The purpose of image compression is to reduce the number of bits required to represent data by removing data redundancy. Due to the large amount of image data, it is very difficult to store, transmit, and process, so the compression of image data becomes very important. This article introduced several lossless compression approaches such as Huffman, Fano, Run Length, Arithmetic, and LZW (Lempel–Ziv–Welch) Coding. The approach which can achieve compact code is regarded as closing to the best solution.

Shengzhong Zhang, Lei Yu, Yinqian Cheng. An Introduction of Image Lossless Compression Approches. Academic Journal of Mathematical Sciences (2023) Vol. 4, Issue 3: 1-4. https://doi.org/10.25236/AJMS.2023.040301.

[1] Robert M. Gray, et. al, Image Compressing and Tree-structured Vector Quantization, Image and Text Compression, 1992, Vol.176: 3-24

[2] Xiaoguang Jia, Lei Wang, Latest Development of Aerospace Remote Sensing Image Compression Technology, Chinese Journal of Graphics and Images, 1997, 2(10): 697-700

[3] Xianglin Li, Digital Image Processing, Graduate School of China University of Science and Technology, 1997.9

[4] Jinfei Wang, Kaizhong Zhang, et. al, Spectral and Spatial Decorrelation of Landsat-TM Data for Lossless Compression , IEEE Trans. on Geoscience and Remote Sensing, 1995, 33(5): 1277-1285

[5] Nazir D. Memon et. al, Lossless Compression of multispectral Image data, IEEE Trans. on Geoscience and Remote Sensing, 1994, 32(2): 282-289

[6] Mei Yuan, Wen Yuan, Data Compression Technology and Its Applications, Electronic Industry Press, 1995.12

[7] Wen Gao, Multimedia Data Compression Technology, Electronic Industry Press, 1994.4

[8] Fouzia I Khandwani, P E Ajmire, A Survey of Lossless Image Compression Techniques, International Journal of Electrical Electronics & Computer Science Engineering, 2018, 5(1): 39-42

[9] Yun Gan, Tao Ran, Research on Image Compression Coding Technology and Lossless Compression Strategies, Computer Knowledge and Technology, 2020, 16(22): 212-213