The first part of this thesis is concerned with efficient adaptive image interpolation techniques for real-time applications. A new image interpolation algorithm is developed that combines optimal data fusion and context modeling of images. Specifically, two estimates of missing pixels obtained by cubic interpolation in perpendicular directions
are optimally fused under minimum mean square (MMSE) criterion. The fused result is further improved by a context-based error feedback mechanism to compensate for the error of cubic interpolation. The proposed image interpolation algorithm preserves edge structures well and achieves superior visual quality. This is accomplished at low computational complexity, making the new algorithm suitable for hardware implementation.
The main part of this thesis is devoted to a more sophisticated image interpolation
algorithm based on hidden Markov modeling (HMM). Most of existing interpolation
algorithms rely on point by point decisions to estimate the missing pixels. In contrast,
the HMM approach of image interpolation estimates a block of missing pixels via maximum a posterior (MAP) sequence estimation. The hidden Markov model can
incorporate the statistics of high resolution images into the interpolation process and
the MAP estimation technique can exploit high-order statistical dependency between
pixels. The proposed HMM-based image interpolation algorithm is implemented and its performance is evaluated and compared with existing methods. The comparison
study shows that the HMM-based image interpolation algorithm can reproduce
cleaner and sharper image details than its predecessors, while suppressing common
interpolation artifacts such as ringing, jaggies, and blurring.