image compression using aswdr and 3dspiht … set partitioning in hierarchical trees (spiht)...

Image Compression Using ASWDR and 3D-SPIHT Algorithms for Satellite Data

Dr.N.MuthumaniAssociate Professor

Department of Computer ApplicationsSNR Sons College

Coimbatore

K.PavithradeviAssistant Professor

Department of Computer ApplicationsGnanamani College of Technology

Nammakal

ABSTRACT

Compression is the process of representing information in a compact form so as to reduce the bitrate fortransmission or storage while maintaining acceptable fidelity or image quality. Algorithms for image compressionbased on wavelets have been developed. These algorithms have resulted in practical advances such as lossless andlossy compression, accuracy, resolution and others. We concentrate on the following methods of coding of waveletcoefficients, in this paper. ASWDR (Adaptively Scanned Wavelet Difference Reduction) algorithm and 3D-SPIHT(Set Partitioning in Hierarchical Tree) algorithm. These algorithms which achieve some of the lowest errors andhighest perceptual quality.

Key words

Remote Sensing Image, Wavelet Decomposition, ASWDR, 3D-SPIHT.

I. INTRODUCTION

Remote sensing in its broadest sense issimply defined as the observation of an object fromsome distance. Earth observation and weathersatellites, medical x-rays for bone fractures are allexamples of remote sensing. Remote sensing devicesmake use of emitted or reflected electromagneticradiation from the object of interest in a certainfrequency domain (infrared, visible light,microwave). Remote sensors are classified as either;active sensors or passive sensors. Active sensorsprovide their own source of radiation to send out toan object and record the magnitude of radiationreturns. Passive sensors record incoming radiationthat has been scattered, observed and transmittedfrom the earth in transmit from its original source, thesun.

In the recent years, operators are gettingmuch more information than ever before due to the

development of image sensors. The essentialdevelopment of sensors has also resulted in theaugmentation of the human observer workload. Todeal with these situations, there is a strong need ofdeveloping an image processing technique whichintegrates the information from different sensors. Itgreatly improves the capability of image interpretationand the reliability of image judgement which resultedin enhancing the accuracy of classification and targetrecognition. . A number of techniques and algorithmshave been proposed in the last few years for thecompression of remote sensing images. Thetechniques and algorithms to take advantage, invarious degrees, of the peculiarities of these images.The improvement of the WDR algorithm of Tian andWells[1] and [2] referred to as ASWDR. The ASWDRalgorithm aims to improve the subjective distortionmeasures. We shall treat three distortion measures,PSNR, MSE, Correlation which we define in thesection on image quality measurements.

International Journal of Scientific & Engineering Research, Volume 6, Issue 10, October-2015 ISSN 2229-5518

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The Set Partitioning in Hierarchical Trees(SPIHT) algorithm [3] is one of the best waveletbased coding algorithm for remote sensing images. Itis very efficiently exploits the self similarity betweendifferent levels in the wavelet pyramid and providesan embedded bit stream which support PSNR, MSEscalability. The excellent performance of SPIHT forremote sensing image makes it an attractive codingstrategy. A 3D extension of SPIHT has beenproposed by Kim and Pearlman in [4]. They applied3D wavelet transform and coded the waveletcoefficients by the 3D SPIHT algorithm.

Compression is a commonly used process toreduce the amount of initial data to be stored ortransmitted by a channel to a receiver. 3D SPIHT uselists (list of significant and insignificant pixels, list of

insignificant sets) which grow very fast compared tolist of 2D SPIHT (each pixels has eight children forthe 3D version and only four in 2D). 3D SPIHT isnecessary to have a wavelet decomposition outputcorresponding to the input of 3D SPIHT encoder. Inthis paper we compare the proposed work usingASWDR and 3D SPIHT algorithms.

II. METHODOLOGY

Image Compression is one of the techniquesin image processing. There are various types ofAlgorithms and techniques are used for compressedthe images. Some of the Algorithms and techniquesare SPECK Algorithm, SPIHT Algorithm, ASWDRAlgorithm, LZW Coding, Fractal Coding. Here theproposed work is represented the architecture asshown the fig-1.

Fig 1: System Architecture

III. THE 3D-SPIHT COMPRESSION

The SPIHT algorithm is a highly refinedversion of the EZW algorithm. It was introduced bySaid and Pearlman. Some of the best results highestPSNR values for given compression ratios for a widevariety of images have been obtained with SPIHT.Consequently, it is probably the most widely usedwavelet based algorithm for image compression,providing a basic standard of comparison for allsubsequent algorithms. SPIHT stands for SetPartitioning in Hierarchical Trees. The termHierarchical trees refers to the quadtrees, SetPartitioning refer to the way these quadtrees divideup, partition, the wavelet transform values at a giventhreshold. The SPIHT algorithm proposed in thispaper solves the spatial and temporal scalabilitytrough the introduction of multiple resolutionsdependent lists and a resolution dependent sorting

pass. It keep important feature of the original SPIHTcoder such as compression efficiency, fullembeddedness, and rate scalability. The fullscalability of the algorithm is achieved through theintroduction multiple resolution dependent lists of thesorting stage of the algorithm. The idea of bitstreamtranscoding without decoding to obtain differentbitstreams for various spatial and temporalresolutions and bit rates is completely supported bythe algorithm.

SPIHT coding is applied on each band of thewavelet transform results to achieve compression. Inorder to take this fact into account, it is preferable toweight each band. As weight we use the energy

, where is the image

band at the wavelength, X and Y and its dimension,

Input Image WaveletDecomposition

Decoded Quantization

Huffman CodingASWDRalgorithm

3D-SPIHTAlgorithm

CompressedImage


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and x and y are the position of a pixel in the band.Depending on energy band, we allocate proportionalnumber of bits for the output of the SPIHT algorithm.The 3D approach consists in considering the wholeremote sensing image as an input for full 3Ddecomposition. To achieve compression 3D SPIHT[5] is then applied.

The 3D SPIHT algorithm of [6] considersset of coefficients that are related through a parentoffspring. In its bitplane coding process, thealgorithm deals with the wavelet coefficients as eithera root of an insignificant set, an individualinsignificant pixel, or a significant pixel. It sorts thesecoefficients in three ordered lists: the list ofinsignificant sets (LIS), the list of insignificant pixels(LIP), and the list of significant pixels (LSP). Themain concepts of the algorithm is managing theselists in order to efficiently extract insignificant sets ina hierarchical structure and identify significantcoefficients which is the core of its high compressionperformance.

The 3D wavelet decomposition provides amultiresolution structure that consists of differentspatio-temporal subbands that can be codedseparately by a scalable encoder to provide variousspatial and temporal scalabilities. In general byapplying levels of 1D temporal decomposition and

levels of 2D spatial decomposition, at mostlevels of spatial resolution and levels oftemporal scalability are achievable. The total numberof possible spatio-temporal resolution in this case is

. To distinguish betweendifferent resolutions levels, we denote the lowestspatial resolution levels as level and thelowest temporal resolution level as . Algorithmprovides full spatial and temporal scalability wouldencode the different resolution resolution subbandsseparately, allowing a transcoder or a decoder todirectly access the data needed to reconstruct adesired spatial and temporal resolution.

IV. ASWDR COMPRESSION

One of the most recent image compressionalgorithms is the Adaptively Scanned Wavelet

Difference Reduction (ASWDR) algorithm of Walker[7][8]. The adjective adaptively scanned refers to thefact that this algorithm modifies the scanning orderused to achieve better performance. AdaptivelyScanned Wavelet Difference Reduction (ASWDR)algorithm produces on embedded bit stream withregion of interest capability. It is simplegeneralization of the compression method developedby [1] which they named as Wavelet DifferenceReduction (WDR). While the WDR method employsa fixed ordering of the positions of waveletcoefficients, the ASWDR method employs a varyingorder which aims to adapt itself to specific imagefeatures. ASWDR algorithm aims to improve thesubjective perceptual qualities of compressed imagesand improve the results of objective measures. Thebasic structure of ASWDR is the following.

V. WAVELET TRANSFORMATION OFIMAGES

Wavelets are mathematical functions thatdecompose data into different frequency components,and then study each component with a resolutionmatched to its scale. Wavelets were developedindependently in the field of mathematics, quantumphysics, electrical engineering, and seismic geology.Interchanges between these fields during the last tenyears have led to many new wavelet applicationssuch as image compression, turbulence, humanvision, radar, and earthquake prediction. The wavelettransformation [9] is a mathematical tool for

Step 1: Wavelet transform image.

Step 2: Initialize scan order and threshold.

Step 3: Significance Pass, Encode newsignificant values using difference reduction.

Step 4: Refinement pass, Generate refinementbits for old significant values.

Step 5: Update scan order to search throughcoefficients that are more likely to besignificant at half-threshold.

Step 6: Divide threshold by 2, repeat step 3and 4.


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decomposition. The wavelet transform is identical toa hierarchical sub band filtering system [10], wherethe sub bands are logarithmically spaced infrequency.

Fig 2: Wavelet Decomposition

The basic idea of the DWT for a two-dimensional image is described as follows. An imageis first decomposed into four parts based onfrequency sub bands, by critically sub samplinghorizontal and vertical channels using sub band filtersand named as Low-Low (LL), Low-High (LH), High-Low (HL), and High- High (HH) sub bands as shownin figure 2.

VI. IMAGE QUALITY MEASUREMENTS

A. Mean Square Error (MSE) The simplest of image quality measurement isMean Square Error (MSE). The large value of MSEmeans that image is poor quality [11]. MSE isdefined as follow:

MSE =

B. Peak Signal to Noise Ratio (PSNR)

The small value of Peak Signal to Noise Ratio(PSNR) means that image is poor quality. In general, agood reconstructed image is one with low MSE andhigh PSNR [11].PSNR is defined as follow:

PSNR =

C. Correlation

Correlation coefficient quantifies the closenessbetween two images. The correlation coefficient iscomputed by using the following equation [12].

A correlation is a number between -1 and +1 thatmeasures the degree of association between twovariables (call them X and Y). A positive value forthe correlation implies a positive association (largevalues of X tend to be associated with large values ofY and small values of X tend to be associated withsmall values of Y). A negative value for thecorrelation implies a negative or inverse association(large values of X tend to be associated with smallvalues of Y and vice versa).

VII. DATASET DESCRIPTION

Remote sensing image is defined as an imageproduced by a recording device that is not in physical orintimate contact with the object under study. Remotesensing image is used to obtained information about atarget or an area or phenomenon through the analysis ofcertain information which is obtain by the remote sensingimagery generally require correction of undesirable sensorcharacteristics and other disturbing effects beforeperforming data analysis. Images obtained by satellite areuseful in many environmental applications such astracking of earth resources, geographical mapping,prediction of agriculture crops, urban growth, weather,flood and fire control etc. When capturing image usingsensors, the resulting image may contain Noise fromdirtiness on the image data acquisition process. So in thispaper, we have analysed a remote sensing image. It isdownloaded from Google sites.

VIII. RESULTS

The fig-3 shows the experimental results of theproposed work. The test image is taken as Inputimge. It has very high frequency components, so theASWDR and 3D-SPIHT algorithm is used tocompress the Image. Both of the Algorithmscompress the image. When compared to theASWDR, 3D-SPIHT produces better compressionwhen compared to ASWDR algorithm which isshown in fig-3. This shows that the 3D-SPIHTAlgorithm has shown good efficiency for imagecompression. The proposed work is done usingMATLAB. 2010 version.


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Remote Sensing Image Remote Sensing Image Remote Sensing Image

Wavelet Decomposition Wavelet Decomposition Wavelet Decomposition

Decoded Image Decoded Image Decoded Image


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ASWDR Image ASWDR Image ASWDR Image

3D-SPIHT Image 3D-SPIHT Image 3D-SPIHT Image

Fig 3: Compression of ASWDR and 3D-SPIHT

ASWDR ALGORITHMIMAGES PSNR MSE CORRELATIONImage1 31.5316 45.7007 0.9812

Image2 31.1684 49.6864 0.9906

Image3 29.7595 68.7274 0.9700

Image4 32.0921 40.1675 0.9903

Image5 30.0326 64.5394 0.9544


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Table 1: Results of PSNR, MSE, CORRELATION Value for ASWDR Algorithm

3D-SPIHT ALGORITHMIMAGES PSNR MSE CORRELATIONImage1 34.8830 21.1242 0.9909

Image2 32.0620 40.4464 0.9963

Image3 33.5381 28.7919 0.9938

Image4 33.3305 30.2015 0.9930

Image5 31.8227 42.7380 0.9949

Table 2: Results of PSNR, MSE, CORRELATION Value for 3D-SPIHTAlgorithm

Fig 4: Comparison of PSNR in ASWDR and 3D-SPIHT

Fig 5: Comparison of MSE in ASWDR and 3D-SPIHT

Fig 6: Comparison of CORRELATION in ASWDRand 3D-SPIHT

IX. CONCLUSION

In this paper, we compared ASWDRalgorithm 3D-SPIHT algorithm for the compressionof remote sensing images. The interesting features ofthe original 3D-SPIHT algorithm such as highcompression efficiency, embeddedness and very finegranurality of the bitstream are kept. Our experimentshows as 3D-SPIHT algorithm seems to be preferablefor compression than ASWDR algorithm because ofits quality of image. Results show that 3D-SPIHTAlgorithm maintaining the full embeddednessrequired by colour image compression and givesbetter performance in terms of the PSNR andcompression ratio than the ASWDR algorithm.

X. REFERENCES

[1] J. Tian and R.O. Wells, Jr. A lossy image codecbased on index coding. IEEE Data CompressionConference, DCC ’96, page 456, 1996.

[2] J. Tian and R.O. Wells, Jr. Embedded imagecoding using waveletdifference- reduction.WaveletImage and Video Compression, P. Topiwala, ed., pp.289–301. Kluwer Academic Publ., Norwell, MA,1998.

[3] Praneesh, M., and Jaya R. Kumar. "Article:Novel Approach for Color based Comic ImageSegmentation for Extraction of Text using ModifyFuzzy Possiblistic C-Means ClusteringAlgorithm." IJCA Special Issue on Information


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Processing and Remote Computing IPRC (1) (2012):16-18.

[4] B.-J. Kim, Z. Xiong, and W. A. Pearlman, “Lowhitrate scalable video coding with 3-d set partitioningin hierarchical trees (3-D SPIHT),” IEEE Trans. Circ.and Syst. for video Technology, vol. 10, no. 8, pp.1374-1387,Dec. 2000

[5] Napoleon, D., Praneesh, M., Sathya, S., &SivaSubramani, M. (2012, March). An efficientmodified fuzzy possibilistic c-means algorithm forsegmenting color based hyperspectral images.In Advances in Engineering, Science andManagement (ICAESM), 2012 InternationalConference on (pp. 798-803). IEEE.

[6] L. Dragotti, G. Poggi, A.R.P. Ragozini,“Compression of multispectral images by three-dimensional SPIHT algorithm”. IEEE Transactionson Geoscience and Remote Sensing 38(1), 2000, pp.416-428.

[7] B.-J.Kim and W. A. Pearlman, “An embeddedvideo coder using three-dimensional set partitioningin hierarchical trees (SPIHT),” inproc. IEEE DataCompression Con$, Mar. 1997, pp. 251-260.

[8] R.Sudhakar, Ms R Karthiga, S.Jayaraman., “ImageCompression using Coding of Wavelet Coefficients –A Survey,” ICGST-GVIP Journal, Vol. 5, Issue. 6,June 2005.

[9] Napoleon, D., Praneesh, M., Sathya, S., &SivaSubramani, M. (2012). An Efficient Numerical

Method for the Prediction of Clusters Using K-Means Clustering Algorithm with Bisection Method.In Global Trends in Information Systems andSoftware Applications (pp. 256-266). Springer BerlinHeidelberg.

[10] James S. Walker., “A Lossy Image Codec Basedon Adaptively Scanned Wavelet DifferenceReduction,” Applicable Analysis, Vol. 85, No. 4, pp.439–458, April 2006.

[11] K.P.Soman,K.I.Ramachandran “Insight intoWavelets from theory to practice”. Prentice-Hall ofIndia Private Limited.

[12] Napoleon, D., Praneesh, M., & Hemalatha, S.(2012). Content based Image Retrieval using Textureand Color Extraction based Binary TreeStructure.Artificial Intelligent Systems and MachineLearning, 4(5), 345-348.

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[14] Napoleon, D., S. Sathya, M. Praneesh, and M.Siva Subramanian. "Remote Sensing ImageCompression using 3 D-Oriented WaveletTransform."International Journal of ComputerApplications 45, no. 24 (2012).

[15] Wang, Z.; Bovik, A.C.; Lu, L., (2002). Why isImage Quality Assessment So Difficult? IEEEInternational Conference on Acoustics, Speech, &Signal Processing, 4, pp. IV-3313 - IV-3316.


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image compression using aswdr and 3dspiht … set partitioning in hierarchical trees (spiht)...

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