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SESSION 2 : IT융합기술 I, 좌장 : 최용수(성결대)

Proposal For Improving Effectiveness Of Quantization Matrices Using Evolution

초록

영어

Testing the effectiveness of quantization matrices in (image) compression is difficult, because each image is unique and requires individualization techniques to optimize the compression ratio. However, there is usually no time to perform these kinds of techniques each time an image is compressed, and thus more general (but less effective) quantization matrices are often used. Although these matrices are standardized, there is still room for improvement. This paper proposes the usage of a popular machine learning technique, genetic algorithms, to actually perform this improvement. Although there might be some generalization issues, we believe it can be partially overcome if the right training set is chosen.

목차

Abstract
 I. INTRODUCTION
 II. GENETIC ALGORITHMS
 III. EVOLUTIONARY COMPRESSION APPROACHES
 IV. CONCLUSION
 Acknowledgements
 REFERENCES

저자정보

  • Reinier Kop Graduate School of Information Science at Kore a University.
  • 김형중 Hyoung-Joong Kim. 교려대학교 교수

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