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Proceedings Paper

The role of spatially adaptive versus non-spatially adaptive distortion in supra-threshold compression
Author(s): Matthew D. Gaubatz; Stephanie Kwan; Sheila S. Hemami
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Paper Abstract

In wavelet-based image coding, a variety of masking properties have been exploited that result in spatially-adaptive quantization schemes. It has been shown that carefully selecting uniform quantization step-sizes across entire wavelet subbands or subband codeblocks results in considerable gains in efficiency with respect to visual quality. These gains have been achieved through analysis of wavelet distortion additivity in the presence of a background image; in effect, how wavelet distortions from different bands mask each other while being masked by the image itself at and above threshold. More recent studies have illustrated how the contrast and structural class of natural image data influences masking properties at threshold. Though these results have been extended in a number of methods to achieve supra-threshold compression schemes, the relationship between inter-band and intra-band masking at supra-threshold rates is not well understood. This work aims to quantify the importance of spatially-adaptive distortion as a function of compressed target rate. Two experiments are performed that require the subject to specify the optimal balance between spatially-adaptive and non-spatiallyadaptive distortion. Analyses of the resulting data indicate that on average, the balance between spatially-adaptive and non-spatially-adaptive distortion is equally important across all tested rates. Furthermore, though it is known that meansquared- error alone is not a good indicator of image quality, it can be used to predict the outcome of this experiment with reasonable accuracy. This result has convenient implications for image coding that are also discussed.

Paper Details

Date Published: 12 February 2007
PDF: 11 pages
Proc. SPIE 6492, Human Vision and Electronic Imaging XII, 64920E (12 February 2007); doi: 10.1117/12.707565
Show Author Affiliations
Matthew D. Gaubatz, Cornell Univ. (United States)
Stephanie Kwan, Cornell Univ. (United States)
Sheila S. Hemami, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 6492:
Human Vision and Electronic Imaging XII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Scott J. Daly, Editor(s)

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