L2 Optimized Predictive Image Coding with L∞ Bound

Chuah, Sceuchin

Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/12744

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DC Field	Value	Language
dc.contributor.advisor	Wu, Xiaolin	en_US
dc.contributor.author	Chuah, Sceuchin	en_US
dc.date.accessioned	2014-06-18T17:00:40Z	-
dc.date.available	2014-06-18T17:00:40Z	-
dc.date.created	2012-11-23	en_US
dc.date.issued	2013-04	en_US
dc.identifier.other	opendissertations/7603	en_US
dc.identifier.other	8665	en_US
dc.identifier.other	3486974	en_US
dc.identifier.uri	http://hdl.handle.net/11375/12744	-
dc.description.abstract	<p>In many scientific, medical and defense applications of image/video compression, an <em>l</em><sub>∞ </sub>error bound is required. However, pure <em>l</em><sub>∞</sub>-optimized image coding, colloquially known as near-lossless image coding, is prone to structured errors such as contours and speckles if the bit rate is not sufficiently high; moreover, previous <em>l</em><sub>∞</sub>-based image coding methods suffer from poor rate control. In contrast, the <em>l</em><sub>2</sub> error metric aims for average fidelity and hence preserves the subtlety of smooth waveforms better than the <em>l</em><sub>∞</sub> error metric and it offers fine granularity in rate control; but pure <em>l</em><sub>2</sub>-based image coding methods (e.g., JPEG 2000) cannot bound individual errors as <em>l</em><sub>∞</sub>-based methods can. This thesis presents a new compression approach to retain the benefits and circumvent the pitfalls of the two error metrics.</p>	en_US
dc.subject	L∞-constrained image compression	en_US
dc.subject	predictive coding	en_US
dc.subject	optimal scalar quantization	en_US
dc.subject	Signal Processing	en_US
dc.subject	Signal Processing	en_US
dc.title	L2 Optimized Predictive Image Coding with L∞ Bound	en_US
dc.type	thesis	en_US
dc.contributor.department	Electrical and Computer Engineering	en_US
dc.description.degree	Master of Applied Science (MASc)	en_US
Appears in Collections:	Open Access Dissertations and Theses

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