Optimized Encoding Methods of the Overlapped Rectangular Non-symmetry and Anti-packing Model

  • Yunping ZhengEmail author
  • Ruijun Li
  • Mudar Sarem
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


The overlapped rectangular non-symmetry and anti-packing model (ORNAM) is a kind of image representation model developed from the original non-overlapped one. Aimed at the difference of them, this paper proposes two methods to improve the compression performance of the ORNAM coding (ORNAMC) for gray images. One is the adaptive arithmetic coding. The other is the adjacent position correlation encoding based on column bisections. In the experiment, we compare the improved algorithms with the original one and the classic quad tree coding algorithm. The results showed that our improvement increases the compression ratio under the premise of maintaining the quality of gray images.


Overlapped Rectangular Non-symmetry and Anti-packing Model (ORNAM) Image compression Adaptive arithmetic encoding Adjacent position correlation encoding Column bisections 



This work is supported by the National Natural Science Foundation of China under Grant No. 61300134, the Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20120172120036, the Natural Science Foundation of Guangdong Province of China under Grant No. S2011040005815, No. S2013010012515, No. 2015A030313206, and No. 2017A030313349, the Fundamental Research Funds for the Central Universities of China under Grant No. 2015ZM133, the Chinese National Scholarship Fund under Grant No. 201406155015, and the Undergraduate Innovative and Entrepreneurial Training Program of Guangdong Province of China under Grant No. S201910561235.


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Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouPeople’s Republic of China
  2. 2.School of Software EngineeringHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  3. 3.General Organization of Remote SensingDamascusSyria

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