<p>In this thesis, we propose a new encoder-friendly image compression strategy for high-throughput cameras and other scenarios of resource-constrained encoders. The encoder performs L<sub>∞</sub>-constrained predictive coding (DPCM coupled with uniform scalar quantizer), while the decoder solves an inverse problem of L<sub>2</sub> restoration of L<sub>∞</sub>-coded images. Although designed for minimum encoder complexity (lower than distributed source coding and compressive sensing), the new codec outperforms state-of-the-art encoder-centric image codecs such as JPEG 2000 in PSNR for bit rates higher than 1.2 bpp, while maintaining much tighter L<sub>∞</sub> error bounds as well. This is achieved by exploiting the tight error bound on each pixel provided by the L<sub>∞</sub>-constrained encoder and by locally adaptive image modeling.</p> / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/9428 |
| Date | 11 1900 |
| Creators | Wang, Heng |
| Contributors | Wu, Xiaolin, Dumitrescu, Sorina, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
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