Wavelet based image coding and interpolation

The need of higher resolution on imaging systems and displays increases everyday.Data rates and bandwidth are still limited to satisfy the demands of enhanced resolutions.So, we should develop intelligent enhancement tools which yield higherresolution images with comparably limited bitrate. In this thesis, we examine thequality enhancement capabilities of two dierent approaches, i.e. image codingand image interpolation. Better image coding algorithms are capable of producingenhanced higher detail images at the same bitrate. For that purpose, wedesign an ecient and intelligent wavelet based image coding algorithm that codesthe hierarchical description of wavelet coecients instead of coding themselves.Namely, we introduce the hierarchical quantization index tree which is composedof quantization index classes. These index classes are constructed using combinationof similar wavelet coecients which leads an adaptive structure. Then,this hierarchical tree is optimized by a simple rate-distortion analysis to achieveecient bit allocation among various dierent regions of natural images. In thesecond part of the thesis, we propose a wavelet based interpolation algorithm thatexploits the correlation between high resolution(HR) and low resolution(LR) images.Basically, we design linear minimum mean square error lters between HRand LR images to recover lost high frequency information. For modeling the relationshipbetween two resolutions, we use two dierent approaches, i.e. blockbased lter design and context adaptive lter design. In block based lter designalgorithm, we partition the image into blocks to capture local frequency variations.In context adaptive algorithm, we use a simple context to adapt dierentimage structures. We also briey mention how these new approaches can beintegrated into a novel coding + resolution enhancement joint framework. Simulationsshow that both our coding and interpolation algorithms perform betterthan most existing schemes.