L2 Optimized Predictive Image Coding with L∞ Bound
| dc.contributor.advisor | Wu, Xiaolin | en_US |
| dc.contributor.author | Chuah, Sceuchin | en_US |
| dc.contributor.department | Electrical and Computer Engineering | 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.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.description.degree | Master of Applied Science (MASc) | 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.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 |
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