HIERARCHAL CODING STRATEGY FOR EFFICIENT COMPRESSION FOR MEDICAL IMAGES: SIMULATION RESULTS

CASE-1

INPUT PROPERTY:

a) Image Name – T128
b) Dimensions – 128×128
c) Bit Depth – 24 bits

Fig7Hierarchal Coding Strategy
Fig. 7 : (A) Original image (B) Wavelet based output (C) Hierarchal zero coding output

Compression for (B) % = 35
Compression for (C) % = 67
Computation time for (B) (sec)=23
Computation time for (C) (sec)= 7
Mean square error for (B) = 0.18
Mean square error for (C) = 0.1

CASE-2

Input property:

A) Image Name – t-256
B) Dimensions – 256 x 256
C) Bit Depth – 24 bitsĀ  coding output
Fig8Hierarchal Coding Strategy
Fig 8:(A) Original image (B) Wavelet based output (C) Hierarchal zero

Compression for (B) % = 33 Compression for (C) % = 47 Computation time for (B) (sec)=4 Computation time for (C) (Sec)=8 Mean square error for (B) = 0.45 Mean square error for (C) = 0.25

Fig9Hierarchal Coding Strategy
Figure 9: PSNR comparison plot

The above figure shows the PSNR improvement of proposed method compared to the conventional method.

CONCLUSION

In this work observed that Hierarchical zero wavelet coding is able to achieve good performance with a relatively simple algorithm. Hierarchical zero wavelet coding does not require complicated bit allocation procedures like sub band coding. It also does not require training or codebook storage like vector quantization , and require prior knowledge of the image source like wavelet based compression (to optimize quantization tables). Hierarchical Zero Wavelet Coding also has the desirable property, which results from its successive approximation quantization.

One desirable consequence of an embedded bit stream is that it is very easy to generate coded outputs with the exact desired size. Truncation of the coded output stream does not produce visual artifacts since the truncation only eliminates the least significant refinement bits of coefficients rather than eliminating entire coefficients as is done in subband coding.

From the simulation results , it is concluded that Hierarchal Zero wavelet coding requires comparatively less(about 60%) time than the Wavelet Based Compression coding system. This coding also shows less percentage of error in retrieved image compare to the existing Wavelet Based Compression coding system. It is further observed that image coded with Hierarchal Zero Wavelet tree coding produces clearer image than other coding system.

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