Chain code

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A chain code is a lossless compression algorithm for monochrome images. The basic principle of chain codes is to separately encode each connected component, or "blob", in the image.

For each such region, a point on the boundary is selected and its coordinates are transmitted. The encoder then moves along the boundary of the region and, at each step, transmits a symbol representing the direction of this movebment.

This continues until the encoder returns to the starting position, at which point the blob has been completely described, and encoding continues with the next blob in the image.

This encoding method is particularly effective for images consisting of a reasonably small number of large connected components.


Some popular chain codes include the Freeman Chain Code of Eight Directions[1] (FCCE), Vertex Chain Code[2] (VCC), Three OrThogonal symbol chain code[3] (3OT) and Directional Freeman Chain Code of Eight Directions[4] (DFCCE).

A related blob encoding method is crack code.[5] Algorithms exist to convert between chain code, crack code, and run-length encoding.

In use[edit]

Recently, the combination of Move-to-front transform and adaptive Run-length encoding accomplished efficient compression of the popular chain codes. [6]

See also[edit]


  1. ^ H. Freeman. On the encoding of arbitrary geometric configurations, IRE Transactions on Electronic Computers EC- 10(1961) 260-268.
  2. ^ E. Bribiesca, A new chain code, Pattern Recognition 32 (1999) 235–251.
  3. ^ H. Sánchez-Cruz, R. M. Rodríguez-Dagnino. Compressing bi-level images by means of a 3-bit chain code. Optical Engineering. SPIE. 44 (9) 097004 (2005) 1-8.
  4. ^ Y.K. Liu, B.Zalik, An efficient chain code with Huffman coding, Pattern Recognition 38 (4) (2005) 553-557.
  5. ^ A. Rosenfeld, A. C. Kak. Digital Picture Processing, 2nd edition (1982). Page 220. Academic Press, Inc. Orlando, FL, USA.
  6. ^ Žalik, Borut; Lukač Niko (2013). "Chain code lossless compression using move-to-front transform and adaptive run-length encoding". Signal Processing: Image Communication. doi:10.1016/j.image.2013.09.002.