Modification of a gray-level dynamic range based on a number of binary bit representation for image compression

(1) * Arief Bramanto Wicaksono Putra Mail (Politeknik Negeri Samarinda, Indonesia)
(2) Supriadi Supriadi Mail (Politeknik Negeri Samarinda, Indonesia)
(3) Aji Prasetya Wibawa Mail (State University of Malang, Indonesia)
(4) Andri Pranolo Mail (Universitas Ahmad Dahlan, Indonesia)
(5) Achmad Fanany Onnilita Gaffar Mail (Politeknik Negeri Samarinda, Indonesia)
*corresponding author

Abstract


The unique features of an image can be obtained by changing the gray level by modifying the dynamic range of the gray level. The gray-level dynamic range modification technique is one technique to minimize the selected features.  Bit rate reduction uses coding information with fewer bits than the original image (image compression). This study using the dynamic level of the gray level of a modified image with the concept of binary bit representation or also called bit manipulation.  Using some binary bit representation options used: 4, 5, 6, and 7 of bit can obtain the best compression performance. Measurement of compression ratio and decompression error ratio to a benchmark comparison called compression performance, which is the ultimate achievement of this study. The results of this study show the use of 6-bit binary representation has the best performance, and the resulting image compression does not resize the resolution of the original image only visually looks different.

Keywords


Gray level dynamic range; Image compression; Binary bit; Performance

   

DOI

https://doi.org/10.31763/sitech.v1i1.17
      

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References


A. B. W. Putra, R. Malani, and M. Mulyanto, “A Gray-Level Dynamic Range Modification Technique for Image Feature Extraction Using Fuzzy Membership Function,” Indones. J. Artif. Intell. Data Min., vol. 1, no. 1, pp. 6–14, Mar. 2018, doi: 10.24014/ijaidm.v1i1.4599.

J. H. Pujar and L. M. Kadlaskar, “A new lossless method of image compression and decompression using huffman coding techniques,” J. Theor. Appl. Inf. Technol., vol. 15, no. 1, pp. 18–23, 2010.

A. Kulkarni and A. Junnarkar, “Gray-Scale Image Compression Techniques: A Review,” Int. J. Comput. Appl., vol. 131, no. 13, pp. 22–25, Dec. 2015, doi: 10.5120/ijca2015907519.

A. Habib and D. Chowdhury, “An Efficient Compression Technique Using Arithmetic Coding,” J. Sci. Res. Reports, vol. 4, no. 1, pp. 60–67, Jan. 2015, doi: 10.9734/JSRR/2015/12846.

S. Alcaraz-Corona and R. M. Rodriguez-Dagnino, “Bi-Level Image Compression Estimating the Markov Order of Dependencies,” IEEE J. Sel. Top. Signal Process., vol. 4, no. 3, pp. 605–611, Jun. 2010, doi: 10.1109/JSTSP.2010.2048232.

M. G. Reyes, X. Zhao, D. L. Neuhoff, and T. N. Pappas, “Lossy Compression of Bilevel Images Based on Markov Random Fields,” in 2007 IEEE International Conference on Image Processing, 2007, pp. II-373-II–376, doi: 10.1109/ICIP.2007.4379170.

A. Masmoudi, W. Puech, and A. Masmoudi, “An improved lossless image compression based arithmetic coding using mixture of non-parametric distributions,” Multimed. Tools Appl., vol. 74, no. 23, pp. 10605–10619, 2015, doi: 10.1007/s11042-014-2195-8.

Shivaputra, S. H.S, and L. V, “An Efficient Lossless Medical Image Compression Technique for Telemedicine Applications,” Comput. Appl. An Int. J., vol. 2, no. 1, pp. 63–69, Feb. 2015, doi: 10.5121/caij.2015.2106.

V. G. Dubey and J. Singh, “Medical Image Compression and Decompression Using Huffman Encoding Technique,” IJAIR, pp. 335–338, 2012.

S. Kumari, S. Khanna, and Taqdir, “Comprehensive Study of the Work Done in Image Processing and Compression Techniques for Redundancy,” Int. Res. J. Eng. Technol., vol. 03, no. 01, pp. 855–860, 2016.

P. Kavitha, “A Survey on Lossless and Lossy Data Compression Methods,” Int. J. Comput. Sci. Eng. Technol., vol. 7, no. 03, pp. 110–114, 2016.

S. S. Pandey, M. P. Singh, and V. Pandey, “Image Transformation and Compression using Fourier Transformation,” Int. J. Curr. Eng. Technol., vol. 5, no. 2, pp. 1178–1182, 2015.

X. Zhou, Y. Bai, and C. Wang, “Image Compression Based on Discrete Cosine Transform and Multistage Vector Quantization,” Int. J. Multimed. Ubiquitous Eng., vol. 10, no. 6, pp. 347–356, Jun. 2015, doi: 10.14257/ijmue.2015.10.6.33.

Z. Abidin and A. Alamsyah, “Wavelet based approach for facial expression recognition,” Int. J. Adv. Intell. Informatics, vol. 1, no. 1, pp. 7–14, Mar. 2015, doi: 10.26555/ijain.v1i1.7.

S. Gomathi and T. Santhanam, “Performance Analysis of Haar Wavelet Transform and Huffman Coding Compression Techniques for Human Object,” Int. J. Comput. Intell. Informatics, vol. 6, no. 3, pp. 233–239, 2016.

L. E. George and G. Al-Khafaji, “Image compression based on non-linear polynomial prediction model,” Int. J. Comput. Sci. Mob. Comput., vol. 4, no. 8, pp. 91–97, 2015.

M. Ammar and Y. Saleem, “Implementation of CCSDS Image Data Compression Standard on DSP Platform,” J. Sp. Technol., vol. V, no. 1, pp. 91–102, 2015.

M. K. Islam, M. Moznuzzaman, M. F. Khatun, and R. Yesmin, “A Proposed Modification of Baseline JPEG Standard Image Compression Technique,” Int. J. Sci. Eng. Res., vol. 6, no. 8, pp. 180–186, 2015.

C. Zhang, C. Wang, and B. Jiang, “Video Compression Algorithm Based on Directional All Phase Biorthogonal Transform and H.263,” Int. J. Signal Process. Image Process. Pattern Recognit., vol. 9, no. 3, pp. 189–198, Mar. 2016, doi: 10.14257/ijsip.2016.9.3.17.

A. B. W. Putra, A. F. O. Gaffar, A. Wajiansyah, and I. H. Qasim, “Feature-Based Video Frame Compression Using Adaptive Fuzzy Inference System,” in 2018 International Symposium on Advanced Intelligent Informatics (SAIN), Aug. 2018, pp. 49–55, doi: 10.1109/SAIN.2018.8673386.


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Copyright (c) 2020 Arief Bramanto Wicaksono Putra, Supriadi Supriadi, Aji Prasetya Wibawa, Andri Pranolo, Achmad Fanany Onnilita Gaffar

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Science in Information Technology Letters
ISSN 2722-4139
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