sub-

images, 3 details and 1 approximation

DWT separates the frequency band of an image into a lower resolution

approximation sub-band (LL) as well as horizontal (HL), vertical (LH) and

diagonal (HH) detail components

SPIHT AND BIT STREAM

SPIHT is computationally very fast and among the best image compression

algorithms known today.

According to statistic analysis of the output binary stream of SPIHT

encoding, propose a simple and effective method combined with Huffman

encode for further compression.

Typically these are values from a range of 256 distinct values.

DECODING

 Decoding is the reverse of encoding which is the process of transforming

information from one format into another.

To convert from code into plain text.

 To convert from a scrambled electronic signal into an interpretable one.

SOFTWARE

MATLAB (7.5 &10)

ADVANTAGES

DWT has a good localization property in the time domain and frequency

domain.

Number of encoding bits is less compare to existing method

APPLICATIONS

• Image compression is mainly used in Transmission

Application

• Compressions used in Storage Application for providing

security

• Image compression is mainly used in navy purposes

becauses they uses low bandwidth.

CONCLUSION

• Image compression using Discrete wavelet Transform (DWT) produces a

good clarity image.

• Even though there is no loss in information the output Image looks

similar to the input image.

REFERRENCESDavid S. Taubman, Michael W. Marcellin - JPEG 2000 – Image

compression, fundamentals, standards and practice", Kluwer academic

publishers, Second printing - 2002.

G. Knowles, "VLSI Architecture for the Discrete Wavelet Transform,”

Electronics Letters, vo1.26, pp. 1184-1185,1990.

QUERIES