The Application of IaCLA Model to Adaptive Image Quantization

Yuan Jiang; Lu Shen Wu; Tao Huang

doi:10.4028/www.scientific.net/AMR.532-533.782

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533The Application of IaCLA Model to Adaptive Image...

The Application of IaCLA Model to Adaptive Image Quantization

Abstract:

The quantization scheme proposed in this paper uses a nonlinear transform to exploit the visual masking such as brightness masking and contrast masking, etc. It’s based on the IaCLA model derived from the intra-band masking. The application of the proposed strategy to JPEG2000 coding framework is presented, and the experimental results show the proposed approach can efficiently protect low amplitude wavelet coefficient and discriminate the simple edge even in the smooth region.

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Advanced Materials Research (Volumes 532-533)

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782-786

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https://doi.org/10.4028/www.scientific.net/AMR.532-533.782

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Online since:

June 2012

Authors:

Yuan Jiang, Lu Shen Wu, Tao Huang

Keywords:

Adaptive Quantization, EBCOT Algorithm, IaCLA model, JPEG 2000

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References

[1] A Mallat, A theory of multiresolution signal decomposition, the wavelet representation, IEEE Transact ion s on Pat tern Analysis and Machine Intelligence. 1989, 11 (7) : pp.674-695.

DOI: 10.1109/34.192463

Google Scholar

[2] JM Shapiro, Embedded image coding using zerotrees of wavelet coefficients, IEEE Transactions on Signal Processing, 1993, 41( 12), p: 3445 - 3462.

DOI: 10.1109/78.258085

Google Scholar

[3] A Said. Pearlman WA. A new, fast and efficient image codec based on set partitioning in hierarchical trees, IEEE Transact ions on Circuits and System s for Video Techno logy, 1996, 6( 3) , p: 243 - 250.

DOI: 10.1109/76.499834

Google Scholar

[4] A Skodras, C Christopoulos and T Ebrahim. The JPEG 2000 still image compression standard. Signal Processing Magazine, IEEE, 2001, 18 ( 5) , p: 36 - 58.

DOI: 10.1109/79.952804

Google Scholar

[5] Marcus N, Reichell J. Compression of Color Images with Wavelets under Consideration of the HVS. (SPIE, 2000, 3644: 129-140).

Google Scholar

[6] Hontsch I, Karam L J. Locally Adaptive Perceptual Image Coding, IEEE Trans on IP, 2000, 9(9), p: 1472-1483.

DOI: 10.1109/83.862622

Google Scholar

[7] Zeng W, Daly S, Lei S. Point-extended Visual Masking for JPEG-2000 Image Compression, IEEE ICIP, 2000, p: 657-660.

DOI: 10.1109/icip.2000.901044

Google Scholar