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Published on: February 12, 2014

Tree-structured method for LUT inverse halftoning and for image halftoning.

Murat Mese1, P P Vaidyanathan

  • 1Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA 91125, USA. mese@systems.caltech.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
Summary

A new tree-structured Look Up Table (TLUT) method reduces storage for inverse halftoning by avoiding nonexistent patterns. This TLUT approach offers improved halftone quality and efficient storage compared to traditional LUT methods.

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Area of Science:

  • Digital Image Processing
  • Computer Vision

Background:

  • Traditional Look Up Table (LUT) methods for inverse halftoning generate large tables.
  • Many LUT entries are unused due to nonexistent binary patterns in common halftones, leading to storage inefficiency.

Purpose of the Study:

  • To propose a novel tree-structured LUT (TLUT) method for inverse halftoning.
  • To reduce storage requirements for inverse halftoning by eliminating nonexistent patterns.
  • To evaluate the performance and quality of the TLUT method on various halftone images.

Main Methods:

  • Developed a tree structure to optimize LUT storage by excluding nonexistent binary patterns.
  • Introduced TLUT halftoning and compared its performance against standard LUT halftoning.
  • Trained the TLUT algorithm on halftones generated by Direct Binary Search (DBS) and error diffusion.

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Main Results:

  • The TLUT method significantly reduces storage requirements compared to the conventional LUT approach.
  • TLUT halftoning achieves better halftone quality and requires less storage than standard LUT halftoning.
  • Halftone quality of TLUT is adjustable based on the tree structure size, bridging the gap between error diffusion and DBS.

Conclusions:

  • The proposed TLUT method offers a more storage-efficient and effective solution for inverse halftoning.
  • TLUT halftoning provides a flexible approach to achieve desired halftone quality and characteristics.
  • The TLUT algorithm presents a complexity trade-off, being more complex than error diffusion but less complex than DBS.