Abstract
Binary-bit plane decomposition (BPD) is an effective method to reduce image complexity. Because of the diversity of sub-patterns of the non-symmetry and anti-packing model (NAM), combining NAM and BPD has resulted in a variety of image representation algorithms, such as a gray image representation based on square NAM and BPD (SNAMBPD), a gray image representation based on triangular NAM and BPD (TNAMBPD), and a color image representation based on rectangular NAM and BPD (RNAMBPDC). The study found that the overlapping rectangular NAM has more advantages in image representation than the square NAM, triangular NAM and non-overlapping rectangular NAM. It can represent larger homogeneous blocks, that is, the number of homogeneous blocks obtained by using the overlapping rectangular NAM is much less. Based on this, this paper proposes a gray image representation based on overlapping rectangular NAM and BPD (ORNAMBPD), and extends a color image representation based on overlapping rectangular NAM and BPD (ORNAMBPDC). The experimental results show that compared with LQT, SNAMBPD and TNAMBPD, our proposed ORNAMBPD has the least number of homogeneous blocks and the highest compression rate. Moreover, ORNAMBPDC also has fewer homogeneous blocks than LQT and RNAMBPDC algorithms, and the compression rate is also much higher.
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Acknowledgement
This work is supported by the Natural Science Foundation of Guangdong Province of China under Grant No. 2017A030313349 and No. 2021A1515011517, the National Natural Science Foundation of China under Grant No. 61300134, and the National Undergraduate Innovative and Entrepreneurial Training Program under Grant No. 202110561070 and No.202110561066.
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Zheng, Y., Xu, Y., Kuang, J., Sarem, M. (2022). Image Representation Based on Overlapping Rectangular NAM and Binary Bit-Plane Decomposition. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_49
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