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Perceptual Image Coding with Discrete Cosine TransformValidation of Computational Model for JND

Perceptual Image Coding with Discrete Cosine Transform: Validation of Computational Model for JND [The limitations of HVS prevent it from sensing all changes in a reconstructed image after compression. By exploiting these limitations of the HVS, PICs are able to achieve higher compression with lesser visual degradation as compared to non-PICs. Since the performance of PIC is largely dependent on the estimation accuracy of visual degradation using computational model for JND, this chapter focuses on the validation of such computational models using a series of subjective experiments.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Perceptual Image Coding with Discrete Cosine TransformValidation of Computational Model for JND

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Publisher
Springer Singapore
Copyright
© The Author(s) 2015
ISBN
978-981-287-542-6
Pages
43 –61
DOI
10.1007/978-981-287-543-3_4
Publisher site
See Chapter on Publisher Site

Abstract

[The limitations of HVS prevent it from sensing all changes in a reconstructed image after compression. By exploiting these limitations of the HVS, PICs are able to achieve higher compression with lesser visual degradation as compared to non-PICs. Since the performance of PIC is largely dependent on the estimation accuracy of visual degradation using computational model for JND, this chapter focuses on the validation of such computational models using a series of subjective experiments.]

Published: May 14, 2015

Keywords: Subjective Experiment; Contrast Sensitivity; Subjective Score; Blocking Artifact; Visual Degradation

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