Adaptive Pixel-Based Technique for Grayscale Image Compression

Main Article Content

Zahraa H. Abed
Ghadah K. AL-Khafaji


Grayscale images are extensively used due to their simplicity and cheapness in storage and transmission compared to color one of RGB base. It can also be considered a solution for color-blind people to ensure a better view of things and reading. However, unfortunately, it is still quite overburdened with redundancy(s) in which the data compression exploits them efficiently depending on the type and way of redundancy removal. This work introduces a hybrid compression system to compress grayscale images using the adaptive pixel-based technique (PBT) of optimized modeling base, with incorporated Minimize Matrix Size Algorithm (MMSA) of three digits values (C321) to encode the residual compactly along the need to overhead information (index, mean). Adapting traditional PBT led to overcoming the problems in the conventional PBT system in terms of large size. It achieved an acceptable reduction in bytes for the deterministic part (M, Indx) of over 400 bytes and the deterministic part (Res), where the size was reduced to more than 5000 bytes on average. The size has been reduced by nearly 50% compared to traditional PBT. The tested results indicate higher quality compared to the standard JPEG and traditional PBT in terms of performance. This includes a Compression Ratio (CR) of 13 and a PSNR of 48 dB. 

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How to Cite
“Adaptive Pixel-Based Technique for Grayscale Image Compression” (2024) Journal of Engineering, 30(05), pp. 52–69. doi:10.31026/j.eng.2024.05.04.

How to Cite

“Adaptive Pixel-Based Technique for Grayscale Image Compression” (2024) Journal of Engineering, 30(05), pp. 52–69. doi:10.31026/j.eng.2024.05.04.

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Abed, Z.H., and AL-Khafaji, G.K., 2022. Pixel based techniques for gray image compression: A review. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(2), pp. 59–70. Doi:10.29304/jqcm.2022.14.2.967.

Abood, Z.I., 2017. Composite techniques based color image compression. Journal of Engineering, 23(3), pp. 80-93. Doi:10.31026/j.eng.2017.03.06.

Abood, Z.I., 2013. Image compression using 3-D two-level techniques. Journal of Engineering, 19(11), pp. 1407-1424. Doi:0.31026/j.eng.2013.11.05.

Ahmed, R.H., and Hamza, E.K., 2021. Designing a Secure Software-Defined Radio Transceiver using the Logistic Map. Journal of Engineering, 27(6), pp. 59–72. Doi:10.31026/j.eng.2021.06.05.

Ahmed, Z J., George, L.E., and Abduljabbar, Z.S., 2020. Fractal image compression using block indexing technique: A review. Iraqi Journal of Science, 61(7), pp.1798-1810. Doi:10.24996/ijs.2021.62.

Ahmed, Z.J., George, L.E., and Hadi, R.A., 2021. Audio compression using transforms and high order entropy encoding. International Journal of Electrical and Computer Engineering, 11(4), pp. 3459–3469.

Al-hadithy, S.S., Al-khafaji, G.K., and Siddeq, M.M., 2021. Adaptive 1-D Polynomial Coding of C621 Base for Image Compression, Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), pp. 5720-5731.

AL-Hadithy, S.S., and AL-Khafaji, G.K., 2022. Adaptive 1-D polynomial coding to compress color image with C421. International Journal of Nonlinear Analysis and Applications. pp. 1261–1276.

Al-Khafaji, G., and Fadhil, S., 2017. Image Compression based on Fixed Predictor Multiresolution Thresholding of Linear Polynomial Near lossless Techniques. Journal of Al-Qadisiyah for computer science and mathematics, 9(2), pp.Page-35. Doi:10.29304/jqcm.2017.9.2.bit

Al-Khafaji, G.K., 2018. Linear Polynomial Coding with Midtread Adaptive Quantizer. Iraqi Journal of Science, 59(1c), pp.585-590. Doi: 10.24996/IJS.2018.59.1C.15.

Al-Khafaji, G.K., and Gorrge, L.E., 2021. Grey-level image compression using 1-d polynomial and hybrid encoding techniques. Journal of Engineering Science and Technology, 16(6), pp. 4707–4728.

AL-Khafaji, G.K., Rasheed, M.H., Siddeq, M.M., and Rodrigues, M.A., 2023. Adaptive polynomial coding of multi-base hybrid compression. International Journal of Engineering, 36(2), pp. 236–252. Doi:10.5829/ije.2023.36.02b.05.

Abd-Alzhra, A.S., and Al-Tamimi, M.S., 2021. Lossy image compression using hybrid deep learning autoencoder based on k-mean clustering. International Journal of Intelligent Engineering & Systems, pp. 7848-7861.

Abd-Alzhra, A. S., and Al- Tamimi, M. S. H., 2022. Image compression using deep learning: methods and techniques. Iraqi Journal of Science, 63(3), pp. 1299–1312. Doi:10.24996/ijs.2022.63.3.34.

Abu-Faraj, M.A., Al-Hyari, A., Obimbo, C., Aldebei, K., Altaharwa, I., Alqadi, Z., and Almanaseer, O., 2023. Protecting digital images using keys enhanced by 2D chaotic logistic maps. Cryptography, 7(2), pp.1-20. Doi:10.3390/cryptography7020020.

Arif, J., Khan, M. A., Ghaleb, B., Ahmad, J., Munir, A., Rashid, U., and Al-Dubai, A. Y., 2022. A novel chaotic permutation-substitution image encryption scheme based on logistic map and random substitution. IEEE Access, 10, pp. 12966-12982. Doi:10.1109/access.2022.3146792.

Azman, N.A.N., Ali, S., Rashid, R.A., Saparudin, F.A., and Sarijari, M.A., 2019. A hybrid predictive technique for lossless image compression. Bulletin of Electrical Engineering and Informatics, 8(4), pp.1289-1296. Doi: 10.11591/eei.v8i4.1612.

Chuman, T., Iida, K. and Kiya, H., 2017, December. Image manipulation on social media for encryption-then-compression systems. 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 858-863). IEEE. Doi:10.1109/APSIPA.2017.8282153 .

George, LE., Hassan, E.K., Mohammed, S.G., and Mohammed, F.G., 2020. Selective image encryption based on DCT, hybrid shift coding and randomly generated secret key. Iraqi Journal of Science, pp.920-935. Doi: 10.24996/ijs.2020.61.4.25.

Hagiwara, K., 2022. Bridging between soft and hard thresholding by scaling. IEICE TRANSACTIONS on Information and Systems, 105(9), pp.1529-1536. Doi: 10.1587/transinf.2021EDP7223.

Hussain, A.A., and AL-Khafaji, G.K., 2021. A pixel based method for image compression. Tikrit Journal of Pure Science, 26(1), pp. 1813-1662. Doi:10.25130/tjps.v26i1.108.

Hussain, A.A., Al-Khafaji, G.K., and Siddeq, M.M., 2020. Developed JPEG Algorithm Applied in Image Compression, IOP Conference Series: Materials Science and Engineering, 928(3).

Hussain, A.J., Al-Fayadh, A., and Radi, N., 2018. Image compression techniques: A survey in lossless and lossy algorithms. Neurocomputing, 300, pp. 44–69. Doi: 10.1016/j.neucom.2018.02.094.

Ibrahim, A.A., George, L.E., and Hassan, E.K., 2020. Color image compression system by using block categorization based on spatial details and DCT followed by improved entropy encoder. Iraqi Journal of Science, 61(11), pp. 3127–3140. Doi:10.24996/ijs.2020.61.11.32.

Liu, X., An, P., Chen, Y., and Huang, X., 2022. An improved lossless image compression algorithm based on Huffman coding. Multimedia Tools and Applications, 81(4), pp. 4781–4795. Doi:10.1007/s11042-021-11017-5.

Kabir, M.A., and Mondal, M.R.H., 2018. Edge-based and prediction-based transformations for lossless image compression. Journal of Imaging, 4(5), p.64. Doi: 10.3390/jimaging4050064.

Liu, H., and Foygel Barber, R., 2020. Between hard and soft thresholding: optimal iterative thresholding algorithms. Information and Inference: A Journal of the IMA, 9(4), pp. 899-933. Doi:10.1093/imaiai/iaz027.

Maghari, A., 2019. A comparative study of DCT and DWT image compression techniques combined with Huffman coding. Jordanian Journal of Computers and Information Technology, 5(2). Doi:10.5455/jjcit.71-1554982934

Mohammed, S.G., Abdul-Jabbar, S.S., and Mohammed, F.G., 2021, December. Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm. Journal of Physics: Conference Series, 2114(1), p. 012080. Doi: 10.1088/1742-6596/2114/1/012080.

Gashnikov, M., and Maksimov, A., 2018, August. Parameterized four direction contour-invariant extrapolator for DPCM image compression. In Tenth International Conference on Digital Image Processing (ICDIP 2018),10806, pp. 1229-1238. Doi:10.1117/12.2503003.

Mahdi, N.S., and Al-Khafaji, G.K., 2022. Image compression using polynomial coding techniques: A review. Journal of Al-Qadisiyah for computer science and mathematics, 14(2), pp. 70-81. Doi:10.29304/jqcm.2022.14.2.968

Nandeesha, R., and Somashekar, K., 2023. Content-Based Image Compression Using Hybrid Discrete Wavelet Transform with Block Vector Quantization. International Journal of Intelligent Systems and Applications in Engineering, 11(5s), pp.19-37.

Narayana, P.S., and Khan, A.M., 2020. MRI image compression using multiple wavelets at different levels of discrete wavelets transform. Journal of Physics: Conference Series,1427(1), P. 012002. Doi:10.1088/1742-6596/1427/1/01200.2.

Rafea, S., and Salman, N.H., 2018. Hybrid DWT-DCT compression algorithm & a new flipping block with an adaptive RLE method for high medical image compression ratio. International Journal of Engineering & Technology, 7(4), pp. 4602-4606. Doi: 10.14419/ijet.v7i4.25904.

Rasheed, M.H., Salih, O.M., Siddeq, M.M., and Rodrigues, M.A., 2020. Image compression based on 2D discrete Fourier transform and matrix minimization algorithm. Array, 6, P. 100024. Doi:10.1016/j.array.2020.100024.

Sadkhan, S.B., 2020, September. A proposed image compression technique based on DWT and predictive techniques. In 2020 3rd International Conference on Engineering Technology and its Applications (IICETA), pp. 246-251. IEEE. Doi:10.1109/IICETA50496.2020.9318802

Salih, A.M., and Mahmood, S.H., 2019. Digital color image watermarking using encoded frequent mark. Journal of Engineering, 25(3), pp.81-88. Doi:10.31026/j.eng.2019.03.07.

Salman, N.H., and Rafea, S., 2020. The arithmetic coding and hybrid discrete wavelet and cosine transform approaches in image compression. Journal of Southwest Jiaotong University, 55(1), pp. 1–9. Doi: 10.35741/issn.0258-2724.55.1.6.

Salman, N.H., 2021. Compare arithmetic coding to the wavelet approaches for medical image compression. Journal of Engineering Science and Technology, 16(1), pp. 737-749.

Salman, N., 2017. New image compression/decompression technique using arithmetic coding algorithm. Journal of Zankoy Sulaimani, 19(1), pp. 263-272. Doi:10.17656/18124100.

Shihab, H.S., 2023. Image compression techniques on-Board small satellites. Iraqi Journal of Science, 64(3), pp. 1518-1534. Doi:10.24996/ijs.2023.64.3.40.

Siddeq, M.M., and Al-Khafaji, G.K., 2013. Applied minimized matrix size algorithm on the transformed images by DCT and DWT used for image compression. International Journal of Computer Applications, 70(15). pp. 33-40. Doi:10.5120/12040-8000.

Siddeq, M.M., and Rodrigues, M.A., 2015. A Novel 2D Image Compression Algorithm Based on Two Levels DWT and DCT Transforms with Enhanced Minimize-Matrix-Size Algorithm for High Resolution Structured Light 3D Surface Reconstruction .3D Research. 6(3), pp. 1–35. Doi:10.1007/s13319-015-0055-6.

Siddeq, M.M., and Rodrigues, M.A., 2017. A novel high-frequency encoding algorithm for image compression. EURASIP Journal on Advances in Signal Processing, 2017, pp. 1-17. Doi:10.1186/s13634-017-0461-4.

Sultan, B.A., and George, L.E., 2021. Color image compression based on spatial and magnitude signal decomposition. International Journal of Electrical and Computer Engineering (IJECE), 11(5), pp. 4069-4081. Doi:10.11591/ijece.v11i5.pp4069-4081.

Toama, R.J., and Hussein, M.N., 2020. A secure cipher for the gray images based on the shamir secret sharing scheme with discrete wavelet haar transform. Journal Of Mechanics Of Continua And Mathematical Sciences, 15(6), pp. 334-351. Doi:10.26782/jmcms.2020.06.00025.

Yousif, R.I., and Salman, N.H., 2021. Image compression based on arithmetic coding algorithm. Iraqi Journal of Science, 62(1), pp. 329–334. Doi:10.24996/ijs.2021.62.1.31.

Zhang, Y., Ding, W., Pan, Z., and Qin, J., 2019. Improved wavelet threshold for image de-noising. Frontiers in neuroscience, 13, P. 39. Doi: 10.3389/fnins.2019.00039.

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