![Indonesian Journal of Electrical Engineering and Computer Science
Vol. 8, No. 3, December 2017, pp. 740 ~ 742
DOI: 10.11591/ijeecs.v8.i3.pp740-742 740
Received August 4, 2017; Revised October 17, 2017; Accepted November 2, 2017
Quantization Encoding Algorithm Based Satellite Image
Compression
Anand M, Dr V Mathivananr
Department of Information Technology, AMET University, Chennai
Abstract
In the field of digital data there is a demand in bandwidth for the transmission of the videos and
images all over the worlds. So in order to reduce the storage space in the field of image applications there
is need for the image compression process with lesser transmission bandwidth. So in this paper we are
proposing a new image compression technique for the compression of the satellite images by using the
Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm
for the compression. The performance of our method can be evaluated and analyzing the PSNR values of
the output images.
Keywords: Compression, Lossy, Quantization, ROI, and, PSNR
Copyright © 2017Institute of Advanced Engineering and Science. All rights reserved.
1. Introduction
Medical image compression based on hybrid DWT with Back Propagation Neural
Network (BPNN) approach is described in [1]. Compressed image quality is improved by DWT
technique and BP algorithm can be widely used as a learning algorithm. Region of interest
coding techniques for medical image compression is described in [2]. ROI coding is to permit
the use of arbitrarily and multiple shaped ROIs with random weights describing the degree of
significance for every ROI images.
An adaptive sampling algorithm is used in [3], for evaluating the area difference
between the predicted and the correct points to decide the significant coefficients. The paper
discussed in [4] uses the hybrid algorithm for the image compression technique. The hybrid
algorithm is DWT for achieving a higher compression ratio and the DCT with the Huffman
algorithm is used to preserve the quality of the reconstructed image.
Region of interest (ROI) based on compression of image is discussed in [5] for
classification applications. The method is a fusion of both lossy and lossless compression along
with wavelets transforms like Embedded Zero-Tree Wavelet (EZW) is as derived techniques. A
method to increase the compression ratio with less computational burden is discussed in [6]. In
order to decomposition of a sub-block into equal sized bands, the DCT is used as a bandpass
filter and a high similarity property is found among the bands.
An improved SPIRT algorithm in which most of the energy is intense in the low
frequency sub-band after wavelet transform is discussed in [7]. An image compression method
based on Integer Wavelet Transform (IWT) and SVD is discussed in [8]. A graph based
quantization is used in the method but the adaptive Huffman coding is used for
entropy encoding.
To compress the information that are sent form one place to another by means of the
low frequency coefficients the LZW algorithm is used in [9]. This compression algorithm is done
for the ROI based extracted image. The statistics analysis associated with difference image is
discussed in [10]. The paper is based on the statistical analysis measure when compared with
the compression of the lossless image.
2. Proposed System
Our proposed system is a method of lossy based image compression scheme based on
the quantization encoding algorithm. In this method the compression is done by extracting the](https://image.slidesharecdn.com/419147quantizationencodingalgorithmbasededittyas-201016084530/85/41-9147-quantization-encoding-algorithm-based-edit-tyas-1-320.jpg)
![ ISSN: 2502-4752
IJEECS Vol. 8, No. 3, December 2017 : 740 – 742
742
(a) (b)
(c) (d)
Figure 2(a). Original image (b) Extracted ROI (c) Quantization Output
(d) Compressed image
6. Conclusion
The proposed image compression method based on the ROI extracted based lossy
compression method by using the quantization algorithm is discussed. The method is tested by
the literally available satellite images. And the performance of the proposed system is analyzed
by calculating the PSNR values of the compressed image. Our proposed system has obtained
PSNR values of about 91.8% db.
References
[1] Perumal B & Rajasekaran MP. A hybrid discrete wavelet transform with neural network back
propagation approach for efficient medical image compression. IEEE International Conference on
Emerging Trends in Engineering, Technology and Science. 2017: 1-5.
[2] Doukas C & Maglogiannis I. Region of interest coding techniques for medical image compression.
IEEE Engineering in medicine and Biology Magazine. 2017; 26(5): 29-35.
[3] Wu YG. Medical image compression by sampling DCT coefficients. IEEE Transactions on
Information Technology in Biomedicine. 2002; 6(1): 86-94.
[4] Sharma S & Bhat U. Image Compression using an efficient hybrid algorithm. 2013.
[5] Reddy BV, Reddy PB, Kumar PS & Reddy AS. Lossless Compression of Medical Images for Better
Diagnosis. IEEE 6th
International Conference on Advanced Computing. 2016: 404-408.
[6] Wu YG & Tai SC. Medical image compression by discrete cosine transforms spectral similarity
strategy. IEEE Transactions on Information Technology in Biomedicine. 2001; 5(3): 236-243.
[7] Bin L & Qinggang M. An improved SPIHT wavelet transform in the underwater acoustic image
compression. IEEE International Conference on Measurement, Information and Control. 2013; 2:
1315-1318.
[8] Panda SS and Jena G. Image Super Resolution Using Wavelet Transformation Based Genetic
Algorithm. In Computational Intelligence in Data Mining. Springer India. 2016; 2: 355-361.
[9] Panda SS, Jena G and Sahu SK. Image super resolution reconstruction using iterative adaptive
regularization method and genetic algorithm. In Computational Intelligence in Data Mining. Springer
India. 2015; 2: 675-681.
[10] Asraf R, Akbar M & Jafri N. Statistical analysis of difference image for absolutely lossless
compression of medical images. IEEE 28th Annual International Conference on Engineering in
Medicine and Biology Society. 2006: 4767-4770.](https://image.slidesharecdn.com/419147quantizationencodingalgorithmbasededittyas-201016084530/85/41-9147-quantization-encoding-algorithm-based-edit-tyas-3-320.jpg)
41 9147 quantization encoding algorithm based edit tyas
- 1. Indonesian Journal of Electrical Engineering and Computer Science Vol. 8, No. 3, December 2017, pp. 740 ~ 742 DOI: 10.11591/ijeecs.v8.i3.pp740-742 740 Received August 4, 2017; Revised October 17, 2017; Accepted November 2, 2017 Quantization Encoding Algorithm Based Satellite Image Compression Anand M, Dr V Mathivananr Department of Information Technology, AMET University, Chennai Abstract In the field of digital data there is a demand in bandwidth for the transmission of the videos and images all over the worlds. So in order to reduce the storage space in the field of image applications there is need for the image compression process with lesser transmission bandwidth. So in this paper we are proposing a new image compression technique for the compression of the satellite images by using the Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm for the compression. The performance of our method can be evaluated and analyzing the PSNR values of the output images. Keywords: Compression, Lossy, Quantization, ROI, and, PSNR Copyright © 2017Institute of Advanced Engineering and Science. All rights reserved. 1. Introduction Medical image compression based on hybrid DWT with Back Propagation Neural Network (BPNN) approach is described in [1]. Compressed image quality is improved by DWT technique and BP algorithm can be widely used as a learning algorithm. Region of interest coding techniques for medical image compression is described in [2]. ROI coding is to permit the use of arbitrarily and multiple shaped ROIs with random weights describing the degree of significance for every ROI images. An adaptive sampling algorithm is used in [3], for evaluating the area difference between the predicted and the correct points to decide the significant coefficients. The paper discussed in [4] uses the hybrid algorithm for the image compression technique. The hybrid algorithm is DWT for achieving a higher compression ratio and the DCT with the Huffman algorithm is used to preserve the quality of the reconstructed image. Region of interest (ROI) based on compression of image is discussed in [5] for classification applications. The method is a fusion of both lossy and lossless compression along with wavelets transforms like Embedded Zero-Tree Wavelet (EZW) is as derived techniques. A method to increase the compression ratio with less computational burden is discussed in [6]. In order to decomposition of a sub-block into equal sized bands, the DCT is used as a bandpass filter and a high similarity property is found among the bands. An improved SPIRT algorithm in which most of the energy is intense in the low frequency sub-band after wavelet transform is discussed in [7]. An image compression method based on Integer Wavelet Transform (IWT) and SVD is discussed in [8]. A graph based quantization is used in the method but the adaptive Huffman coding is used for entropy encoding. To compress the information that are sent form one place to another by means of the low frequency coefficients the LZW algorithm is used in [9]. This compression algorithm is done for the ROI based extracted image. The statistics analysis associated with difference image is discussed in [10]. The paper is based on the statistical analysis measure when compared with the compression of the lossless image. 2. Proposed System Our proposed system is a method of lossy based image compression scheme based on the quantization encoding algorithm. In this method the compression is done by extracting the
- 2. IJEECS ISSN: 2502-4752 Quantization Encoding Algorithm Based Satellite Image Compression (Anand M) 741 ROI regions from the original satellite images. Then the compression algorithm of quantization encoding is applied and is compressed. The framework of the system is shown in Figure 1. 3. Extraction of ROI The ROI extraction process is the first step in our proposed system. This ROI image is the images that are used for the compression process so as to increase our compression accuracy. The system is said to be as the lossy compression method. The reason for this is due to the usage of the ROI image for the compression process. From the ROI images the lossy compression is done by using the quantization encoding algorithm. Figure 1. Framework of the proposed image compression system 4. Quantization Encoding Algorithm The quantization algorithm is a type of the compression algorithm that is used for the compression process. Here the ROI images extracted from the original images are taken as the input for the quantization encoding is used for compression. That allows the probability density modeling functions by the prototype vectors. This usually works by obtaining the values by means of encoding multi-dimensional space vectors into a finite set of vector space values from a subspace. A lower vector space region needs a less storage space, so the data is compressed. 5. Results and Discussion The image compression technique based on the ROI image based quantization encoding is said to be as the lossy compression technique in which the compression occurs with a dataloss in it during the encoding and decoding of the transmission process. The output result is as shown in Figure 2. Compressed Image Input image ROI Extraction Quantization Algorithm Decompressio n of ROI
- 3. ISSN: 2502-4752 IJEECS Vol. 8, No. 3, December 2017 : 740 – 742 742 (a) (b) (c) (d) Figure 2(a). Original image (b) Extracted ROI (c) Quantization Output (d) Compressed image 6. Conclusion The proposed image compression method based on the ROI extracted based lossy compression method by using the quantization algorithm is discussed. The method is tested by the literally available satellite images. And the performance of the proposed system is analyzed by calculating the PSNR values of the compressed image. Our proposed system has obtained PSNR values of about 91.8% db. References [1] Perumal B & Rajasekaran MP. A hybrid discrete wavelet transform with neural network back propagation approach for efficient medical image compression. IEEE International Conference on Emerging Trends in Engineering, Technology and Science. 2017: 1-5. [2] Doukas C & Maglogiannis I. Region of interest coding techniques for medical image compression. IEEE Engineering in medicine and Biology Magazine. 2017; 26(5): 29-35. [3] Wu YG. Medical image compression by sampling DCT coefficients. IEEE Transactions on Information Technology in Biomedicine. 2002; 6(1): 86-94. [4] Sharma S & Bhat U. Image Compression using an efficient hybrid algorithm. 2013. [5] Reddy BV, Reddy PB, Kumar PS & Reddy AS. Lossless Compression of Medical Images for Better Diagnosis. IEEE 6th International Conference on Advanced Computing. 2016: 404-408. [6] Wu YG & Tai SC. Medical image compression by discrete cosine transforms spectral similarity strategy. IEEE Transactions on Information Technology in Biomedicine. 2001; 5(3): 236-243. [7] Bin L & Qinggang M. An improved SPIHT wavelet transform in the underwater acoustic image compression. IEEE International Conference on Measurement, Information and Control. 2013; 2: 1315-1318. [8] Panda SS and Jena G. Image Super Resolution Using Wavelet Transformation Based Genetic Algorithm. In Computational Intelligence in Data Mining. Springer India. 2016; 2: 355-361. [9] Panda SS, Jena G and Sahu SK. Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm. In Computational Intelligence in Data Mining. Springer India. 2015; 2: 675-681. [10] Asraf R, Akbar M & Jafri N. Statistical analysis of difference image for absolutely lossless compression of medical images. IEEE 28th Annual International Conference on Engineering in Medicine and Biology Society. 2006: 4767-4770.