Image Compression Study using Diversified Approaches

  • Jayshree R. Pansare Research Scholar at School of Computer Science and IT at Devi Ahilya Vishwavidyalaya, Indore (Madhya Pradesh), India Department of Computer Engineering M.E.S. College of Engineering, Pune, India
  • Ketki R Jadhav Department of Computer Engineering M.E.S. College of Engineering, Pune, India;
Keywords: Image compression, FELICS, Block coding, DCT-DKT HWT, LAR-LLC, DDCPM, APC, CALIC.


Image compression plays an important role in minimizing irrelevance and redundancy of digital images for efficient transmission and storage. It saves large storage capacity and transmission bandwidth. The aim of image compression algorithm is to reduce amount of data required to represent image with less degradation without loss. This paper studies diversified approaches for compression of images such as FELICS, Wavelet transformation based approach, Prediction based approach, Combination of method based approach and Block coding based approach etc. Under different approaches there are different techniques for compression of image and gives better results than previous state-of-art techniques. Different methods gives different compression ratio, saves memory, and gives high performance. They have their own strength and weaknesses.


(1) J. Zobel and A. Moffat, “Adding compression to a full-text retrieval system,” Softw.-Pract. Exper, vol. 25, no. 8, pp. 891-903, 1995.

(2) P. Franti, “A fast and efficient compression method for binary images,” Signal Process., Image Commun., vol. 6, no. 1, pp. 69-76, Mar. 1994.

(3) P. Franti and O. Nevalainen, “A two-stage modeling method for compressing binary images by arithmetic coding,” Comput. J., vol. 36, no. 7, pp. 615-622, 1993.

(4) X. Wu and N. Memon, “Context-based, adaptive, lossless image coding,” IEEE Trans. Commun., vol. 45, no. 4, pp. 437-444, Apr. 1997.

(5) A. Podlasov and P. Franti, “Lossless image compression via bit-plane separation and multilayer context tree modeling,”J. Electron. Imag, vol. 15, no. 4, pp. 043-009, Nov. 2006.

(6) P. Franti, E. Ageenko, P. Kopylov, S. Grhn, and F. Berger, “Compression of map images for real-time applications,” Image Vis. Comput., vol. 22, no. 13, pp. 1105-1115, 2004.

(7) A. Alarabeyyat, S. Al-Hashemi, T. Khdou1, M. Hjouj Btoush, S. Bani-Ahmad, R. Al-Hashemi, “Lossless Image Compression Technique Using Combination Methods,” Journal of Software Engineering and Applications, pp 752-763, 2012.

(8) A. Bookstein and S. T. Klein, “Is Huffman coding dead?”Computing, vol. 50, no. 4, pp. 279-296, 1993.

(9) A. El-Maleh, S. al Zahir, and E. Khan, “A geometric-primitives-based compression scheme for testing systems-on-a-chip,” in Proc. 19th IEEE Comput. Soc. Conf. VLSI Test Symp., pp. 54-59, Apr. 2001.

(10) Roman Starosolski, “New simple and efficient color space transformations for lossless image compression,” J. Vis. Commun. Image R. vol. 25, pp. 1056-1063, 2014.

(11) Manoj Kumar, Ankita Vaish, “Prediction error based compression of color images using WDR coding,” Int. J. Electron. Commun. (AE), vol. 70, pp. 1164-1171, 2016.

(12) Xiwen OwenZhao, Zhihai HenryHe, “Lossless Image Compression Using Super-Spatial Structure Prediction,” IEEE Trans. Signal Process, vol. 17, no. 4, pp. 383-386, April 2010.

(13) Zhiwei Xiong, Xiaoyan Sun, FengWu, “Block-Based Image Compression With Parameter-Assistant Inpainting,” IEEE Trans. Image Process, vol. 19, NO. 6, pp. 1651-1657, June 2010.

(14) Cheng-Chen Lin and Yin-Tsung Hwang, “An Efficient Lossless Compression Scheme for Hyperspectral Images Using Two-Stage Prediction,” IEEE Trans. Geoscience and remote

sensing, vol. 7, no. 3, pp. 558-562, July 2010.

(15) Hong-Sik Kim, Joohong Lee, Hyunjin Kim, Sungho Kang, and Woo Chan Park, “A Lossless Color Image Compression Architecture Using a Parallel Golomb-Rice Hardware CODEC,”IEEE Trans. Circuits Syst. For Video Technol., vol. 21, no. 11, pp. 1581-1587, November 2011.

(16) Andrew Martchenko and Guang Deng, “Bayesian Predictor Combination for Lossless Image Compression, ”IEEE Trans. Image Process., vol. 22, no. 12, pp. 5263-5269, December 2013.

(17) Seyun Kim and Nam Ik Cho, “Hierarchical Prediction and Context Adaptive Coding for Lossless Color Image Compression,” IEEE Trans. Image Process, vol. 23, no. 1, pp.

-448, January 2014.

(18) Yi Liu, Olivier Dforges, and Khouloud Samrouth ,“LAR-LLC: A Low-Complexity Multiresolution Lossless Image Codec,” IEEE Trans. Circuits Syst. for Video Technol., vol. 26, no. 8, pp. 1490-1501, august 2016.

(19) H. B. Kekrea, Prachi Natub and Tanuja Sarodec, “Color Image Compression using Vector Quantization and Hybrid Wavelet Transform,” Procedia Computer Science, vol. 89, pp. 778 784, 2016.

(20) Tsung-Han Tsai, Yu-Hsuan Lee, and Yu-Yu Lee, “Design and Analysis of High-Throughput Lossless Image Compression Engine Using VLSI Oriented FELICS Algorithm,” IEEE trans. on very large scale integr.(vlsi) syst., vol. 18, no. 1, pp. 39-52, January 2010.

How to Cite
Pansare, J. R., & Jadhav, K. R. (2017). Image Compression Study using Diversified Approaches. European Journal of Applied Sciences, 5(1), 13.