A COMPARATIVE STUDY OF DCT AND WAVELET-BASED IMAGE CODING & RECONSTRUCTION Mr. S Majumder & Dr. Md. A Hussain Department of Electronics & Communication.

A COMPARATIVE STUDY OF DCT AND WAVELET-BASED

IMAGE CODING & RECONSTRUCTION

Mr. S Majumder & Dr. Md. A Hussain

Department of Electronics & Communication Engineering NERIST (North Eastern Regional Institute of Science & Technology)

(Deemed University), Arunachal Pradesh [email protected] & [email protected]

IMAGE COMPRESSIONTHE NEED FOR COMPRESSION

1. Spatial redundancy Correlation between neighboring pixels values

2. Spectral redundancy’ Correlation between different spectral bands

INTRODUCTION TO IMAGE COMPRESSION 1. Lossless compression2. Lossy compression

OBJECTIVE 1. Minimum distortion

2. High compression ratio3. Fast computation time

DCT-Based Image Coding Standard

The DCT can be regarded as a discrete-time version of the Fourier-Cosine series. It is a close relative of DFT, a technique for converting a signal into elementary frequency components. Thus DCT can be computed with a Fast Fourier Transform (FFT) like algorithm in O(n log n) operations. Unlike DFT, DCT is real-valued and provides a better approximation of a signal with fewer coefficients. The DCT of a discrete signal x(n), n=0, 1, .. , N-1 is defined as:

where, C(u) = 0.707 for u = 0 and

= 1 otherwise.

DCT based Encoder & Decoder

DISCRETE WAVELET TRANSFORM

FLOWCHART FOR 2D FORWARD DWT

2D DWT (4 Steps)

WAVELETS

ZIGZAG SCAN PROCEDURE

Zigzag Scanning converts the 2D data into 1D data

QUANTIZATION

Uniform Quantization

Non-Uniform Quantization Quantization

UNIFORM QUANTIZATION

FLOWCHART FOR UNIFORM QUANTIZER & DEQUANTIZER

ENTROPY ENCODING

The quantized data contains redundant information. It is a waste of storage space if we were to save the redundancies of the quantized data.

Run-Length Encoding

Huffman Encoding

ENTROPY ENCODING

RUN-LENGTH ENCODING

FLOWCHART FOR RUN LENGTH ENCODER & DECODER

HUFFMAN ENCODING

FLOWCHART FOR HUFFMAN ENCODER & DECODER

FLOWCHART FOR 2D INVERSE DWT

DCT and DWT (Daubechies 6-tap) output size, after coding indifferent coding techniques versus Quantization level (for 14,321 bytes)

Image Size after Coding in various schemes

0

2000

4000

6000

8000

10000

12000

2 4 6 8 10 12 14 16 18 20 22 24 26 28

Quantization Levels

No

. o

f B

ytes

DWT+RLE

DWT+Huff

DWT+Huff+RLE

DCT+RLE

DCT+Huff

DCT+Huff+RLE

Reconstructed image size versus Quantization levels for different encoding techniques

Reconstructed image size Comparison

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2 4 6 8 10 12 14 16 18 20 22 24 26 28

Quantization Levels

No

. of

by

tes

Original size

DWT

DCT

CONCLUSION• For still images, the wavelet transform based

compression outperforms the DCT based compression typically in terms of the compressed output for different quantization levels, as well as the reconstructed image quality.

• For the same reconstructed image size of 14 Kb and equivalent image clarity, DWT based coded image requires less than half transmission bandwidth and storage requirement as compared to DCT based coded image.

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Prentice Hall, 1995, [3] Gersho, A. and Gray, R. M. Vector Quantization and Signal Compression, Kluwer

Academic Publishers, 1991[4] Nelson, M. The Data Compression Book,2nd ed., M&T books, Nov. 1995, [5] Tsai, M. J., Villasenor, J. D., and Chen, F. Stack-Run Image Coding, IEEE Trans.

CSVT, vol. 6, no. 5, Oct. 1996, pp. 519-521, [6] A.B.Watson, G.Yang, J.A.Solomon, and J Villasenor, Visibility of Wavelet Quantization

Noise, IEEE Transactions on Image Processing, Vol. 6, No. 8, August 2002.[7] O.N.Gerekand, A.E.Cetin, “Adaptive polyphase subband decomposition structures for

image compression,” IEEE Trans. Image Processing, vol. 9, pp. 1649–1659, Oct. 2000.[8] S.D.Servetto, K.Ramchandran, V.A.Vaishampayan, and K Nahrstedt, Multiple

Description “Wavelet Based Image Coding”, IEEE Trans on Image Processing, vol. 9, no. 5, may 2000

[9] Henrique S. Malvar, “Fast Progressive Image Coding without Wavelets” IEEE Data Compression Conference – Snowbird, Utah, March 2000

THANK YOU