Performance of Discrete Meyer wavelet Transform on Encoding Levels using Computed Tomography Lung Image

 

R. Pandian, S. Lalitha Kumari, R Raja Kumar

Associate Professor, Department of EIE, Sathyabama Institute of Science and Technology, Chennai

 

ABSTRACT:

The image transmits and storage capability could be a broad appliance in compression. Image compression techniques engage the appropriate and successful transforms and encoding methods to attain the aim. In this work, discrete Meyer transform based image compression algorithm is used for decomposing the image. The effects of different Encoder loops are described based on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The finest encoding loop level for compression is also found based on the results.

 

 

1. INTRODUCTION:

In image storage and transmission memory requirements are always a challenge. Image compression algorithms are applied to overcome this, by the reduction of storage space and thus lead to sharing of documents easier. Image compression techniques formulate use of various transforms and algorithms. The algorithms can be normally classified as lossless and lossy compression^1,2. Lossless compression technique is chosen, if there is no compromise on high quality output without any loss inconsistency, whereas lossy compression technique is employed the areas, where a compromise is permissible in quality³. In lossy compression, there is negligible loss of quality, but the loss is too small to be visible. This technique is used in applications where a less amount of compromise on quality of image is tolerable. Initially, the image is usually decomposed with the help of proper transforms such as adaptive lifting, wavelet transform and wavelet packets⁴. Claypoole et al. worked an adaptive lifting scheme for image reduction and produced a MSE of 38.5 dB for Doppler, blocks etc. Claypoole et al. achieved a wavelet lifting scheme for image reduction with a PSNR of 42 dB⁵. Minh et al. used a wavelet transforms based image reduction for various images such as bike, cafe, water and X-rays⁶.

 

 

Entropy was used as a figure of merit and it was establish as 5.90, in their work. Gemma et al. implemented a contourlet transform based compression for both peppers and Barbara images⁷. Entropy was adopted as a figure of merit and it was calculated as 30.47 dB in their work. Deng et al. used wavelet lifting scheme for various images such as rectangular, crosses, houses and peppers⁸. The performance is evaluated with their entropy values. Hoagused DWT-seam carving methodology for a test image and bitrate was used a figure of merit and it was found as 0.09⁹. In their work, discrete wavelet transform is implemented for transforming the image and is primarily used for decomposition of images, whereas encoding is used to achieve the entire compression process and different type of Encoding methods are analyzed by various loop level to accomplish the image compression. The paper is structured in the following way. The transform methods and encoding algorithms are given below. Results, discussion and conclusion are below.

 

2.WAVELET TRANSFORM:

In wavelet analysis a set of basic functions are used to represent the images. A single prototype function called the mother wavelets used for deriving the basis function, by translating and dilating the mother wavelet¹⁰. The wavelet transform can be viewed as a decomposition of an image in the time scale plane. In this work, Daubechies, dmeyer transform is used. The basic and compact wavelet, which is proposed by Daubechies, is orthonormal wavelets, which is called as Daubechies wavelet. It is designed with extremely phase and highest number of vanishing moments for a given support width. Associated scaling filters are minimum-phase filter. Daubechies wavelets are generally used for solving fractal problems, signal discontinuities, etc. The dmeyer transform is nearly symmetrical wavelet, which are also proposed in this work¹¹.

 

3. ENCODING:

In compression, for the reduction of the redundant data and elimination of the irrelevant data is performed by encoding methods. In this work, embedded zero tree wavelet (EZW), set partitioning in hierarchical Trees (SPIHT), spatial orientation tree wavelet (STW), wavelet difference reduction (WDR) and adaptively scanned wavelet difference reduction (ASWDR) are used¹².

 

Embedded Zero Wavelet:

The embedded zero tree wavelet algorithm (EZW) is an image compression algorithm, in which embedded code represents a chain of binary decisions which distinguishes an image from the “null” image EZW which leads the compression results¹³.

 

 (CR- 16.61 %) (CR- 19.69 %) (CR- 21.7 %)

 

                                      (CR-24.15 %)                                          (CR- 26.56 %)                                              (CR-29.27%)

 

                              (CR-32.19 %)                                               (CR-36.1 %)                                                    (CR- 41.16 %)

Figure 1a. Compressed image of CT lung image using dmeyer with EZW (CR- 16.61 %),

(CR- 19.69 %) and (CR- 21.7 %), b Compressed image of CT lung image using dmeyer with EZW,

(CR-24.15%), (CR- 26.56 %) and (CR-29.27%)C.

Compressed image of CT lung image using dmeyer with EZW, (CR-32.19 %), (CR-36.1 %) and (CR- 41.16 %).

 

 

Table 1. Performance Evaluation of Various Encoding Levels.

 

Set Partitioning in Hierarchical Trees:

This algorithm uses a spatial orientation tree structure, which can be able to extract significant coefficients in wavelet domain, SPIHT does not have flexible features of bit stream but, it supports multi-rate, and it has high signal-to-noise ratio(SNR) and good image restoration quality, hence, it is appropriate for a high real-time requirement¹⁴. In this work dmeyer transform used for decomposing the lung image and encoding is performed by EZW. The compression is performed for various encoding loops which is shown in the Table 1 and compressed image shown in figure 1.

 

If the encoding loop level is increased, the PSNR value gets increased, irrespective of the wavelet type. The dmeyer transform performed well at loop level 10, which is found in terms of PSNR value. At loop level 1, the PSNR value is very low. At higher loop levels, especially at 6 and 8, the dmeyer wavelet is not performed equally well. The PSNR values a real most moderate, compared to levels 1 and 2. The bits per pixel values are also obtained and the variations in its values are also following the PSNR value, since, it is related to the PSNR. The mean square error is also decreased if the level of loop is increased. It is wise to obtain a minimum value of error, which is also provided by dmeyer at with the last loop level of decomposition.

 

4. CONCLUSION:

The main objective of this paper is to attain the high compression ratio, which is done by dmeyer transform with spiht encoding methods. It is clear from the results that the nature of the encoding loop is the cause for the change in the performance of compression. Results clearly indicate that the number of encoding loops also plays an important role in the compression algorithm. It is also evident that the rise in the number of encoding loop leads to loss in information.

 

5. REFERENCES:

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9.   Pandian R, Lalitha Kumari S, V Amala Rani, S Bestly Joe, Anne Frank Joe, Performance of loops in medical image compression, Biomedical Research 2018; 29 (15): 3054-3056.

10. S. Lalitha Kumari and R. Pandian (2016), “Analysis of Multi Scale Features of Compressed Medical Images”, International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) – 2016, 978-1-4673-9939-5/16/$31.00 ©2016 IEEE

11. Calderbank A R, Daubechies I, Sweldens W and Yeo B L, Wavelet transforms that map integers to integers. Appl. Comput. Harmon. Anal. 5(1998), 332–369.

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14. R. Pandian and Dr. S. Lalitha Kumari, , CT image for Lung cancer identification, Research Journal of Pharmacy and Technology, 9(2016), 2359-2361 .

 

 

 

 

 

Received on 06.06.2019           Modified on 04.07.2019

Accepted on 01.08.2019         © RJPT All right reserved