<p>In this thesis we systematically reexamine the classical problem of image interpolation with an aim to better preserve the structural information, such as edges and textures, in the interpolated image. We take on the technical challenge of faithfully reconstructing high frequency components because this is critical to the perceptual quality. To achieve the above goal we develop three new adaptive image interpolation methods: 1) a classification-based method that is driven by contextual information of the low resolution image and the prior knowledge extracted from a training set of high resolution images; 2) An adaptive soft-decision block estimation method that learns and adapts to varying scene structures, guided by a two-dimension piecewise autoregressive model; 3) A model-based non-linear image restoration scheme in which the model parameters and high resolution pixels are jointly estimated through non-linear least squares estimation.</p> <p>The latter part of this thesis is devoted to the research of interpolation-based image compression, which is a relatively new topic. Our research is motivated by two important applications of visual communication: low bit-rate image coding and multiple description coding. We succeed in developing standard-compliant interpolation-based compression techniques for the above two applications. In their respective categories, these techniques exceed the best rate-distortion performance reported so far in the literature.</p> / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/17408 |
| Date | 01 1900 |
| Creators | Zhang, Xiangjun |
| Contributors | Wu, Xiaolin, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
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